system

Abstract

The system according to this embodiment aims to streamline the management of the data infrastructure, visualize data, standardize column names, suggest the optimal storage location for tables, and create and optimize queries. [Solution] The system according to the embodiment comprises a visualization unit, a unification unit, a proposal unit, a query creation unit, and an optimization unit. The visualization unit visualizes the data. The unification unit unifies the naming conventions for column names based on the data visualized by the visualization unit. The proposal unit proposes the optimal table storage location based on the column names unified by the unification unit. The query creation unit creates a query based on the storage location proposed by the proposal unit. The optimization unit analyzes the query created by the query creation unit and proposes optimizations.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in the management of the data base, visualization of data, unification of column names, proposal of the optimal storage location of tables, creation and optimization of queries have not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of data base management and perform visualization of data, unification of column names, proposal of the optimal storage location of tables, creation and optimization of queries.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a visualization unit, a unification unit, a proposal unit, a query creation unit, and an optimization unit. The visualization unit visualizes the data. The unification unit unifies the naming conventions for column names based on the data visualized by the visualization unit. The proposal unit proposes the optimal table storage location based on the column names unified by the unification unit. The query creation unit creates a query based on the storage location proposed by the proposal unit. The optimization unit analyzes the query created by the query creation unit and proposes optimizations. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the management of the data infrastructure, visualize data, standardize column names, suggest the optimal storage location for tables, and create and optimize queries. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The data concierge system according to an embodiment of the present invention is an AI service aimed at improving the efficiency and security of data infrastructure management. This data concierge system visualizes data structures, making it easier for users to understand the overall picture of the data. It standardizes column naming conventions to improve the efficiency of data utilization. Furthermore, the AI ​​suggests the optimal table storage location, accelerating data retrieval and utilization. It includes a query creation assistance function to support users in creating queries effectively and efficiently, and also analyzes executed queries to suggest optimizations. In addition, it investigates the impact of specification changes and identifies necessary actions. Regarding data security, the AI ​​classifies confidential and public data and constantly monitors whether the scope of disclosure is appropriate. Furthermore, by detecting abnormal access patterns, it enables rapid countermeasures. It enhances data security and auditing functions by tracking user behavior, monitoring policy violations, and auditing data changes. These functions provide rapid feedback through real-time alerts. For example, the data concierge system visualizes data structures, making it easier for users to understand the overall picture of the data. It standardizes column naming conventions to improve the efficiency of data utilization. Furthermore, the AI ​​suggests the optimal storage location for tables, accelerating data retrieval and utilization. It includes query creation assistance features to support users in creating queries effectively and efficiently, and also analyzes executed queries to suggest optimizations. It also investigates the impact of specification changes and identifies necessary actions. In terms of data security, the AI ​​classifies sensitive and public data and constantly monitors whether the scope of disclosure is appropriate. Furthermore, it can detect abnormal access patterns, enabling rapid countermeasures. Data security and auditing capabilities are enhanced by tracking user behavior, monitoring policy violations, and auditing data changes. These functions provide rapid feedback through real-time alerts. As a result, the data concierge system can significantly improve the efficiency and security of data management.

[0029] The data concierge system according to this embodiment comprises a visualization unit, a unification unit, a proposal unit, a query creation unit, and an optimization unit. The visualization unit visualizes the data. The visualization unit displays the data structure in the form of graphs, charts, heatmaps, etc. The visualization unit makes it possible to visually grasp the overall picture of the data. For example, the visualization unit can display the hierarchical structure of the data in a tree format. The visualization unit can also display the relationships between data in a network diagram. Furthermore, the visualization unit can display the distribution of data in a histogram or scatter plot. The unification unit unifies the naming conventions for column names based on the data visualized by the visualization unit. The unification unit provides, for example, guidelines for unifying the naming conventions for column names. The unification unit can also automatically apply the naming conventions for column names. For example, the unification unit unifies the naming conventions by adding prefixes or suffixes to the column names. The unification unit also has a function to ensure that the changes to the naming conventions for column names do not affect the existing database. The Proposal Unit suggests the optimal table storage location based on the column names standardized by the Unification Unit. For example, the Proposal Unit suggests the optimal storage location based on data access frequency and importance. The Proposal Unit can also automatically select data storage locations. For example, it analyzes data access patterns and suggests the optimal storage location. Furthermore, the Proposal Unit includes features to maintain data integrity when changing data storage locations. The Query Creation Unit creates queries based on the storage locations suggested by the Proposal Unit. The Query Creation Unit provides, for example, auxiliary functions for users creating queries. The Query Creation Unit includes an auto-completion function for queries. For example, it assists in query creation by completing parts of the query entered by the user. The Query Creation Unit also includes a query syntax checking function. For example, it checks the syntax of the query entered by the user and points out errors. The Optimization Unit analyzes the queries created by the Query Creation Unit and suggests optimizations. For example, the Optimization Unit suggests optimization methods to improve query execution speed. The Optimization Unit can also automatically generate query execution plans.For example, the optimization unit analyzes the query execution plan and proposes the optimal execution plan. The optimization unit also analyzes the query execution results and provides feedback for query optimization. This allows the data concierge system to efficiently perform data visualization, standardization of naming conventions, suggestion of optimal storage locations, query creation, and query optimization.

[0030] The visualization unit visualizes data. For example, it displays data structures in formats such as graphs, charts, and heatmaps. Specifically, it selects an appropriate visualization method depending on the type and characteristics of the data. For example, in the case of time-series data, line graphs and bar charts can be used to visually represent data fluctuations. For categorical data, pie charts and bar charts can be used to show data distribution. Heatmaps represent data density and intensity using color intensity, making them suitable for intuitively understanding specific patterns and outliers. Furthermore, the visualization unit can display the hierarchical structure of data in a tree format. The tree format visually shows parent-child relationships and hierarchical structures, making it easier to understand the data structure. It can also display data relationships using network diagrams. Network diagrams use nodes and edges to show relationships between data, helping to visually grasp complex relationships. Additionally, histograms and scatter plots can be used to display data distribution and correlations. Histograms show data distribution as bar graphs, making them suitable for visually understanding data bias and variance. Scatter plots show the correlation between two variables and help visually grasp data trends and patterns. This allows the visualization section to provide a visual overview of the data, supporting data analysis and understanding.

[0031] The unification unit unifies column naming conventions based on data visualized by the visualization unit. For example, the unification unit provides guidelines for unifying column naming conventions. Specifically, these guidelines include adding prefixes and suffixes, using camel case or snake case, and standardizing abbreviations. Based on these guidelines, the unification unit can automatically apply the column naming conventions. For instance, adding prefixes and suffixes to column names clarifies the column's role and data type. Using camel case or snake case improves the readability of column names. Furthermore, standardizing abbreviations maintains consistency in column names. The unification unit includes features to prevent impact on existing databases when changing column naming conventions. Specifically, it automatically modifies the database schema to maintain data integrity. It also manages the column name change history and allows reverting to the original column name if necessary. This enables the unification unit to standardize data naming conventions and streamline data management and utilization.

[0032] The proposal unit suggests the optimal storage location for tables based on column names standardized by the unification unit. For example, the proposal unit suggests the optimal storage location based on data access frequency and importance. Specifically, it analyzes data access patterns and stores frequently accessed data in high-speed storage and less frequently accessed data in cost-effective storage. Furthermore, depending on the importance of the data, critical data is stored in highly redundant storage to ensure data security. The proposal unit can also automatically select data storage locations. For example, it monitors data access patterns in real time and dynamically changes the optimal storage location. In addition, the proposal unit has functions to maintain data integrity when changing data storage locations. Specifically, it performs data integrity checks when data is moved and manages the data movement history. This allows the proposal unit to optimize data storage locations and improve data access performance and cost efficiency.

[0033] The query creation unit creates queries based on the storage locations suggested by the suggestion unit. The query creation unit provides, for example, auxiliary functions for users when creating queries. Specifically, it has an auto-completion function, assisting query creation by completing parts of the query entered by the user. For example, if the user enters part of a table name or column name, the query creation unit presents suggestions, and the user completes the query by selecting from them. The query creation unit also has a query syntax checking function. Specifically, it checks the syntax of the query entered by the user in real time and points out errors. This allows users to create accurate queries. Furthermore, the query creation unit has a query optimization suggestion function, analyzing the execution plan of the query created by the user and suggesting the optimal query. This can improve query execution speed. The query creation unit supports users in creating queries, enabling efficient and accurate query creation.

[0034] The optimization unit analyzes queries created by the query generation unit and proposes optimizations. For example, the optimization unit proposes optimization techniques to improve query execution speed. Specifically, it automatically generates a query execution plan and proposes the optimal execution plan. For example, it analyzes the query execution plan and proposes optimization techniques to improve query execution speed, such as adding indexes or changing join orders. The optimization unit also analyzes the query execution results and provides feedback for query optimization. Specifically, it evaluates the query performance based on the query execution results and points out areas for improvement. Furthermore, the optimization unit can learn query optimization techniques based on past query execution history and use this to improve future query optimization. As a result, the optimization unit can improve query execution speed and optimize database performance.

[0035] The visualization unit can visualize the data structure. For example, the visualization unit can display the data structure in a tree format. The visualization unit makes it possible to visually grasp the hierarchical structure of the data. For example, the visualization unit can also display the relationships between data in a network diagram. The visualization unit can also display the distribution of data in a histogram or scatter plot. This makes it easier for users to understand the overall picture of the data by visualizing the data structure. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the visualization unit can input the data structure into a generative AI, and the generative AI can generate graphs or charts to visualize the data structure.

[0036] The unification unit can standardize column naming conventions. For example, the unification unit provides guidelines for standardizing column naming conventions. The unification unit can also automatically apply column naming conventions. For example, the unification unit standardizes naming conventions by adding prefixes or suffixes to column names. Furthermore, the unification unit has a function to ensure that changes to column naming conventions do not affect existing databases. This improves the efficiency of data utilization through the standardization of column naming conventions. Some or all of the above processing in the unification unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the unification unit can input column naming conventions into a generation AI, and the generation AI can propose the optimal naming convention.

[0037] The suggestion unit can propose the optimal storage location for tables. For example, it can propose the optimal storage location based on the frequency and importance of data access. The suggestion unit can also automatically select the data storage location. For example, it can analyze data access patterns and propose the optimal storage location. Furthermore, the suggestion unit has a function to maintain data integrity when changing data storage locations. This speeds up data retrieval and utilization by suggesting the optimal table storage location. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input data access patterns into a generative AI, which can then propose the optimal storage location.

[0038] The query creation unit can assist in query creation. For example, the query creation unit provides assistance functions when a user creates a query. The query creation unit has an auto-completion function for queries. For example, the query creation unit assists in query creation by completing parts of the query entered by the user. The query creation unit also has a query syntax checking function. For example, the query creation unit checks the syntax of the query entered by the user and points out errors. This allows the user to create queries effectively and efficiently through query creation assistance. Some or all of the above processing in the query creation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the query creation unit can input a part of the query entered by the user into the generation AI, and the generation AI can complete the query.

[0039] The optimization unit can analyze the execution query and propose optimizations. For example, the optimization unit proposes optimization methods to improve the execution speed of the query. The optimization unit can also automatically generate the execution plan of the query. For example, the optimization unit analyzes the execution plan of the query and proposes the optimal execution plan. The optimization unit also analyzes the execution results of the query and provides feedback for query optimization. As a result, the execution efficiency of the query is improved through the analysis of the execution query and the proposal of optimizations. Some or all of the above processing in the optimization unit may be performed using, for example, a generation AI, or without a generation AI. For example, the optimization unit can input the execution query to a generation AI, and the generation AI can perform query optimization.

[0040] The proposal department can investigate the impact of specification changes and identify necessary countermeasures. For example, the proposal department can identify the scope of impact of specification changes and propose necessary countermeasures. The proposal department can also automatically analyze the impact of specification changes. For example, the proposal department can simulate the impact of specification changes and propose the optimal countermeasures. Furthermore, the proposal department provides guidelines to minimize the impact of specification changes. This improves system stability by clarifying the impact investigation and countermeasures for specification changes. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input the impact of specification changes into a generative AI, and the generative AI can propose the optimal countermeasures.

[0041] The visualization unit can classify confidential and public data and monitor whether the scope of disclosure is appropriate. For example, the visualization unit classifies data based on its confidentiality. The visualization unit can also automatically monitor the scope of data disclosure. For example, the visualization unit periodically checks the scope of data disclosure and maintains an appropriate scope. Furthermore, the visualization unit has a function to consider data confidentiality when changing the scope of data disclosure. This improves data security through data classification and monitoring of the scope of disclosure. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the data confidentiality into a generative AI, and the generative AI can perform data classification.

[0042] The optimization unit can detect abnormal access patterns. For example, the optimization unit can detect abnormal access frequency. The optimization unit can also automatically identify the source of abnormal access. For example, the optimization unit can analyze access logs and detect abnormal access patterns. Furthermore, the optimization unit has a function for monitoring abnormal access patterns in real time. This makes it possible to take countermeasures quickly upon detection of abnormal access patterns. Some or all of the above processing in the optimization unit may be performed using, for example, a generation AI, or without a generation AI. For example, the optimization unit can input access logs into a generation AI, which can then detect abnormal access patterns.

[0043] The query generation unit can track user behavior. For example, it can analyze access logs and track user behavior. The query generation unit can also automatically identify user behavior patterns. For example, it can analyze a user's access history and identify behavior patterns. The query generation unit also has a function to monitor user behavior in real time. This allows for understanding data usage through tracking user behavior. Some or all of the above processing in the query generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the query generation unit can input access logs into a generation AI, which can then track user behavior.

[0044] The proposal unit can monitor policy violations. For example, the proposal unit monitors policy violations based on policy definitions. The proposal unit can also automatically detect policy violations. For example, the proposal unit analyzes access logs to detect policy violations. The proposal unit also has a function for monitoring policy violations in real time. This enhances data security through policy violation monitoring. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input access logs into a generative AI, which can then detect policy violations.

[0045] The visualization unit can audit data changes. For example, the visualization unit records the history of data changes. The visualization unit can also automatically audit data changes. For example, the visualization unit analyzes the history of data changes and performs an audit. The visualization unit also has a function to perform periodic audits of data changes. This ensures data integrity through audits of data changes. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the visualization unit can input the history of data changes into a generation AI, and the generation AI can perform the audit.

[0046] The optimization unit can provide real-time alerts. For example, the optimization unit provides real-time alerts based on alert trigger conditions. The optimization unit can also automatically select the notification method for alerts. For example, the optimization unit analyzes the alert trigger conditions and proposes the optimal notification method. The optimization unit also has a function to periodically review the alert trigger conditions. This allows for rapid feedback through real-time alerts. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the alert trigger conditions into a generative AI, and the generative AI can provide real-time alerts.

[0047] The visualization unit can select the optimal visualization method by referring to the user's past operation history when visualizing data. For example, the visualization unit may prioritize displaying graph types that the user has frequently used in the past. The visualization unit can also suggest the optimal visualization method for a specific dataset based on the user's past operation history. For example, the visualization unit may analyze the user's operation history and automatically select the most effective visualization method. The visualization unit also has a function to monitor the user's operation history in real time and adjust the visualization method based on the operation history. This allows the optimal visualization method to be selected by referring to the user's past operation history. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input the user's operation history data into a generative AI, which can then select the optimal visualization method.

[0048] The visualization unit can apply different visualization algorithms depending on the type of data when visualizing data. For example, the visualization unit can use line graphs for numerical data and bar graphs for categorical data. It can also use timeline charts for time-series data and maps for geographic data. For example, the visualization unit can use heatmaps for complex datasets and pie charts for simple datasets. The visualization unit also has a function to automatically select a visualization algorithm according to the type of data. This deepens the understanding of the data by applying a visualization algorithm appropriate to the data type. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input the type of data into a generative AI, and the generative AI can apply the optimal visualization algorithm.

[0049] The visualization unit can prioritize the visualization of highly relevant data by considering the user's geographical location information during data visualization. For example, if the user is in a specific region, the visualization unit will prioritize displaying data related to that region. The visualization unit can also automatically select the most relevant data based on the user's location information. For example, if the user is on the move, the visualization unit will update and display data related to the user's current location in real time. Furthermore, the visualization unit has a function to monitor the user's geographical location information in real time and adjust the data priority based on the location information. This ensures that highly relevant data is displayed preferentially by considering the user's geographical location information. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the user's geographical location information into a generative AI, which can then determine the data priority based on the location information.

[0050] The visualization unit can analyze a user's social media activity and visualize relevant data when visualizing data. For example, the visualization unit can display relevant data based on the content of a user's social media posts. The visualization unit can also prioritize the display of relevant data based on the activity of a user's followers and friends. For example, the visualization unit can analyze a user's social media trends and display the most relevant data. The visualization unit also has a function to monitor a user's social media activity in real time and adjust the priority of data based on the activity. This allows relevant data to be displayed by analyzing the user's social media activity. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input user social media activity data into a generative AI, which can then determine the priority of data based on the activity.

[0051] The unification unit can select the optimal naming convention when unifying column naming conventions by referring to the naming history of past databases. For example, the unification unit can analyze column names used in past databases and propose the most frequently used naming conventions. The unification unit can also select naming conventions suitable for a specific project or team from past naming history. For example, the unification unit can automatically select the most consistent naming conventions based on past naming history. Furthermore, the unification unit has a function to monitor past naming history in real time and adjust naming conventions based on the naming history. This allows for the selection of the optimal naming conventions by referring to the naming history of past databases. Some or all of the above processes in the unification unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the unification unit can input past naming history data into a generation AI, which can then select the optimal naming conventions.

[0052] The unification unit can apply different naming conventions depending on the data category when unifying column naming conventions. For example, the unification unit can use a specific prefix for numerical data and a different prefix for categorical data. It can also use a specific suffix for time-series data and a different suffix for geographic data. For example, the unification unit can apply detailed naming conventions to complex datasets and concise naming conventions to simple datasets. The unification unit also has a function to automatically select naming conventions according to the data category. This deepens the understanding of the data by applying naming conventions appropriate to the data category. Some or all of the above processing in the unification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the unification unit can input the data category into a generative AI, and the generative AI can apply the most suitable naming convention.

[0053] The unification unit can prioritize the application of highly relevant naming conventions when unifying column naming conventions, taking into account the user's geographical location information. For example, if a user is in a specific region, the unification unit will prioritize the application of naming conventions related to that region. The unification unit can also automatically select the most relevant naming conventions based on the user's location information. For example, if a user is on the move, the unification unit will update and apply naming conventions related to their current location in real time. The unification unit also has a function to monitor the user's geographical location information in real time and adjust the priority of naming conventions based on that information. This ensures that highly relevant naming conventions are applied preferentially by considering the user's geographical location information. Some or all of the above processing in the unification unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the unification unit can input the user's geographical location information into a generation AI, which can then determine the priority of naming conventions based on that location information.

[0054] The unification unit can analyze a user's social media activity and apply relevant naming conventions when unifying column naming conventions. For example, the unification unit can apply relevant naming conventions based on the content of a user's social media posts. The unification unit can also preferentially apply relevant naming conventions based on the activity of a user's followers and friends. For example, the unification unit can analyze a user's social media trends and apply the most relevant naming conventions. The unification unit also has a function to monitor a user's social media activity in real time and adjust the priority of naming conventions based on the activity content. This ensures that relevant naming conventions are applied by analyzing the user's social media activity. Some or all of the above processing in the unification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the unification unit can input user social media activity data into a generative AI, which can then determine the priority of naming conventions based on the activity content.

[0055] The suggestion unit can select the optimal storage location by referring to the past storage history of the database when proposing a storage location for a table. For example, the suggestion unit can analyze the storage locations used in past databases and propose the most frequently used storage locations. The suggestion unit can also select a storage location suitable for a specific project or team based on past storage history. For example, the suggestion unit can automatically select the most efficient storage location based on past storage history. Furthermore, the suggestion unit has a function to monitor past storage history in real time and adjust storage location suggestions based on the storage history. This allows for the selection of the optimal storage location by referring to the past storage history of the database. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input past storage history data into a generative AI, which can then select the optimal storage location.

[0056] The suggestion function can apply different storage algorithms depending on the data type when suggesting storage locations for tables. For example, the suggestion function may use a specific storage algorithm for numerical data and a different one for categorical data. It may also use a specific storage algorithm for time-series data and a different one for geographic data. For example, the suggestion function may apply a detailed storage algorithm to complex datasets and a concise one to simple datasets. The suggestion function also has a function to automatically select a storage algorithm according to the data type. This speeds up data retrieval and utilization by applying a storage algorithm appropriate to the data type. Some or all of the above processing in the suggestion function may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion function can input the data type into a generative AI, which can then apply the optimal storage algorithm.

[0057] The suggestion unit, when proposing storage locations for tables, can prioritize suggesting highly relevant storage locations by considering the user's geographical location information. For example, if the user is in a specific region, the suggestion unit will prioritize suggesting storage locations related to that region. The suggestion unit can also automatically select the most relevant storage location based on the user's location information. For example, if the user is on the move, the suggestion unit will update and propose storage locations related to the user's current location in real time. The suggestion unit also has a function to monitor the user's geographical location information in real time and adjust the priority of storage locations based on that location information. This ensures that highly relevant storage locations are suggested preferentially by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI, which can then determine the priority of storage locations based on that location information.

[0058] The suggestion unit can analyze the user's social media activity and suggest relevant storage locations when proposing table storage locations. For example, the suggestion unit can suggest relevant storage locations based on the content of the user's social media posts. The suggestion unit can also prioritize suggesting relevant storage locations based on the activity of the user's followers and friends. For example, the suggestion unit can analyze the user's social media trends and suggest the most relevant storage locations. The suggestion unit also has a function to monitor the user's social media activity in real time and adjust the priority of storage locations based on the activity content. This allows relevant storage locations to be suggested by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's social media activity data into a generative AI, which can then determine the priority of storage locations based on the activity content.

[0059] The query creation unit can select the optimal query creation method by referring to the user's past query creation history when creating a query. For example, the query creation unit may prioritize suggesting query structures that the user has frequently used in the past. The query creation unit can also suggest the optimal query creation method for a specific dataset based on the user's past query creation history. For example, the query creation unit may analyze the user's query creation history and automatically select the most effective query creation method. Furthermore, the query creation unit has a function to monitor the user's query creation history in real time and adjust the query creation method based on the history. This allows the optimal query creation method to be selected by referring to the user's past query creation history. Some or all of the above processing in the query creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the query creation unit can input the user's query creation history data into a generative AI, which can then select the optimal query creation method.

[0060] The query generation unit can apply different query generation algorithms depending on the type of data when creating queries. For example, the query generation unit may use a specific query generation algorithm for numerical data and a different one for categorical data. It may also use a specific query generation algorithm for time series data and a different one for geographic data. For example, the query generation unit may apply a detailed query generation algorithm to complex datasets and a concise one to simple datasets. The query generation unit also has a function to automatically select a query generation algorithm according to the type of data. This makes query creation more efficient by applying a query generation algorithm appropriate to the type of data. Some or all of the above processing in the query generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the query generation unit can input the type of data into a generative AI, and the generative AI can apply the optimal query generation algorithm.

[0061] The query generation unit can prioritize the creation of highly relevant queries by considering the user's geographical location information during query creation. For example, if the user is in a specific region, the query generation unit will prioritize the creation of queries related to that region. The query generation unit can also automatically select the most relevant queries based on the user's location information. For example, if the user is on the move, the query generation unit will update and create queries related to the user's current location in real time. Furthermore, the query generation unit has a function to monitor the user's geographical location information in real time and adjust the priority of queries based on that location information. This ensures that highly relevant queries are prioritized by considering the user's geographical location information. Some or all of the above processing in the query generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the query generation unit can input the user's geographical location information into a generation AI, which can then determine the priority of queries based on that location information.

[0062] The query generation unit can analyze a user's social media activity and create relevant queries when generating queries. For example, the query generation unit can create relevant queries based on the content of a user's social media posts. The query generation unit can also prioritize the creation of relevant queries based on the activity of a user's followers and friends. For example, the query generation unit can analyze a user's social media trends and create the most relevant queries. The query generation unit also has a function to monitor a user's social media activity in real time and adjust the priority of queries based on the activity. In this way, relevant queries are created by analyzing a user's social media activity. Some or all of the above processing in the query generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the query generation unit can input user social media activity data into a generative AI, and the generative AI can determine the priority of queries based on the activity.

[0063] The optimization unit can select the optimal optimization method by referring to the user's past query optimization history when optimizing queries. For example, the optimization unit may prioritize suggesting optimization methods that the user has frequently used in the past. The optimization unit can also suggest the optimal optimization method for a specific dataset based on the user's past query optimization history. For example, the optimization unit can analyze the user's query optimization history and automatically select the most effective optimization method. The optimization unit also has a function to monitor the user's query optimization history in real time and adjust the optimization method based on the history. This allows the optimal optimization method to be selected by referring to the user's past query optimization history. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's query optimization history data into a generative AI, which can then select the optimal optimization method.

[0064] The optimization unit can apply different optimization algorithms depending on the data type when optimizing queries. For example, the optimization unit may use a specific optimization algorithm for numerical data and a different one for categorical data. It may also use a specific optimization algorithm for time series data and a different one for geographic data. For example, the optimization unit may apply a detailed optimization algorithm to complex datasets and a concise one to simple datasets. The optimization unit also has a function to automatically select an optimization algorithm according to the data type. This improves the execution efficiency of queries by applying an optimization algorithm appropriate to the data type. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the optimization unit can input the data type into a generative AI, and the generative AI can apply the optimal optimization algorithm.

[0065] The optimization unit can prioritize and optimize queries that are highly relevant, taking into account the user's geographical location information during query optimization. For example, if the user is in a specific region, the optimization unit will prioritize and optimize queries related to that region. The optimization unit can also automatically select the most relevant queries based on the user's location information. For example, if the user is on the move, the optimization unit will update and optimize queries related to the user's current location in real time. The optimization unit also has a function to monitor the user's geographical location information in real time and adjust the priority of queries based on that location information. This ensures that highly relevant queries are prioritized and optimized by considering the user's geographical location information. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's geographical location information into a generative AI, which can then determine the priority of queries based on that location information.

[0066] The optimization unit can analyze a user's social media activity and optimize relevant queries when optimizing queries. For example, the optimization unit optimizes relevant queries based on the content of a user's social media posts. The optimization unit can also prioritize and optimize relevant queries based on the activity of a user's followers and friends. For example, the optimization unit analyzes the user's social media trends and optimizes the most relevant queries. The optimization unit also has a function to monitor the user's social media activity in real time and adjust query priorities based on the activity content. This allows relevant queries to be optimized by analyzing the user's social media activity. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's social media activity data into a generative AI, which can then determine query priorities based on the activity content.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The visualization unit can select the optimal visualization method by referring to the user's past operation history when visualizing data. For example, the visualization unit may prioritize displaying graph types that the user has frequently used in the past. The visualization unit can also suggest the optimal visualization method for a specific dataset based on the user's past operation history. For example, the visualization unit may analyze the user's operation history and automatically select the most effective visualization method. The visualization unit also has a function to monitor the user's operation history in real time and adjust the visualization method based on the operation history. This allows the optimal visualization method to be selected by referring to the user's past operation history. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input the user's operation history data into a generative AI, which can then select the optimal visualization method.

[0069] The unification unit can select the optimal naming convention when unifying column naming conventions by referring to the naming history of past databases. For example, the unification unit can analyze column names used in past databases and propose the most frequently used naming conventions. The unification unit can also select naming conventions suitable for a specific project or team from past naming history. For example, the unification unit can automatically select the most consistent naming conventions based on past naming history. Furthermore, the unification unit has a function to monitor past naming history in real time and adjust naming conventions based on the naming history. This allows for the selection of the optimal naming conventions by referring to the naming history of past databases. Some or all of the above processes in the unification unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the unification unit can input past naming history data into a generation AI, which can then select the optimal naming conventions.

[0070] The suggestion unit can select the optimal storage location by referring to the past storage history of the database when proposing a storage location for a table. For example, the suggestion unit can analyze the storage locations used in past databases and propose the most frequently used storage locations. The suggestion unit can also select a storage location suitable for a specific project or team based on past storage history. For example, the suggestion unit can automatically select the most efficient storage location based on past storage history. Furthermore, the suggestion unit has a function to monitor past storage history in real time and adjust storage location suggestions based on the storage history. This allows for the selection of the optimal storage location by referring to the past storage history of the database. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input past storage history data into a generative AI, which can then select the optimal storage location.

[0071] The query creation unit can select the optimal query creation method by referring to the user's past query creation history when creating a query. For example, the query creation unit may prioritize suggesting query structures that the user has frequently used in the past. The query creation unit can also suggest the optimal query creation method for a specific dataset based on the user's past query creation history. For example, the query creation unit may analyze the user's query creation history and automatically select the most effective query creation method. Furthermore, the query creation unit has a function to monitor the user's query creation history in real time and adjust the query creation method based on the history. This allows the optimal query creation method to be selected by referring to the user's past query creation history. Some or all of the above processing in the query creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the query creation unit can input the user's query creation history data into a generative AI, which can then select the optimal query creation method.

[0072] The optimization unit can select the optimal optimization method by referring to the user's past query optimization history when optimizing queries. For example, the optimization unit may prioritize suggesting optimization methods that the user has frequently used in the past. The optimization unit can also suggest the optimal optimization method for a specific dataset based on the user's past query optimization history. For example, the optimization unit can analyze the user's query optimization history and automatically select the most effective optimization method. The optimization unit also has a function to monitor the user's query optimization history in real time and adjust the optimization method based on the history. This allows the optimal optimization method to be selected by referring to the user's past query optimization history. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's query optimization history data into a generative AI, which can then select the optimal optimization method.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The visualization section visualizes the data. For example, it displays the data structure in the form of graphs, charts, heatmaps, etc., allowing users to visually grasp the overall picture of the data. Furthermore, it can also display the hierarchical structure of the data in a tree format, the relationships between data in a network diagram, and the distribution of data in a histogram or scatter plot. Step 2: The unification unit unifies the column naming conventions based on the data visualized by the visualization unit. For example, it provides guidelines for unifying column naming conventions and unifies naming conventions by adding prefixes or suffixes to column names. It also includes features to avoid affecting existing databases. Step 3: The proposal unit suggests the optimal table storage location based on the column names standardized by the unification unit. For example, it suggests the optimal storage location based on data access frequency and importance, and analyzes data access patterns to suggest the optimal storage location. It also includes functions to maintain data integrity. Step 4: The query creation unit creates the query based on the storage location suggested by the proposal unit. For example, it provides assistance functions for users when creating queries, including query auto-completion and syntax checking functions. Step 5: The optimization unit analyzes the query created by the query generation unit and proposes optimizations. For example, it proposes optimization methods to improve the query execution speed and automatically generates an execution plan for the query. It also analyzes the query execution results and provides feedback for further query optimization.

[0075] (Example of form 2) The data concierge system according to an embodiment of the present invention is an AI service aimed at improving the efficiency and security of data infrastructure management. This data concierge system visualizes data structures, making it easier for users to understand the overall picture of the data. It standardizes column naming conventions to improve the efficiency of data utilization. Furthermore, the AI ​​suggests the optimal table storage location, accelerating data retrieval and utilization. It includes a query creation assistance function to support users in creating queries effectively and efficiently, and also analyzes executed queries to suggest optimizations. In addition, it investigates the impact of specification changes and identifies necessary actions. Regarding data security, the AI ​​classifies confidential and public data and constantly monitors whether the scope of disclosure is appropriate. Furthermore, by detecting abnormal access patterns, it enables rapid countermeasures. It enhances data security and auditing functions by tracking user behavior, monitoring policy violations, and auditing data changes. These functions provide rapid feedback through real-time alerts. For example, the data concierge system visualizes data structures, making it easier for users to understand the overall picture of the data. It standardizes column naming conventions to improve the efficiency of data utilization. Furthermore, the AI ​​suggests the optimal storage location for tables, accelerating data retrieval and utilization. It includes query creation assistance features to support users in creating queries effectively and efficiently, and also analyzes executed queries to suggest optimizations. It also investigates the impact of specification changes and identifies necessary actions. In terms of data security, the AI ​​classifies sensitive and public data and constantly monitors whether the scope of disclosure is appropriate. Furthermore, it can detect abnormal access patterns, enabling rapid countermeasures. Data security and auditing capabilities are enhanced by tracking user behavior, monitoring policy violations, and auditing data changes. These functions provide rapid feedback through real-time alerts. As a result, the data concierge system can significantly improve the efficiency and security of data management.

[0076] The data concierge system according to this embodiment comprises a visualization unit, a unification unit, a proposal unit, a query creation unit, and an optimization unit. The visualization unit visualizes the data. The visualization unit displays the data structure in the form of graphs, charts, heatmaps, etc. The visualization unit makes it possible to visually grasp the overall picture of the data. For example, the visualization unit can display the hierarchical structure of the data in a tree format. The visualization unit can also display the relationships between data in a network diagram. Furthermore, the visualization unit can display the distribution of data in a histogram or scatter plot. The unification unit unifies the naming conventions for column names based on the data visualized by the visualization unit. The unification unit provides, for example, guidelines for unifying the naming conventions for column names. The unification unit can also automatically apply the naming conventions for column names. For example, the unification unit unifies the naming conventions by adding prefixes or suffixes to the column names. The unification unit also has a function to ensure that the changes to the naming conventions for column names do not affect the existing database. The Proposal Unit suggests the optimal table storage location based on the column names standardized by the Unification Unit. For example, the Proposal Unit suggests the optimal storage location based on data access frequency and importance. The Proposal Unit can also automatically select data storage locations. For example, it analyzes data access patterns and suggests the optimal storage location. Furthermore, the Proposal Unit includes features to maintain data integrity when changing data storage locations. The Query Creation Unit creates queries based on the storage locations suggested by the Proposal Unit. The Query Creation Unit provides, for example, auxiliary functions for users creating queries. The Query Creation Unit includes an auto-completion function for queries. For example, it assists in query creation by completing parts of the query entered by the user. The Query Creation Unit also includes a query syntax checking function. For example, it checks the syntax of the query entered by the user and points out errors. The Optimization Unit analyzes the queries created by the Query Creation Unit and suggests optimizations. For example, the Optimization Unit suggests optimization methods to improve query execution speed. The Optimization Unit can also automatically generate query execution plans.For example, the optimization unit analyzes the query execution plan and proposes the optimal execution plan. The optimization unit also analyzes the query execution results and provides feedback for query optimization. This allows the data concierge system to efficiently perform data visualization, standardization of naming conventions, suggestion of optimal storage locations, query creation, and query optimization.

[0077] The visualization unit visualizes data. For example, it displays data structures in formats such as graphs, charts, and heatmaps. Specifically, it selects an appropriate visualization method depending on the type and characteristics of the data. For example, in the case of time-series data, line graphs and bar charts can be used to visually represent data fluctuations. For categorical data, pie charts and bar charts can be used to show data distribution. Heatmaps represent data density and intensity using color intensity, making them suitable for intuitively understanding specific patterns and outliers. Furthermore, the visualization unit can display the hierarchical structure of data in a tree format. The tree format visually shows parent-child relationships and hierarchical structures, making it easier to understand the data structure. It can also display data relationships using network diagrams. Network diagrams use nodes and edges to show relationships between data, helping to visually grasp complex relationships. Additionally, histograms and scatter plots can be used to display data distribution and correlations. Histograms show data distribution as bar graphs, making them suitable for visually understanding data bias and variance. Scatter plots show the correlation between two variables and help visually grasp data trends and patterns. This allows the visualization section to provide a visual overview of the data, supporting data analysis and understanding.

[0078] The unification unit unifies column naming conventions based on data visualized by the visualization unit. For example, the unification unit provides guidelines for unifying column naming conventions. Specifically, these guidelines include adding prefixes and suffixes, using camel case or snake case, and standardizing abbreviations. Based on these guidelines, the unification unit can automatically apply the column naming conventions. For instance, adding prefixes and suffixes to column names clarifies the column's role and data type. Using camel case or snake case improves the readability of column names. Furthermore, standardizing abbreviations maintains consistency in column names. The unification unit includes features to prevent impact on existing databases when changing column naming conventions. Specifically, it automatically modifies the database schema to maintain data integrity. It also manages the column name change history and allows reverting to the original column name if necessary. This enables the unification unit to standardize data naming conventions and streamline data management and utilization.

[0079] The proposal unit suggests the optimal storage location for tables based on column names standardized by the unification unit. For example, the proposal unit suggests the optimal storage location based on data access frequency and importance. Specifically, it analyzes data access patterns and stores frequently accessed data in high-speed storage and less frequently accessed data in cost-effective storage. Furthermore, depending on the importance of the data, critical data is stored in highly redundant storage to ensure data security. The proposal unit can also automatically select data storage locations. For example, it monitors data access patterns in real time and dynamically changes the optimal storage location. In addition, the proposal unit has functions to maintain data integrity when changing data storage locations. Specifically, it performs data integrity checks when data is moved and manages the data movement history. This allows the proposal unit to optimize data storage locations and improve data access performance and cost efficiency.

[0080] The query creation unit creates queries based on the storage locations suggested by the suggestion unit. The query creation unit provides, for example, auxiliary functions for users when creating queries. Specifically, it has an auto-completion function, assisting query creation by completing parts of the query entered by the user. For example, if the user enters part of a table name or column name, the query creation unit presents suggestions, and the user completes the query by selecting from them. The query creation unit also has a query syntax checking function. Specifically, it checks the syntax of the query entered by the user in real time and points out errors. This allows users to create accurate queries. Furthermore, the query creation unit has a query optimization suggestion function, analyzing the execution plan of the query created by the user and suggesting the optimal query. This can improve query execution speed. The query creation unit supports users in creating queries, enabling efficient and accurate query creation.

[0081] The optimization unit analyzes queries created by the query generation unit and proposes optimizations. For example, the optimization unit proposes optimization techniques to improve query execution speed. Specifically, it automatically generates a query execution plan and proposes the optimal execution plan. For example, it analyzes the query execution plan and proposes optimization techniques to improve query execution speed, such as adding indexes or changing join orders. The optimization unit also analyzes the query execution results and provides feedback for query optimization. Specifically, it evaluates the query performance based on the query execution results and points out areas for improvement. Furthermore, the optimization unit can learn query optimization techniques based on past query execution history and use this to improve future query optimization. As a result, the optimization unit can improve query execution speed and optimize database performance.

[0082] The visualization unit can visualize the data structure. For example, the visualization unit can display the data structure in a tree format. The visualization unit makes it possible to visually grasp the hierarchical structure of the data. For example, the visualization unit can also display the relationships between data in a network diagram. The visualization unit can also display the distribution of data in a histogram or scatter plot. This makes it easier for users to understand the overall picture of the data by visualizing the data structure. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the visualization unit can input the data structure into a generative AI, and the generative AI can generate graphs or charts to visualize the data structure.

[0083] The unification unit can standardize column naming conventions. For example, the unification unit provides guidelines for standardizing column naming conventions. The unification unit can also automatically apply column naming conventions. For example, the unification unit standardizes naming conventions by adding prefixes or suffixes to column names. Furthermore, the unification unit has a function to ensure that changes to column naming conventions do not affect existing databases. This improves the efficiency of data utilization through the standardization of column naming conventions. Some or all of the above processing in the unification unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the unification unit can input column naming conventions into a generation AI, and the generation AI can propose the optimal naming convention.

[0084] The suggestion unit can propose the optimal storage location for tables. For example, it can propose the optimal storage location based on the frequency and importance of data access. The suggestion unit can also automatically select the data storage location. For example, it can analyze data access patterns and propose the optimal storage location. Furthermore, the suggestion unit has a function to maintain data integrity when changing data storage locations. This speeds up data retrieval and utilization by suggesting the optimal table storage location. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input data access patterns into a generative AI, which can then propose the optimal storage location.

[0085] The query creation unit can assist in query creation. For example, the query creation unit provides assistance functions when a user creates a query. The query creation unit has an auto-completion function for queries. For example, the query creation unit assists in query creation by completing parts of the query entered by the user. The query creation unit also has a query syntax checking function. For example, the query creation unit checks the syntax of the query entered by the user and points out errors. This allows the user to create queries effectively and efficiently through query creation assistance. Some or all of the above processing in the query creation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the query creation unit can input a part of the query entered by the user into the generation AI, and the generation AI can complete the query.

[0086] The optimization unit can analyze the execution query and propose optimizations. For example, the optimization unit proposes optimization methods to improve the execution speed of the query. The optimization unit can also automatically generate the execution plan of the query. For example, the optimization unit analyzes the execution plan of the query and proposes the optimal execution plan. The optimization unit also analyzes the execution results of the query and provides feedback for query optimization. As a result, the execution efficiency of the query is improved through the analysis of the execution query and the proposal of optimizations. Some or all of the above processing in the optimization unit may be performed using, for example, a generation AI, or without a generation AI. For example, the optimization unit can input the execution query to a generation AI, and the generation AI can perform query optimization.

[0087] The proposal department can investigate the impact of specification changes and identify necessary countermeasures. For example, the proposal department can identify the scope of impact of specification changes and propose necessary countermeasures. The proposal department can also automatically analyze the impact of specification changes. For example, the proposal department can simulate the impact of specification changes and propose the optimal countermeasures. Furthermore, the proposal department provides guidelines to minimize the impact of specification changes. This improves system stability by clarifying the impact investigation and countermeasures for specification changes. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input the impact of specification changes into a generative AI, and the generative AI can propose the optimal countermeasures.

[0088] The visualization unit can classify confidential and public data and monitor whether the scope of disclosure is appropriate. For example, the visualization unit classifies data based on its confidentiality. The visualization unit can also automatically monitor the scope of data disclosure. For example, the visualization unit periodically checks the scope of data disclosure and maintains an appropriate scope. Furthermore, the visualization unit has a function to consider data confidentiality when changing the scope of data disclosure. This improves data security through data classification and monitoring of the scope of disclosure. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the data confidentiality into a generative AI, and the generative AI can perform data classification.

[0089] The optimization unit can detect abnormal access patterns. For example, the optimization unit can detect abnormal access frequency. The optimization unit can also automatically identify the source of abnormal access. For example, the optimization unit can analyze access logs and detect abnormal access patterns. Furthermore, the optimization unit has a function for monitoring abnormal access patterns in real time. This makes it possible to take countermeasures quickly upon detection of abnormal access patterns. Some or all of the above processing in the optimization unit may be performed using, for example, a generation AI, or without a generation AI. For example, the optimization unit can input access logs into a generation AI, which can then detect abnormal access patterns.

[0090] The query generation unit can track user behavior. For example, it can analyze access logs and track user behavior. The query generation unit can also automatically identify user behavior patterns. For example, it can analyze a user's access history and identify behavior patterns. The query generation unit also has a function to monitor user behavior in real time. This allows for understanding data usage through tracking user behavior. Some or all of the above processing in the query generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the query generation unit can input access logs into a generation AI, which can then track user behavior.

[0091] The proposal unit can monitor policy violations. For example, the proposal unit monitors policy violations based on policy definitions. The proposal unit can also automatically detect policy violations. For example, the proposal unit analyzes access logs to detect policy violations. The proposal unit also has a function for monitoring policy violations in real time. This enhances data security through policy violation monitoring. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input access logs into a generative AI, which can then detect policy violations.

[0092] The visualization unit can audit data changes. For example, the visualization unit records the history of data changes. The visualization unit can also automatically audit data changes. For example, the visualization unit analyzes the history of data changes and performs an audit. The visualization unit also has a function to perform periodic audits of data changes. This ensures data integrity through audits of data changes. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the visualization unit can input the history of data changes into a generation AI, and the generation AI can perform the audit.

[0093] The optimization unit can provide real-time alerts. For example, the optimization unit provides real-time alerts based on alert trigger conditions. The optimization unit can also automatically select the notification method for alerts. For example, the optimization unit analyzes the alert trigger conditions and proposes the optimal notification method. The optimization unit also has a function to periodically review the alert trigger conditions. This allows for rapid feedback through real-time alerts. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the alert trigger conditions into a generative AI, and the generative AI can provide real-time alerts.

[0094] The visualization unit can estimate the user's emotions and adjust the data visualization method based on the estimated user emotions. For example, if the user is stressed, the visualization unit can display a simple and intuitive graph. If the user is relaxed, the visualization unit can also display a complex graph containing detailed data. For example, if the user is in a hurry, the visualization unit can display a concise graph highlighting the key points. The visualization unit also has a function to monitor the user's emotions in real time and adjust the visualization method according to changes in emotions. This allows for a deeper understanding of the user by adjusting the data visualization method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using a generative AI, or not. For example, the visualization unit can input user emotion data into a generative AI, which can then adjust the visualization method based on the emotions.

[0095] The visualization unit can select the optimal visualization method by referring to the user's past operation history when visualizing data. For example, the visualization unit may prioritize displaying graph types that the user has frequently used in the past. The visualization unit can also suggest the optimal visualization method for a specific dataset based on the user's past operation history. For example, the visualization unit may analyze the user's operation history and automatically select the most effective visualization method. The visualization unit also has a function to monitor the user's operation history in real time and adjust the visualization method based on the operation history. This allows the optimal visualization method to be selected by referring to the user's past operation history. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input the user's operation history data into a generative AI, which can then select the optimal visualization method.

[0096] The visualization unit can apply different visualization algorithms depending on the type of data when visualizing data. For example, the visualization unit can use line graphs for numerical data and bar graphs for categorical data. It can also use timeline charts for time-series data and maps for geographic data. For example, the visualization unit can use heatmaps for complex datasets and pie charts for simple datasets. The visualization unit also has a function to automatically select a visualization algorithm according to the type of data. This deepens the understanding of the data by applying a visualization algorithm appropriate to the data type. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input the type of data into a generative AI, and the generative AI can apply the optimal visualization algorithm.

[0097] The visualization unit can estimate the user's emotions and determine the priority of data to visualize based on the estimated user emotions. For example, if the user is stressed, the visualization unit will prioritize displaying important data. If the user is relaxed, the visualization unit can also display a comprehensive overview including detailed data. For example, if the user is in a hurry, the visualization unit will prioritize displaying data that highlights key points. The visualization unit also has a function to monitor the user's emotions in real time and adjust data priorities according to changes in emotions. This ensures that important data is displayed preferentially by determining data priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input user emotion data into a generative AI, which can then determine the priority of data based on the emotions.

[0098] The visualization unit can prioritize the visualization of highly relevant data by considering the user's geographical location information during data visualization. For example, if the user is in a specific region, the visualization unit will prioritize displaying data related to that region. The visualization unit can also automatically select the most relevant data based on the user's location information. For example, if the user is on the move, the visualization unit will update and display data related to the user's current location in real time. Furthermore, the visualization unit has a function to monitor the user's geographical location information in real time and adjust the data priority based on the location information. This ensures that highly relevant data is displayed preferentially by considering the user's geographical location information. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the user's geographical location information into a generative AI, which can then determine the data priority based on the location information.

[0099] The visualization unit can analyze a user's social media activity and visualize relevant data when visualizing data. For example, the visualization unit can display relevant data based on the content of a user's social media posts. The visualization unit can also prioritize the display of relevant data based on the activity of a user's followers and friends. For example, the visualization unit can analyze a user's social media trends and display the most relevant data. The visualization unit also has a function to monitor a user's social media activity in real time and adjust the priority of data based on the activity. This allows relevant data to be displayed by analyzing the user's social media activity. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input user social media activity data into a generative AI, which can then determine the priority of data based on the activity.

[0100] The unification unit can estimate the user's emotions and adjust the column naming convention based on the estimated emotions. For example, if the user is stressed, the unification unit suggests simple and intuitive column names. If the user is relaxed, the unification unit can also suggest column names that include detailed descriptions. For example, if the user is in a hurry, the unification unit suggests short and easy-to-remember column names. The unification unit also has a function to monitor the user's emotions in real time and adjust the column naming convention in response to changes in emotions. This allows for a deeper understanding of the user by adjusting the column naming convention according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the unification unit may be performed using a generative AI, or not. For example, the unification unit can input user emotion data into a generative AI, which can then adjust the column naming convention based on the emotions.

[0101] The unification unit can select the optimal naming convention when unifying column naming conventions by referring to the naming history of past databases. For example, the unification unit can analyze column names used in past databases and propose the most frequently used naming conventions. The unification unit can also select naming conventions suitable for a specific project or team from past naming history. For example, the unification unit can automatically select the most consistent naming conventions based on past naming history. Furthermore, the unification unit has a function to monitor past naming history in real time and adjust naming conventions based on the naming history. This allows for the selection of the optimal naming conventions by referring to the naming history of past databases. Some or all of the above processes in the unification unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the unification unit can input past naming history data into a generation AI, which can then select the optimal naming conventions.

[0102] The unification unit can apply different naming conventions depending on the data category when unifying column naming conventions. For example, the unification unit can use a specific prefix for numerical data and a different prefix for categorical data. It can also use a specific suffix for time-series data and a different suffix for geographic data. For example, the unification unit can apply detailed naming conventions to complex datasets and concise naming conventions to simple datasets. The unification unit also has a function to automatically select naming conventions according to the data category. This deepens the understanding of the data by applying naming conventions appropriate to the data category. Some or all of the above processing in the unification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the unification unit can input the data category into a generative AI, and the generative AI can apply the most suitable naming convention.

[0103] The unification unit can estimate the user's emotions and determine the priority of naming conventions based on the estimated emotions. For example, if the user is stressed, the unification unit will prioritize the naming conventions for important column names. If the user is relaxed, the unification unit may also prioritize detailed naming conventions. For example, if the user is in a hurry, the unification unit will prioritize concise naming conventions. The unification unit also has a function to monitor the user's emotions in real time and adjust the priority of naming conventions according to changes in emotions. This ensures that important naming conventions are applied preferentially by determining the priority of naming conventions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the unification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the unification unit can input user emotion data into a generating AI, which can then determine the priority of naming conventions based on the emotion.

[0104] The unification unit can prioritize the application of highly relevant naming conventions when unifying column naming conventions, taking into account the user's geographical location information. For example, if a user is in a specific region, the unification unit will prioritize the application of naming conventions related to that region. The unification unit can also automatically select the most relevant naming conventions based on the user's location information. For example, if a user is on the move, the unification unit will update and apply naming conventions related to their current location in real time. The unification unit also has a function to monitor the user's geographical location information in real time and adjust the priority of naming conventions based on that information. This ensures that highly relevant naming conventions are applied preferentially by considering the user's geographical location information. Some or all of the above processing in the unification unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the unification unit can input the user's geographical location information into a generation AI, which can then determine the priority of naming conventions based on that location information.

[0105] The unification unit can analyze a user's social media activity and apply relevant naming conventions when unifying column naming conventions. For example, the unification unit can apply relevant naming conventions based on the content of a user's social media posts. The unification unit can also preferentially apply relevant naming conventions based on the activity of a user's followers and friends. For example, the unification unit can analyze a user's social media trends and apply the most relevant naming conventions. The unification unit also has a function to monitor a user's social media activity in real time and adjust the priority of naming conventions based on the activity content. This ensures that relevant naming conventions are applied by analyzing the user's social media activity. Some or all of the above processing in the unification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the unification unit can input user social media activity data into a generative AI, which can then determine the priority of naming conventions based on the activity content.

[0106] The suggestion unit can estimate the user's emotions and suggest the optimal storage location for the table based on the estimated emotions. For example, if the user is stressed, the suggestion unit may suggest a simple and intuitive storage location. If the user is relaxed, the suggestion unit may also suggest a storage location with detailed explanations. For example, if the user is in a hurry, the suggestion unit may suggest a storage location that can be accessed quickly. The suggestion unit also has a function to monitor the user's emotions in real time and adjust the storage location suggestions according to changes in emotions. This speeds up data retrieval and utilization by suggesting the optimal table storage location according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, and the generative AI can suggest a storage location based on the emotions.

[0107] The suggestion unit can select the optimal storage location by referring to the past storage history of the database when proposing a storage location for a table. For example, the suggestion unit can analyze the storage locations used in past databases and propose the most frequently used storage locations. The suggestion unit can also select a storage location suitable for a specific project or team based on past storage history. For example, the suggestion unit can automatically select the most efficient storage location based on past storage history. Furthermore, the suggestion unit has a function to monitor past storage history in real time and adjust storage location suggestions based on the storage history. This allows for the selection of the optimal storage location by referring to the past storage history of the database. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input past storage history data into a generative AI, which can then select the optimal storage location.

[0108] The suggestion function can apply different storage algorithms depending on the data type when suggesting storage locations for tables. For example, the suggestion function may use a specific storage algorithm for numerical data and a different one for categorical data. It may also use a specific storage algorithm for time-series data and a different one for geographic data. For example, the suggestion function may apply a detailed storage algorithm to complex datasets and a concise one to simple datasets. The suggestion function also has a function to automatically select a storage algorithm according to the data type. This speeds up data retrieval and utilization by applying a storage algorithm appropriate to the data type. Some or all of the above processing in the suggestion function may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion function can input the data type into a generative AI, which can then apply the optimal storage algorithm.

[0109] The suggestion unit can estimate the user's emotions and determine storage location priorities based on the estimated emotions. For example, if the user is stressed, the suggestion unit may prioritize suggesting storage locations for important tables. If the user is relaxed, the suggestion unit may also prioritize suggesting detailed storage locations. For example, if the user is in a hurry, the suggestion unit may prioritize suggesting storage locations that can be accessed quickly. The suggestion unit also has a function to monitor the user's emotions in real time and adjust storage location priorities according to changes in emotions. This ensures that important data is stored preferentially by determining storage location priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then determine storage location priorities based on the emotions.

[0110] The suggestion unit, when proposing storage locations for tables, can prioritize suggesting highly relevant storage locations by considering the user's geographical location information. For example, if the user is in a specific region, the suggestion unit will prioritize suggesting storage locations related to that region. The suggestion unit can also automatically select the most relevant storage location based on the user's location information. For example, if the user is on the move, the suggestion unit will update and propose storage locations related to the user's current location in real time. The suggestion unit also has a function to monitor the user's geographical location information in real time and adjust the priority of storage locations based on that location information. This ensures that highly relevant storage locations are suggested preferentially by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI, which can then determine the priority of storage locations based on that location information.

[0111] The suggestion unit can analyze the user's social media activity and suggest relevant storage locations when proposing table storage locations. For example, the suggestion unit can suggest relevant storage locations based on the content of the user's social media posts. The suggestion unit can also prioritize suggesting relevant storage locations based on the activity of the user's followers and friends. For example, the suggestion unit can analyze the user's social media trends and suggest the most relevant storage locations. The suggestion unit also has a function to monitor the user's social media activity in real time and adjust the priority of storage locations based on the activity content. This allows relevant storage locations to be suggested by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's social media activity data into a generative AI, which can then determine the priority of storage locations based on the activity content.

[0112] The query generation unit can estimate the user's emotions and adjust the query generation method based on the estimated emotions. For example, if the user is stressed, the query generation unit provides a simple and intuitive query generation interface. If the user is relaxed, the query generation unit can also provide a query generation interface with detailed options. For example, if the user is in a hurry, the query generation unit can prioritize voice input to enable rapid query generation. The query generation unit also has the capability to monitor the user's emotions in real time and adjust the query generation method in response to changes in emotions. This allows for a deeper understanding of the user by adjusting the query generation method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the query generation unit may be performed using or without a generative AI. For example, the query generation unit can input user emotion data into a generative AI, which can then adjust the query generation method based on the emotions.

[0113] The query creation unit can select the optimal query creation method by referring to the user's past query creation history when creating a query. For example, the query creation unit may prioritize suggesting query structures that the user has frequently used in the past. The query creation unit can also suggest the optimal query creation method for a specific dataset based on the user's past query creation history. For example, the query creation unit may analyze the user's query creation history and automatically select the most effective query creation method. Furthermore, the query creation unit has a function to monitor the user's query creation history in real time and adjust the query creation method based on the history. This allows the optimal query creation method to be selected by referring to the user's past query creation history. Some or all of the above processing in the query creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the query creation unit can input the user's query creation history data into a generative AI, which can then select the optimal query creation method.

[0114] The query generation unit can apply different query generation algorithms depending on the type of data when creating queries. For example, the query generation unit may use a specific query generation algorithm for numerical data and a different one for categorical data. It may also use a specific query generation algorithm for time series data and a different one for geographic data. For example, the query generation unit may apply a detailed query generation algorithm to complex datasets and a concise one to simple datasets. The query generation unit also has a function to automatically select a query generation algorithm according to the type of data. This makes query creation more efficient by applying a query generation algorithm appropriate to the type of data. Some or all of the above processing in the query generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the query generation unit can input the type of data into a generative AI, and the generative AI can apply the optimal query generation algorithm.

[0115] The query generation unit can estimate the user's emotions and determine query priorities based on those emotions. For example, if the user is stressed, the query generation unit will prioritize important queries. If the user is relaxed, the query generation unit may also prioritize detailed queries. For example, if the user is in a hurry, the query generation unit will prioritize queries that can be executed quickly. The query generation unit also has a function to monitor the user's emotions in real time and adjust query priorities according to changes in emotions. This ensures that important queries are prioritized by determining query priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the query generation unit may be performed using a generative AI, or not. For example, the query generation unit can input user emotion data into a generative AI, which can then determine query priorities based on those emotions.

[0116] The query generation unit can prioritize the creation of highly relevant queries by considering the user's geographical location information during query creation. For example, if the user is in a specific region, the query generation unit will prioritize the creation of queries related to that region. The query generation unit can also automatically select the most relevant queries based on the user's location information. For example, if the user is on the move, the query generation unit will update and create queries related to the user's current location in real time. Furthermore, the query generation unit has a function to monitor the user's geographical location information in real time and adjust the priority of queries based on that location information. This ensures that highly relevant queries are prioritized by considering the user's geographical location information. Some or all of the above processing in the query generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the query generation unit can input the user's geographical location information into a generation AI, which can then determine the priority of queries based on that location information.

[0117] The query generation unit can analyze a user's social media activity and create relevant queries when generating queries. For example, the query generation unit can create relevant queries based on the content of a user's social media posts. The query generation unit can also prioritize the creation of relevant queries based on the activity of a user's followers and friends. For example, the query generation unit can analyze a user's social media trends and create the most relevant queries. The query generation unit also has a function to monitor a user's social media activity in real time and adjust the priority of queries based on the activity. In this way, relevant queries are created by analyzing a user's social media activity. Some or all of the above processing in the query generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the query generation unit can input user social media activity data into a generative AI, and the generative AI can determine the priority of queries based on the activity.

[0118] The optimization unit can estimate the user's emotions and adjust the query optimization method based on the estimated user emotions. For example, if the user is stressed, the optimization unit provides a simple and intuitive optimization method. If the user is relaxed, the optimization unit can also provide an optimization method with detailed options. For example, if the user is in a hurry, the optimization unit provides a method that allows for rapid optimization. The optimization unit also has a function to monitor the user's emotions in real time and adjust the optimization method in response to changes in emotions. This improves the execution efficiency of queries by adjusting the query optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using a generative AI, or not using a generative AI. For example, the optimization unit can input user emotion data into a generative AI, and the generative AI can adjust the optimization method based on the emotions.

[0119] The optimization unit can select the optimal optimization method by referring to the user's past query optimization history when optimizing queries. For example, the optimization unit may prioritize suggesting optimization methods that the user has frequently used in the past. The optimization unit can also suggest the optimal optimization method for a specific dataset based on the user's past query optimization history. For example, the optimization unit can analyze the user's query optimization history and automatically select the most effective optimization method. The optimization unit also has a function to monitor the user's query optimization history in real time and adjust the optimization method based on the history. This allows the optimal optimization method to be selected by referring to the user's past query optimization history. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's query optimization history data into a generative AI, which can then select the optimal optimization method.

[0120] The optimization unit can apply different optimization algorithms depending on the data type when optimizing queries. For example, the optimization unit may use a specific optimization algorithm for numerical data and a different one for categorical data. It may also use a specific optimization algorithm for time series data and a different one for geographic data. For example, the optimization unit may apply a detailed optimization algorithm to complex datasets and a concise one to simple datasets. The optimization unit also has a function to automatically select an optimization algorithm according to the data type. This improves the execution efficiency of queries by applying an optimization algorithm appropriate to the data type. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the optimization unit can input the data type into a generative AI, and the generative AI can apply the optimal optimization algorithm.

[0121] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is stressed, the optimization unit will prioritize optimizing important queries. If the user is relaxed, the optimization unit may also prioritize detailed optimizations. For example, if the user is in a hurry, the optimization unit will prioritize queries that can be optimized quickly. The optimization unit also has a function to monitor the user's emotions in real time and adjust optimization priorities according to changes in emotions. This ensures that important queries are optimized preferentially by determining optimization priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using a generative AI, or not using a generative AI. For example, the optimization unit can input user emotion data into a generative AI, which can then determine optimization priorities based on the emotions.

[0122] The optimization unit can prioritize and optimize queries that are highly relevant, taking into account the user's geographical location information during query optimization. For example, if the user is in a specific region, the optimization unit will prioritize and optimize queries related to that region. The optimization unit can also automatically select the most relevant queries based on the user's location information. For example, if the user is on the move, the optimization unit will update and optimize queries related to the user's current location in real time. The optimization unit also has a function to monitor the user's geographical location information in real time and adjust the priority of queries based on that location information. This ensures that highly relevant queries are prioritized and optimized by considering the user's geographical location information. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's geographical location information into a generative AI, which can then determine the priority of queries based on that location information.

[0123] The optimization unit can analyze a user's social media activity and optimize relevant queries when optimizing queries. For example, the optimization unit optimizes relevant queries based on the content of a user's social media posts. The optimization unit can also prioritize and optimize relevant queries based on the activity of a user's followers and friends. For example, the optimization unit analyzes the user's social media trends and optimizes the most relevant queries. The optimization unit also has a function to monitor the user's social media activity in real time and adjust query priorities based on the activity content. This allows relevant queries to be optimized by analyzing the user's social media activity. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's social media activity data into a generative AI, which can then determine query priorities based on the activity content.

[0124] The optimization unit can estimate the user's emotions and adjust the query optimization method based on the estimated user emotions. For example, if the user is stressed, the optimization unit provides a simple and intuitive optimization method. If the user is relaxed, the optimization unit can also provide an optimization method with detailed options. For example, if the user is in a hurry, the optimization unit provides a method that allows for rapid optimization. The optimization unit also has a function to monitor the user's emotions in real time and adjust the optimization method in response to changes in emotions. This improves the execution efficiency of queries by adjusting the query optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using a generative AI, or not using a generative AI. For example, the optimization unit can input user emotion data into a generative AI, and the generative AI can adjust the optimization method based on the emotions.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The suggestion unit can estimate the user's emotions and suggest the optimal storage location for the table based on the estimated emotions. For example, if the user is stressed, the suggestion unit may suggest a simple and intuitive storage location. If the user is relaxed, the suggestion unit may also suggest a storage location with detailed explanations. For example, if the user is in a hurry, the suggestion unit may suggest a storage location that can be accessed quickly. The suggestion unit also has a function to monitor the user's emotions in real time and adjust the storage location suggestions according to changes in emotions. This speeds up data retrieval and utilization by suggesting the optimal table storage location according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, and the generative AI can suggest a storage location based on the emotions.

[0127] The visualization unit can estimate the user's emotions and adjust the data visualization method based on the estimated user emotions. For example, if the user is stressed, the visualization unit can display a simple and intuitive graph. If the user is relaxed, the visualization unit can also display a complex graph containing detailed data. For example, if the user is in a hurry, the visualization unit can display a concise graph highlighting the key points. The visualization unit also has a function to monitor the user's emotions in real time and adjust the visualization method according to changes in emotions. This allows for a deeper understanding of the user by adjusting the data visualization method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using a generative AI, or not. For example, the visualization unit can input user emotion data into a generative AI, which can then adjust the visualization method based on the emotions.

[0128] The unification unit can estimate the user's emotions and adjust the column naming convention based on the estimated emotions. For example, if the user is stressed, the unification unit suggests simple and intuitive column names. If the user is relaxed, the unification unit can also suggest column names that include detailed descriptions. For example, if the user is in a hurry, the unification unit suggests short and easy-to-remember column names. The unification unit also has a function to monitor the user's emotions in real time and adjust the column naming convention in response to changes in emotions. This allows for a deeper understanding of the user by adjusting the column naming convention according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the unification unit may be performed using a generative AI, or not. For example, the unification unit can input user emotion data into a generative AI, which can then adjust the column naming convention based on the emotions.

[0129] The query generation unit can estimate the user's emotions and adjust the query generation method based on the estimated emotions. For example, if the user is stressed, the query generation unit provides a simple and intuitive query generation interface. If the user is relaxed, the query generation unit can also provide a query generation interface with detailed options. For example, if the user is in a hurry, the query generation unit can prioritize voice input to enable rapid query generation. The query generation unit also has the capability to monitor the user's emotions in real time and adjust the query generation method in response to changes in emotions. This allows for a deeper understanding of the user by adjusting the query generation method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the query generation unit may be performed using or without a generative AI. For example, the query generation unit can input user emotion data into a generative AI, which can then adjust the query generation method based on the emotions.

[0130] The optimization unit can estimate the user's emotions and adjust the query optimization method based on the estimated user emotions. For example, if the user is stressed, the optimization unit provides a simple and intuitive optimization method. If the user is relaxed, the optimization unit can also provide an optimization method with detailed options. For example, if the user is in a hurry, the optimization unit provides a method that allows for rapid optimization. The optimization unit also has a function to monitor the user's emotions in real time and adjust the optimization method in response to changes in emotions. This improves the execution efficiency of queries by adjusting the query optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using a generative AI, or not using a generative AI. For example, the optimization unit can input user emotion data into a generative AI, and the generative AI can adjust the optimization method based on the emotions.

[0131] The visualization unit can select the optimal visualization method by referring to the user's past operation history when visualizing data. For example, the visualization unit may prioritize displaying graph types that the user has frequently used in the past. The visualization unit can also suggest the optimal visualization method for a specific dataset based on the user's past operation history. For example, the visualization unit may analyze the user's operation history and automatically select the most effective visualization method. The visualization unit also has a function to monitor the user's operation history in real time and adjust the visualization method based on the operation history. This allows the optimal visualization method to be selected by referring to the user's past operation history. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input the user's operation history data into a generative AI, which can then select the optimal visualization method.

[0132] The unification unit can select the optimal naming convention when unifying column naming conventions by referring to the naming history of past databases. For example, the unification unit can analyze column names used in past databases and propose the most frequently used naming conventions. The unification unit can also select naming conventions suitable for a specific project or team from past naming history. For example, the unification unit can automatically select the most consistent naming conventions based on past naming history. Furthermore, the unification unit has a function to monitor past naming history in real time and adjust naming conventions based on the naming history. This allows for the selection of the optimal naming conventions by referring to the naming history of past databases. Some or all of the above processes in the unification unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the unification unit can input past naming history data into a generation AI, which can then select the optimal naming conventions.

[0133] The suggestion unit can select the optimal storage location by referring to the past storage history of the database when proposing a storage location for a table. For example, the suggestion unit can analyze the storage locations used in past databases and propose the most frequently used storage locations. The suggestion unit can also select a storage location suitable for a specific project or team based on past storage history. For example, the suggestion unit can automatically select the most efficient storage location based on past storage history. Furthermore, the suggestion unit has a function to monitor past storage history in real time and adjust storage location suggestions based on the storage history. This allows for the selection of the optimal storage location by referring to the past storage history of the database. Some or all of the above processes in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input past storage history data into a generative AI, which can then select the optimal storage location.

[0134] The query creation unit can select the optimal query creation method by referring to the user's past query creation history when creating a query. For example, the query creation unit may prioritize suggesting query structures that the user has frequently used in the past. The query creation unit can also suggest the optimal query creation method for a specific dataset based on the user's past query creation history. For example, the query creation unit may analyze the user's query creation history and automatically select the most effective query creation method. Furthermore, the query creation unit has a function to monitor the user's query creation history in real time and adjust the query creation method based on the history. This allows the optimal query creation method to be selected by referring to the user's past query creation history. Some or all of the above processing in the query creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the query creation unit can input the user's query creation history data into a generative AI, which can then select the optimal query creation method.

[0135] The optimization unit can select the optimal optimization method by referring to the user's past query optimization history when optimizing queries. For example, the optimization unit may prioritize suggesting optimization methods that the user has frequently used in the past. The optimization unit can also suggest the optimal optimization method for a specific dataset based on the user's past query optimization history. For example, the optimization unit can analyze the user's query optimization history and automatically select the most effective optimization method. The optimization unit also has a function to monitor the user's query optimization history in real time and adjust the optimization method based on the history. This allows the optimal optimization method to be selected by referring to the user's past query optimization history. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's query optimization history data into a generative AI, which can then select the optimal optimization method.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The visualization section visualizes the data. For example, it displays the data structure in the form of graphs, charts, heatmaps, etc., allowing users to visually grasp the overall picture of the data. Furthermore, it can also display the hierarchical structure of the data in a tree format, the relationships between data in a network diagram, and the distribution of data in a histogram or scatter plot. Step 2: The unification unit unifies the column naming conventions based on the data visualized by the visualization unit. For example, it provides guidelines for unifying column naming conventions and unifies naming conventions by adding prefixes or suffixes to column names. It also includes features to avoid affecting existing databases. Step 3: The proposal unit suggests the optimal table storage location based on the column names standardized by the unification unit. For example, it suggests the optimal storage location based on data access frequency and importance, and analyzes data access patterns to suggest the optimal storage location. It also includes functions to maintain data integrity. Step 4: The query creation unit creates the query based on the storage location suggested by the proposal unit. For example, it provides assistance functions for users when creating queries, including query auto-completion and syntax checking functions. Step 5: The optimization unit analyzes the query created by the query generation unit and proposes optimizations. For example, it proposes optimization methods to improve the query execution speed and automatically generates an execution plan for the query. It also analyzes the query execution results and provides feedback for further query optimization.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the visualization unit, unification unit, proposal unit, query creation unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the visualization unit displays the data structure in the form of graphs, charts, heatmaps, etc., using the display 40A of the smart device 14. The unification unit unifies the naming conventions for column names using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal storage location for tables using the specific processing unit 290 of the data processing unit 12. The query creation unit assists in query creation using the control unit 46A of the smart device 14. The optimization unit proposes query optimization using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the visualization unit, unification unit, proposal unit, query creation unit, and optimization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the visualization unit displays the data structure in the form of graphs, charts, heatmaps, etc., using the display of the smart glasses 214. The unification unit unifies the naming conventions for column names using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal storage location for tables using the specific processing unit 290 of the data processing unit 12. The query creation unit assists in query creation using the control unit 46A of the smart glasses 214. The optimization unit proposes query optimization using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the visualization unit, unification unit, proposal unit, query creation unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the visualization unit displays the data structure in the form of graphs, charts, heatmaps, etc., using the display 343 of the headset terminal 314. The unification unit unifies the naming conventions for column names using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal storage location for tables using the specific processing unit 290 of the data processing unit 12. The query creation unit assists in query creation using the control unit 46A of the headset terminal 314. The optimization unit proposes query optimization using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0178] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0181] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the visualization unit, unification unit, proposal unit, query creation unit, and optimization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the visualization unit displays the data structure in the form of graphs, charts, heatmaps, etc., using the display of the robot 414. The unification unit unifies the naming conventions for column names by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal storage location for tables by the specific processing unit 290 of the data processing unit 12. The query creation unit assists in query creation by the control unit 46A of the robot 414. The optimization unit proposes query optimization by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0191] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0194] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0200] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0201] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0202] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0203] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0204] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0209] (Note 1) A visualization unit that visualizes the data, A unification unit that standardizes the naming conventions for column names based on the data visualized by the visualization unit, A proposal unit that proposes the optimal storage location for the table based on the column names standardized by the aforementioned unification unit, A query creation unit that creates a query based on the storage location proposed by the proposal unit, The system includes an optimization unit that analyzes the queries created by the query generation unit and proposes optimizations. A system characterized by the following features. (Note 2) The aforementioned visualization unit, Visualize data structures The system described in Appendix 1, characterized by the features described herein. (Note 3) The unification unit is, Standardize the naming conventions for column names. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the optimal storage location for the tables. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned query generation unit, Helps create queries The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, It analyzes the execution query and suggests optimizations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, Investigate the impact of specification changes and identify necessary countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned visualization unit, We classify confidential and public data and monitor whether the scope of disclosure is appropriate. The system described in Appendix 1, characterized by the features described herein. (Note 9) The optimization unit, Detect abnormal access patterns The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned query generation unit, Track user behavior The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, Monitor for policy violations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned visualization unit, Audit data changes The system described in Appendix 1, characterized by the features described herein. (Note 13) The optimization unit, We provide real-time alerts. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned visualization unit, We estimate user emotions and adjust the data visualization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned visualization unit, When visualizing data, the optimal visualization method is selected by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned visualization unit, When visualizing data, different visualization algorithms are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned visualization unit, It estimates user emotions and determines the priority of data to visualize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, When visualizing data, the system prioritizes the visualization of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, When visualizing data, analyze users' social media activity and visualize the relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The unification unit is, The system estimates user sentiment and adjusts column naming conventions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The unification unit is, The system estimates user sentiment and adjusts column naming conventions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The unification unit is, When standardizing column naming conventions, refer to the naming history of past databases to select the most suitable naming convention. The system described in Appendix 1, characterized by the features described herein. (Note 23) The unification unit is, When standardizing column naming conventions, apply different naming conventions depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The unification unit is, The system estimates user sentiment and determines naming convention priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The unification unit is, When standardizing column naming conventions, prioritize applying naming conventions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The unification unit is, When standardizing column naming conventions, we analyze users' social media activity and apply relevant naming conventions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and suggests the optimal storage location for the table based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When suggesting a storage location for a table, the system selects the optimal location by referring to the past storage history of the database. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When suggesting a storage location for a table, different storage algorithms are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of storage locations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When suggesting storage locations for tables, the system prioritizes suggesting highly relevant locations by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When suggesting storage locations for tables, the system analyzes the user's social media activity and suggests relevant storage locations. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned query generation unit, It estimates the user's sentiment and adjusts how queries are created based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned query generation unit, When creating a query, the system selects the optimal query creation method by referring to the user's past query creation history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned query generation unit, When creating queries, different query creation algorithms are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned query generation unit, It estimates the user's sentiment and prioritizes queries based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned query generation unit, When creating queries, the system prioritizes creating highly relevant queries by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned query generation unit, When creating queries, analyze users' social media activity and generate relevant queries. The system described in Appendix 1, characterized by the features described herein. (Note 39) The optimization unit, It estimates user sentiment and adjusts query optimization based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The optimization unit, When optimizing queries, the system selects the optimal optimization method by referring to the user's past query optimization history. The system described in Appendix 1, characterized by the features described herein. (Note 41) The optimization unit, When optimizing queries, different optimization algorithms are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 42) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The optimization unit, When optimizing queries, the system prioritizes optimizing highly relevant queries by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 44) The optimization unit, When optimizing queries, we analyze users' social media activity and optimize relevant queries. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A visualization unit that visualizes the data, A unification unit that standardizes the naming conventions for column names based on the data visualized by the visualization unit, A proposal unit that proposes the optimal storage location for the table based on the column names standardized by the aforementioned unification unit, A query creation unit that creates a query based on the storage location proposed by the proposal unit, The system includes an optimization unit that analyzes the queries created by the query generation unit and proposes optimizations. A system characterized by the following features.

2. The aforementioned visualization unit, Visualize data structures The system according to feature 1.

3. The unification unit is, Standardize the naming conventions for column names. The system according to feature 1.

4. The aforementioned proposal section is, We propose the optimal storage location for the tables. The system according to feature 1.

5. The aforementioned query generation unit, Helps create queries The system according to feature 1.

6. The optimization unit, It analyzes the execution query and suggests optimizations. The system according to feature 1.

7. The aforementioned proposal section is, Investigate the impact of specification changes and identify necessary countermeasures. The system according to feature 1.

8. The aforementioned visualization unit, We classify confidential and public data and monitor whether the scope of disclosure is appropriate. The system according to feature 1.

Images