System and method for generating meta data and optimizing databases using questioner information

Abstract

The meta generation and database optimization device using questioner information disclosed herein includes a processor and a memory storing at least one instruction executed by the processor, the at least one instruction being configured to cause the processor to perform a natural language query transmission stage in which a user inputs a question and the result is transmitted to a transformation matrix via a result platform; a question processing stage in which the transformation matrix analyzes the natural language question via a large-scale language model and generates a meta natural language question by referring to related data from an embedded vector store; and a meta information generation stage in which a database query is generated based on the meta information generated by the optimal database generation unit to derive optimized results.

Description

[Technical Field]

[0001] The present disclosure relates to the fields of artificial intelligence technology and database query processing technology, and in particular provides a system and method for realizing personalized data access by optimizing a metadata structure based on a user's profile information and behavioral history and automatically generating efficient database queries via an optimal database generation unit. [Background technology]

[0002] Modern companies accumulate huge amounts of data, and effectively managing and analyzing this data has emerged as a core element of corporate competitiveness. Many database systems provide the same metadata structure to all users.

[0003] Patent Document 1 (Korean Patent Publication No. 10-2023-0075864) discloses a method for optimizing queries. While such conventional query optimization methods are useful for reducing the complexity of system management, they are inefficient in accessing data required for each user's work from various departments and positions. Furthermore, because conventional query optimization methods do not take into account a user's personal profile information or behavioral history, users must set complex search criteria to find the data they need or manually select the desired information from numerous pieces of data. Furthermore, conventional query optimization methods require users to have knowledge of the SQL language when directly accessing a database to query data. This burdens non-experts and beginners with the need to create complex SQL queries, resulting in low data usability.

[0004] Recently, there has been an increasing number of attempts to utilize artificial intelligence and machine learning technologies to advance database management and data analysis. Natural language processing (NLP)-based question-answering systems have been developed that use these technologies to understand questions entered by users in natural language and provide relevant data. However, these systems often fail to adequately reflect user intent and domain information. To address this issue, recommendation systems have been introduced that analyze user behavior patterns to recommend necessary information, but these systems have primarily focused on content consumption. Furthermore, while automated tuning tools exist to improve database query performance, they are still far removed from providing customized data to users.

[0005] As such, traditional query optimization methods are designed to provide the same metadata structure to all users and to provide data query systems that do not reflect the individual needs and preferences of users, resulting in limited data accessibility and usability. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Korean Patent Publication No. 10-2023-0075864 Summary of the Invention [Problem to be solved by the invention]

[0007] The present disclosure has been devised in response to the above-mentioned background art, and can provide a system and method for automatically generating efficient database queries by adjusting the exposure order of metadata items based on user profile information and behavioral history to generate an optimized metadata structure so that various users within a company can efficiently access and analyze massive amounts of data, and transmitting commands to an optimal database generation unit based on the generated metadata structure.

[0008] The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0009] A method performed by a computing device according to some embodiments for solving the above-mentioned problem, wherein a meta generation and database optimization device using questioner information according to one aspect of the present disclosure for achieving the above-mentioned object includes a processor; and a memory storing at least one instruction executed via the processor, wherein the at least one instruction may be configured to perform a natural language query transmission stage in which a user inputs a question and the transmission is transmitted to a transformation matrix via a result platform; a question processing stage in which the transformation matrix analyzes the natural language question via a large-scale language model and generates a meta natural language question by referencing related data from an embedded vector store; and a meta information generation stage in which a database query is generated based on the meta information generated in the optimal database generation unit to derive optimized results.

[0010] A meta generation and database optimization system using questioner information according to another aspect of the present disclosure may include a questioner information collection module that understands a user's role through user information and helps classify data based on that understanding; a metadata generation module that logically structures various objects in a corporate database and classifies them into dimensions, attributes, and measures; a metadata optimization module that optimizes the exposure order of metadata items based on the collected questioner information and prioritizes providing the most relevant business data to the user; and a database generation module based on an optimal database generation unit that generates a database query based on the optimized metadata and executes the query in the database.

[0011] The questioner information collection module may include a profile information collector responsible for collecting basic profile information such as user ID, department, and job title; a behavioral history analyzer that collects and analyzes the user's previous search history, click patterns, frequently used keywords, and areas of interest; and a data store including a database or data lake that stores and manages the collected profile information and behavioral history.

[0012] The metadata generation module may include a database object relationship mapping device that specifies and maps relationships between objects in a company's database; a metadata structuring engine that creates a folder-based hierarchical classification system and generates metadata composed of dimensions, attributes, and measures; and a metadata store that saves and manages the generated metadata structure.

[0013] The metadata optimization module may include a prioritization engine that dynamically determines the exposure order of metadata items based on collected user profiles and behavioral history; a real-time update monitor that monitors user behavior changes in real time and updates the metadata structure; and a personalized metadata provider that provides an optimized metadata structure to the user.

[0014] The database generation module based on the optimal database generation unit may include a metadata mapping engine that maps metadata items selected by the user to actual tables and fields of the database; a database query generator that generates optimized SQL queries based on the mapped information; a query optimization engine that optimizes database queries by applying index utilization, join optimization, and efficient placement techniques for filter conditions; and a result providing module that provides the results of executed queries to the user in the form of visualization tools or reports.

[0015] A method for meta generation and database optimization using questioner information according to yet another aspect of the present disclosure may include a step in which a user inputs a question in natural language to a system; a step in which a conversion matrix converts the natural language into metalanguage and transmits the question content; a step in which a natural language processing module receives the metalanguage question, analyzes the natural language question, and presents a metalanguage response accordingly; a step in which an optimal database generation unit receives the metalanguage command and generates an optimal database query, prepares a question result response from the resulting data set (DATA SET), and transmits the result response to the user.

[0016] A method for meta generation and database optimization using questioner information according to yet another aspect of the present disclosure is a method for meta generation and database optimization using questioner information, performed by a computing device including at least one processor, and may include: a natural language query transmission stage in which a user inputs a question and transmits it to a transformation matrix via a result platform; a question processing stage in which the transformation matrix analyzes the natural language question via a large-scale language model and generates a meta natural language question by referring to related data from an embedded vector store; and a meta information generation stage in which an optimal database generation unit generates a database query based on the meta information generated and derives optimized results.

[0017] The method may further include a result providing step in which the final result based on the metadata is transmitted to the user in real time in a streaming manner.

[0018] According to yet another aspect of the present disclosure, a computer program stored on a computer-readable storage medium is a computer program stored on a computer-readable storage medium, which, when executed by one or more processors, performs a meta generation and database optimization method using questioner information, and the method may include: a natural language query transmission step in which a user inputs a question and transmits it to a transformation matrix via a result platform; a question processing step in which the transformation matrix analyzes the natural language question via a large-scale language model and generates a meta natural language question by referring to related data from an embedded vector store; and a meta information generation step in which a database query is generated based on the meta information generated in the optimal database generation unit to derive optimized results. [Effects of the Invention]

[0019] The present disclosure has been devised in response to the above-mentioned background art, and can provide a system and method for automatically generating efficient database queries by dynamically adjusting the exposure order of metadata items based on a user's profile information and behavioral history to generate an optimized metadata structure, and transmitting commands to an optimal database generator based on this structure.

[0020] By providing a metadata structure optimized for each user, users can quickly and accurately access the data they need, which reduces data search time and improves work efficiency, thereby increasing user productivity.

[0021] By analyzing user profiles and behavioral history, we provide personalized data services by prioritizing the exposure of the most relevant metadata items to each user, which contributes to improving user satisfaction and maximizing data utilization.

[0022] Based on the optimized metadata structure, commands are sent to the optimal database generator to automatically generate efficient database queries, improving database response speed. This enables efficient use of system resources, reduces server load, and cuts operating costs.

[0023] Further scope of applicability of the present disclosure will become apparent from the following detailed description. However, it should be understood that the detailed description and specific embodiments, such as preferred embodiments of the present invention, are given by way of example only, since various changes and modifications within the spirit and scope of the invention will be apparent to those skilled in the art. [Brief explanation of the drawings]

[0024] Various aspects are described with reference to the drawings, wherein like reference numerals are used to generally refer to like components. In the following embodiments, for purposes of explanation, numerous specific details are presented to provide a general understanding of one or more aspects. However, it will be apparent that such aspects may be practiced without such specific details. In other instances, well-known structures and devices are shown in block diagram form to facilitate describing one or more aspects. [Figure 1] FIG. 1 is a block diagram illustrating a computing device according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is a block diagram illustrating the configuration of a system for generating meta data and optimizing a database using questioner information according to an embodiment of the present disclosure. [Figure 3] FIG. 3 is a configuration diagram of a meta generation and database optimization system using questioner information according to the present disclosure. [Figure 4] FIG. 4 is a schematic diagram illustrating an example of the operation of the questioner information-based meta generation and database optimization system of the present disclosure. [Figure 5] FIG. 5 is a flowchart showing information processing of the meta generation and database optimization system using questioner information of the present disclosure. [Figure 6] FIG. 6 is a flowchart illustrating the meta generation and database optimization method using questioner information of the present disclosure. [Figure 7] FIG. 7 is a general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION OF THE INVENTION

[0025] Various embodiments are described below with reference to the drawings. Various descriptions are provided herein to facilitate understanding of the present disclosure. However, these embodiments can undoubtedly be practiced without these specific descriptions.

[0026] As used herein, terms such as "component," "module," and "system" refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or the execution of software. For example, a component may be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and / or thread of execution. A component may be localized within one computer. A component may also be distributed across two or more computers. Such components may also execute from various computer-readable media having various data structures stored therein. Components may communicate, for example, through local and / or remote processing, by signals comprising one or more data packets (e.g., data from one component interacting with other components in a local system, a distributed system, or data transmitted over a network such as the Internet to other systems).

[0027] The suffixes "module" and "section" used in the following description are given or mixed together solely for the convenience of writing the specification, and do not have any meanings or roles that are distinct from each other.

[0028] Also, as used herein, the terms "information" and "data" can often be used interchangeably.

[0029] When a component is said to be "coupled" or "connected" to another component, it should be understood that it may be directly coupled or connected to the other component, but that there may be other components in between. On the other hand, when a component is said to be "directly coupled" or "directly connected" to another component, it should be understood that there are no other components in between.

[0030] Additionally, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X utilizes A or B" is intended to mean one of the natural inclusive permutations. That is, if X utilizes A, or X utilizes B, or X utilizes both A and B, then "X utilizes A or B" can apply to any of these. Additionally, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the associated listed items.

[0031] Additionally, the predicate "comprises" and / or modifier "comprises" should be understood to mean the presence of the feature and / or component in question. However, the predicate "comprises" and / or modifier "comprises" should be understood not to exclude the presence or addition of one or more other features, components, and / or groups thereof. Additionally, unless otherwise specified or unless the context makes clear that a singular form is indicated, the singular in the present specification and claims should generally be construed to mean "one or more."

[0032] Hereinafter, the same or similar components will be denoted by the same reference numerals, and repeated explanations will be omitted. Furthermore, when describing the embodiments disclosed herein, if a detailed description of related publicly known technology is deemed to obscure the gist of the embodiments disclosed herein, the detailed description will be omitted. Furthermore, the accompanying drawings are intended to facilitate understanding of the embodiments disclosed herein, and the technical ideas disclosed herein are not limited by the accompanying drawings.

[0033] The term "at least one of A or B" should be interpreted as meaning "including only A," "including only B," or "a combination of A and B."

[0034] Those skilled in the art should further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logic, and algorithm steps described in accordance with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the various illustrative components, blocks, configurations, means, logic, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints of the overall system. Skilled artisans can implement the described functionality in various ways for each particular application. However, such implementation decisions should not be interpreted as departing from the scope of the present disclosure.

[0035] The description of the embodiments set forth herein is provided to enable one of ordinary skill in the art to make and practice the invention. Various modifications to these embodiments will be apparent to those of ordinary skill in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited by the embodiments set forth herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0036] Hereinafter, a system and method for generating meta data and optimizing a database using questioner information in the present disclosure will be described with reference to FIGS.

[0037] FIG. 1 is a block diagram illustrating a computing device according to an embodiment of the present disclosure.

[0038] 1, the configuration of computing device 100 shown in FIG. 1 is merely a simplified example. For example, computing device 100 may include other components for implementing the computing environment of computing device 100, and only some of the disclosed components may constitute computing device 100.

[0039] Computing device 100 may include a processor 110 , a memory 130 , and a network unit 150 .

[0040] According to an embodiment of the present disclosure, the processor 110 may generally include any type of device capable of processing data and operations of the computing device 100. For example, the processor 110 may refer to a data processing device embedded in hardware having circuits physically structured to perform functions expressed by code or instructions contained in a program. Examples of such data processing devices embedded in hardware include a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc., but the scope of the present invention is not limited thereto.

[0041] The processor 110 may be configured with one or more cores and may include a central processing unit (CPU) of a computing device, and may further include a processor for data analysis and deep learning, such as a general purpose graphics processing unit (GPGPU) or a tensor processing unit (TPU).

[0042] According to one embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

[0043] According to one embodiment of the present disclosure, the memory 130 may include at least one type of storage medium selected from the group consisting of flash memory, hard disk, micro multimedia card, card-type memory (e.g., SD or XD memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk. The computing device 100 may also operate in conjunction with web storage that performs storage functions for the memory 130 over the Internet. The foregoing description of memory is merely exemplary, and the present disclosure is not limited thereto.

[0044] The network unit 150 according to an embodiment of the present disclosure can use various wired communication systems such as a Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), Very High Speed ​​DSL (VDSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and a Local Area Network (LAN).

[0045] In addition, the network unit 150 presented in this specification can use various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA) and other systems.

[0046] In the present disclosure, the network unit 150 may be configured regardless of the communication mode, such as wired or wireless, and may be configured as various communication networks such as a short-range communication network (LAN: Local Area Network), a personal communication network (PAN: Personal Area Network), a wide area communication network (WAN: Wide Area Network), etc. In addition, the network may be the well-known World Wide Web (WWW), or may use a wireless transmission technology used for short-range communication, such as infrared data association (IrDA) or Bluetooth (registered trademark).

[0047] The techniques described herein may be used in the networks mentioned above as well as other networks.

[0048] In this specification, a database may be controlled by at least one relational database management system (RDBMS) such as ORACLE, PostgreSQL, MySQL, MS-SQL, or MS-SQL Server. The database may be one in which data is input and output using database statements.

[0049] The application of the present disclosure may include a separate syntax execution module. When the database statement inputs or outputs data to or from the database management system (RDBMS), the syntax execution module may generate a customized statement that conforms to the syntax of the database type. For example, through the present disclosure, a user can conveniently generate, check, and control statements through abstracted mathematical expressions provided in a user interface (UI) provided by the application, regardless of the type of database.

[0050] The application may also be associated with a database (SQL) statement, where the statement performs one or more of create, read, update, and delete (CRUD). Here, the database (SQL) statement may be a signal associated with one of commands, insert, update, or delete, and the signal may be input or displayed as the name or abbreviation of the database (SQL) statement in a spreadsheet cell. For example, the display may be as shown in Table 1 below.

[0051] [Table 1] The user input information may also be used to set columns related to syntax generation or to specify record conditions. For example, SQL syntax can be executed even when the column order or column names between the original data and the output data are changed. The user input information may also display annotations for the output data.

[0052] FIG. 2 is a block diagram of the questioner-informed meta generation and database optimization system of the present disclosure.

[0053] Referring to FIG. 2, the meta generation and database optimization system based on questioner information of the present disclosure may be composed of a questioner information collection module 10, a metadata generation module 20, a metadata optimization module 30, and an optimal database generation unit (i-META)-based database generation module 40.

[0054] The questioner information collection module 10 understands a user's role through user information and helps classify data based on that information. The questioner information collection module 10 collects basic profile information, such as the user's ID, department, and job title. This information is used to understand the user's position and role within the organization and predict the type of data they will need. It also analyzes the user's previous search history, click patterns, frequently used keywords, and areas of interest to understand the user's data usage patterns and preferences. This allows the system to build a deep understanding of each user and lay the foundation for providing personalized data services. For example, if a user in the finance department frequently inquires about data on profit and loss analysis, the system learns from this and prioritizes exposing the metadata items preferred by the user to increase accessibility.

[0055] The metadata generation module 20 logically structures various objects (tables, views, stored procedures) in a company database and classifies them into dimensions, attributes, and measures. The metadata generation module 20 clearly specifies the relationships between various objects (tables, views, stored procedures, etc.) in a company database (DB) and logically structures them. It introduces a folder-based hierarchical classification system and generates classification items consisting of dimensions, attributes, and measures.

[0056] A dimension is a standard element used to analyze data, such as time, region, product, or customer, and is used to classify data.

[0057] Attributes are used to define detailed characteristics of a dimension, allowing for more detailed categorization of data. For example, attributes for a "product" dimension might include product category, brand, model, etc.

[0058] Measures are the numerical data that you actually want to measure, such as sales amount, sales volume, and profit margin.

[0059] This structure clearly defines the meaning and relationships of data, helping users to understand and explore data more intuitively. It also facilitates data exploration through a hierarchical structure, helping users to quickly find the information they need.

[0060] The metadata optimization module 30 is a module that optimizes the exposure order of metadata items based on the collected questioner information and prioritizes providing the most relevant business data to users. The metadata optimization module 30 dynamically adjusts the exposure order of metadata classification items based on the collected questioner information and generates an optimized metadata structure. This is to prioritize providing the most relevant business data to each user.

[0061] The metadata optimization module 30 adjusts the exposure order of metadata items in the priority setting items for each user, taking into consideration the user's department, position, area of ​​interest, etc. For example, for a user in the sales department, dimensions and measures related to sales performance are placed at the top to improve accessibility.

[0062] The metadata optimization module 30 analyzes the user's previous data inquiry history and interaction patterns in the behavior history-based optimization items, and arranges frequently used metadata items at the top.

[0063] The metadata optimization module 30 monitors changes in user behavior in real time in real-time update items and continuously updates the metadata exposure order, thereby flexibly responding to changing user demands.

[0064] The database generation module 40 based on the optimized database generation unit (i-META) is a module in which the optimized database generation unit (i-META) generates an SQL query based on the optimized metadata and executes it on the database.

[0065] The optimal database generation module (i-META) based database generation module 40 transmits commands to the optimal database generation module (i-META) based on the optimized metadata structure, and automatically generates efficient SQL queries.

[0066] The database generation module 40 based on the optimal database generation unit (i-META) accurately maps the metadata items selected by the user to the actual tables and fields of the database in the metadata mapping items.

[0067] The database generation module 40, based on the optimal database generation unit (i-META), generates optimized SQL queries based on the mapped information in the SQL query generation item. In this case, it applies various query optimization techniques such as index utilization, join optimization, and efficient placement of filter conditions to improve data query speed.

[0068] The database generation module 40 based on the optimal database generation unit (i-META) executes the generated SQL query against the database in the query execution and result provision items, and provides the results to the user in the form of a visualization tool or report.

[0069] For example, when a marketing department user wants to analyze customer responses by campaign for the upcoming quarter, the system uses the user's profile and optimized metadata structure to present related metadata items and generates an efficient SQL query based on the selected items to provide the results.

[0070] The disclosed meta generation and database optimization system based on questioner information generates an optimized metadata structure by adjusting the exposure order of metadata items based on the profile information and behavioral history of the user (questioner) so that various users within a company can efficiently access and analyze vast amounts of data, and can generate an efficient database (SQL) based on the generated metadata structure by issuing commands to the optimal database generation unit (i-META).

[0071] Therefore, the disclosed meta generation and database optimization system using questioner information overcomes the limitations of existing data query systems, enables user-customized data access, and improves business efficiency and data usability.

[0072] FIG. 3 is a configuration diagram of a meta generation and database optimization system using questioner information according to the present disclosure.

[0073] Referring to FIG. 3 , a questioner-informed meta generation and database optimization system according to one embodiment of the present disclosure may be a data service system based on natural language processing (NLP) technology that can utilize a metalanguage to process natural language questions and generate and optimize database (SQL) to derive appropriate results from the database.

[0074] In one embodiment, the presently disclosed system for generating meta data based on user information and optimizing databases can convert natural language queries submitted by users into a metalanguage for processing, which can analyze the user queries, automatically generate and execute appropriate SQL queries, and provide the resulting data to the users in natural language. This system may also include morphological analysis and similar question management functions for processing natural language queries.

[0075] The disclosed meta generation and database optimization system using questioner information can be composed of a user (USER), a transformation matrix (G-MATRIX), a natural language processing module (NLP), and an optimal database generation unit (i-META).

[0076] The user enters a question into the system in natural language.

[0077] The conversion matrix (G-MATRIX) converts natural language into metalanguage and transmits the query content.

[0078] The natural language processing module (NLP) receives the metalinguistic question, analyzes the natural language question, and provides a metalinguistic response accordingly.

[0079] The optimal database generation unit (i-META) receives the metalanguage command, generates an optimal SQL query, prepares a query result response from the resulting data SET result, and sends the result response to the user (USER).

[0080] In the disclosed system for generating meta data based on questioner information and optimizing databases, when a user inputs a question in natural language, the system extracts main nouns and verbs through morphological analysis and then processes synonyms. The extracted information is converted into a metalanguage, and an SQL query is automatically generated based on the extracted information. The generated SQL query is executed in a database to obtain results, which are then converted into natural language via the metalanguage and provided to the user. The metalanguage plays an important role in the processes of sending questions, commands, and receiving results, and the user ultimately receives the results processed in natural language.

[0081] For example, when a user inputs a natural language query such as "Show me the current sales status of the sales department this year," the system can convert this into meta information such as "x = this year," "y = sales department," and "sales = sales amount." An SQL query can be generated based on this information, and the sales status desired by the user can ultimately be provided.

[0082] The presently disclosed meta generation and database optimization system based on questioner information may include a similar question management function, and can detect questions similar to previously used queries and process data more efficiently based on the detected queries. It also utilizes database (SQL) optimization and data caching technologies to improve query performance and shorten system response time. Furthermore, meta-language-based query processing allows users to easily obtain desired information without having to create database (SQL) queries themselves. Furthermore, processing database queries based on natural language provides a system that allows users to easily query data without having to create SQL queries, thereby improving data accessibility and making it easy for non-experts to use.

[0083] FIG. 4 is a schematic diagram illustrating an example of the operation of the questioner information-based meta generation and database optimization system of the present disclosure.

[0084] 4, the presently disclosed meta generation and database optimization system based on questioner information analyzes user questions expressed in natural language, generates SQL queries based on the queries, queries data from a database, and provides the results in a form that can be easily understood by the user. This system is primarily used for data queries such as sales management in a specific business division, such as the electric power division of a company.

[0085] In one embodiment, the questioner-informed meta generation and database optimization system of the present disclosure may include the following components:

[0086] The user asks in natural language about the current sales status of a particular business unit, for example, "How much did we sell this year?" or "What are the sales figures for the Electrical Division in 2023?"

[0087] The transformation matrix (G-MATRIX) is a module that analyzes the user's natural language questions and generates SQL queries. For example, it processes a query such as "Sales of the Electrical Division of S Company from January 1, 2023 to today."

[0088] The optimal database generator (i-META) converts natural language queries into a metalanguage and optimizes the database (SQL) based on this metalanguage.

[0089] The natural language processing module (NLP) analyzes the user's natural language question, extracts the necessary data, and converts it into a metalanguage.

[0090] In one embodiment, the questioner-informed meta generation and database optimization system of the present disclosure can operate as follows.

[0091] When a user types a question in natural language, such as "What were the sales figures for the Electrical Division in 2023?", the NLP module analyzes the question and extracts important time ranges and departments.

[0092] The transformation matrix (G-MATRIX) module generates a specific SQL query based on the analyzed data: "Sales of S Company's Electrical Division from January 1, 2023 to today."

[0093] The generated SQL query is transmitted to the database via the optimal database generation unit (i-META) and executed.

[0094] The execution results are organized in a data format such as Table A and provided to the user in the form of Template B.

[0095] In one embodiment, the disclosed questioner-informed meta generation and database optimization system can use natural language processing (NLP) algorithms to efficiently manage sales data for a specific business division, such as a company's electrical division, and provide data services that convert users' natural language questions into database (SQL) queries and derive relevant information from the database.

[0096] In one embodiment, the disclosed questioner-informed meta generation and database optimization system can provide an automated system that allows users to request data in natural language and then analyzes and converts it into precise SQL queries.

[0097] In one embodiment, the questioner-informed meta generation and database optimization system of the present disclosure may include a module optimized to generate SQL queries and query data for a specific department or time period.

[0098] In one embodiment, the result data of the questioner-informed meta generation and database optimization system of the present disclosure can be provided in a user-customized format, and visual reports can also be generated via templates.

[0099] In one embodiment, the questioner-informed meta generation and database optimization system of the present disclosure can operate as follows.

[0100] When a user inputs a natural language question such as "Show me the sales of the electric division this year," the system converts the question into "Sales of S Company's electric division from January 1, 2023 to today," and then executes an SQL query to derive the results. The results are provided in the form of Table A and output to the user as visualized data using Template B.

[0101] In one embodiment, the disclosed meta generation and database optimization system based on query information provides a convenient system that allows users to query necessary data through natural language queries without having to understand complex query languages ​​such as SQL, allowing non-experts to easily access data and efficiently create sales management and reports.

[0102] FIG. 5 is a flowchart showing information processing of the meta generation and database optimization system using questioner information of the present disclosure.

[0103] 5, a questioner-informed meta generation and database optimization system according to an embodiment of the present disclosure may be a data service system that combines natural language processing (NLP) technology and large-scale language models (LLMs) to analyze a user's natural language question and provide an optimal response based on metadata. The questioner-informed meta generation and database optimization system of the present disclosure can then utilize the learned metadata to generate SQL queries and provide results in real time.

[0104] In one embodiment, the disclosed system and method for questioner-informed meta generation and database optimization may be a system that receives a user's natural language question, learns metadata through a large-scale language model, and automatically generates SQL queries based on the learned metadata to query data from a database, where the meta information is managed in an embedded vector store and the system provides responses in real time through continuous query processing and caching.

[0105] In one embodiment, the presently disclosed questioner-informed meta generation and database optimization system may be composed of a user, a result platform (AUD7 Platform), a transformation matrix (G-MATRIX), an optimal database generator (i-META), and an LLM (large-scale language model).

[0106] A user inputs a natural language question, such as "What are the sales figures for the Electric Power Division in 2023?"

[0107] The result platform (AUD7 Platform) transmits the user's natural language query to the transformation matrix (G-MATRIX) and then returns the results to the user.

[0108] The transformation matrix (G-MATRIX) processes the user's natural language query to generate metadata and converts the natural language query into an embedded vector form using a large-scale language model (LLM).

[0109] The large-scale language model (LLM) converts users' natural language questions into meta-information, transmits the metadata to the optimal database generator (i-META), and the optimal database generator (i-META) can use the learned data in the process of generating and optimizing SQL queries.

[0110] The optimal database generation unit (i-META) constructs templates based on the metadata generated by the transformation matrix (G-MATRIX) and optimizes SQL queries.

[0111] FIG. 6 is a flowchart illustrating the meta generation and database optimization method using questioner information of the present disclosure.

[0112] Referring to FIG. 6, the meta generation and database optimization method using questioner information of the present disclosure may include a natural language question transmission step S1000, a question processing step S2000, a meta information generation step S3000, and a result provision step S4000.

[0113] In the natural language query transmission step S1000, when a user inputs a query, the query is transmitted to a transformation matrix (G-MATRIX) via the AUD7 platform.

[0114] In the query processing step S2000, a transformation matrix (G-MATRIX) analyzes the natural language query through a large-scale language model and generates a meta-natural language query by referencing relevant data from an embedded vector store.

[0115] The meta information generation step S3000 generates an SQL query based on the meta information generated by the optimal database generation unit (i-META) and derives an optimized result.

[0116] In the result providing step S4000, the final result based on the metadata is transmitted to the user in real time in a streaming manner.

[0117] In one embodiment, the disclosed method for generating meta data and optimizing a database based on questioner information involves a user entering a question such as, "What are the sales figures for the electric power division in 2023?" The system analyzes the question using an embedded vector store and generates meta data. The transformation matrix (G-MATRIX) executes an SQL query based on the generated meta data, and the optimal database generator (i-META) provides an optimized template to the user. The results are returned in real time via a streaming method.

[0118] The disclosed method for generating meta data using questioner information and optimizing a database stores meta data learned through a large-scale language model in a vector store, and can provide accurate answers to questions based on this data.

[0119] The disclosed method for generating meta data and optimizing databases based on questioner information can utilize cached data associated with a question to provide a quick response and can inspect the cached data in real time to ensure accuracy.

[0120] The disclosed method for generating meta data and optimizing a database based on questioner information can efficiently process data when a user continuously inputs questions, taking into account the relevance of previous questions. SQL queries can be automatically generated based on the meta information, optimized, and executed in a database.

[0121] The disclosed method of generating meta data and optimizing databases based on questioner information utilizes large-scale language models and metadata to process natural language questions and provide accurate data in real time, thereby significantly shortening the data search and analysis process and maximizing efficiency through caching and continuous query processing functions.

[0122] FIG. 7 is a general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0123] Referring to FIG. 7, the computer program, when executed by one or more processors, can perform actions for user authorization.

[0124] Also disclosed in accordance with one embodiment of the present disclosure is a computer-readable medium having a data structure stored thereon.

[0125] A data structure can refer to the organization, management, and storage of data that allows efficient access and modification. A data structure can refer to the organization of data to solve a specific problem (e.g., data retrieval, data storage, data modification) in the shortest possible time. A data structure can also be defined as a physical or logical relationship between data elements designed to support a specific data processing function. Logical relationships between data elements may include user-defined interlinking relationships between data elements. Physical relationships between data elements may include actual relationships between data elements physically stored in a computer-readable storage medium (e.g., a permanent storage device). Specifically, a data structure may include a collection of data, relationships between data, and functions or instructions that can be applied to the data. Effectively designed data structures enable computing devices to perform operations while minimizing the use of computing device resources. Specifically, effectively designed data structures enable computing devices to increase the efficiency of operations, retrieval, ingestion, deletion, comparison, exchange, and search.

[0126] Data structures can be classified into linear and non-linear data structures depending on their form. A linear data structure may be a structure in which only one piece of data is linked after another. Linear data structures may include lists, stacks, queues, and deques. A list may refer to a series of data sets that are internally ordered. A list may include a linked list. A linked list may be a data structure in which data is linked in a linear fashion, with each piece of data having a pointer. In a linked list, the pointer may contain information about the connection to the next or previous piece of data. A linked list may be expressed as a singly linked list, a doubly linked list, or a circularly linked list depending on its form. A stack may be a data array structure in which access to data is restricted. A stack may be a linear data structure in which data can be manipulated (e.g., inserted or deleted) only at one end of the data structure. Data stored in a stack may be a last-in, first-out (LIFO) data structure. A queue is a data structure that has limited access to data and, unlike a stack, may be a first-in, first-out (FIFO) data structure. A deck may be a data structure that allows data to be processed at both ends of the data structure.

[0127] The nonlinear data structure may be a structure in which multiple pieces of data are concatenated after one piece of data. The nonlinear data structure may include a graph data structure. The graph data structure may be defined by vertices and edges, and an edge may include a line connecting two different vertices. The graph data structure may include a tree data structure. The tree data structure may be a data structure in which there is one path connecting two different vertices among the multiple vertices included in the tree. In other words, the graph data structure may be a data structure that does not form a loop.

[0128] The data structure may include data to be input to a neural network. The data structure including the data to be input to a neural network may be stored on a computer-readable medium. The data to be input to a neural network may include training data input during a neural network training process and / or input data to a neural network after training has been completed. The data to be input to a neural network may include data that has undergone pre-processing and / or data to be pre-processed. Pre-processing may include a data processing process for inputting data to a neural network. Therefore, the data structure may include data to be pre-processed and data generated by pre-processing. The above-described data structures are merely examples, and the present disclosure is not limited thereto.

[0129] The data structure may include neural network weights. In this specification, the terms "weight" and "parameter" may be used interchangeably. The data structure including the neural network weights may be stored on a computer-readable medium. The neural network may include multiple weights. The weights may be variable and may be adjusted by a user or algorithm to enable the neural network to perform a desired function. For example, if one or more input nodes are interconnected to an output node by respective links, the output node may determine a data value to be output from the output node based on the values ​​input to the input nodes connected to the output node and the weights assigned to the links corresponding to each input node. The above-described data structure is merely exemplary, and the present disclosure is not limited thereto.

[0130] While the present disclosure has been described above as generally capable of being embodied in computing devices, those skilled in the art will appreciate that the present disclosure can also be embodied in combination with computer-executable instructions and / or other program modules that can be executed on one or more computers and / or as a combination of hardware and software.

[0131] Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Those skilled in the art will also appreciate that the methods of the present disclosure can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, any of which can operate in conjunction with one or more associated devices.

[0132] Moreover, the embodiments described in this disclosure may be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0133] A computer typically includes a variety of computer-readable media. Any medium accessible by a computer can be computer-readable, including volatile and non-volatile media, transitory and non-transitory media, and portable and non-portable media. By way of example, and not limitation, computer-readable media can include computer-readable storage media and computer-readable transmission media. Computer-readable storage media include volatile and non-volatile media, transitory and non-transitory media, portable and non-portable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage devices, magnetic cassettes, magnetic tape, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store information.

[0134] Computer-readable transmission media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above media should also be included within the scope of computer-readable transmission media.

[0135] An exemplary environment 1100 embodying various aspects of the present disclosure is shown including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. The system bus 1108 couples system components, including but not limited to the system memory 1106, to the processing unit 1104. The processing unit 1104 may be any of a variety of commercially available processors. Dual processors and other multi-processor architectures may also be utilized as the processing unit 1104.

[0136] The system bus 1108 may be any of several types of bus structures that may be further interconnected: a memory bus, a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 1106 includes read-only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input / output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, or EEPROM, and contains the basic routines that help transfer information between components within the computer 1102, such as during start-up. The RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

[0137] The computer 1102 also includes an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)—which may be configured for external use in a suitable chassis (not shown)—a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from and writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., for reading a CD-ROM disk 1122 or reading from and writing to other high-capacity optical media such as DVDs). The hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 may be coupled to the system bus 1108 by a hard disk drive interface 1124, a magnetic disk drive interface 1126, and an optical drive interface 1128, respectively. The interface 1124 for external drive implementations includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

[0138] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, etc. In the case of computer 1102, the drives and media accommodate storing any data in a suitable digital format. While the foregoing description of computer-readable media refers to hard disk drives, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will recognize that other types of computer-readable media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc., may also be used in the exemplary operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

[0139] A number of program modules, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136, may be stored on the drives and in RAM 1112. All or portions of the operating system, applications, modules, and / or data may be cached in RAM 1112. It will be apparent that the present disclosure may be embodied in various commercially available operating systems or combinations of operating systems.

[0140] A user can enter commands and information into the computer 1102 through one or more wired or wireless input devices, such as a keyboard 1138 and a pointing device, such as a mouse 1140. Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, or the like. These and other input devices are often coupled to the processing unit 1104 through an input device interface 1142 that is coupled to the system bus 1108, but may also be coupled by a variety of interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, or the like.

[0141] The term "user input" in this disclosure may refer to any form of user input related to a user request made within a user interface (or within a web page). For example, the user input may include a user input for moving a pointer object. As another example, the user input may include a user input for selecting a specific object on the user interface. For example, a user input for an object (e.g., a module, a tap, etc.) may be made by touching or clicking the object. When a user input related to a selection is received, a new object may be displayed on the user interface or web page in response to the input, or the attributes of the object may be changed and displayed.

[0142] As yet another example, the user input may include information such as language, letters, numbers, and symbols input by various input means. The user input is not limited to the above examples, and various forms of user actions are possible, such as mouse cursor control, mouse wheel scrolling, keyboard directional keys, mouse click, and touch.

[0143] A monitor 1144 or other type of display device is also coupled to the system bus 1108 via an interface, such as a video adapter 1146. In addition to the monitor 1144, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, and the like. For example, the monitor 1144 or other type of display device may include at least one of a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), a flexible display, a 3D display, and an e-ink display. The display also outputs (displays) data processed by the processor 110.

[0144] The computer 1102 can operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 1148, via wired and / or wireless communications. The remote computer 1148 may be a workstation, computing device computer, router, personal computer, handheld computer, microprocessor-based entertainment appliance, peer device, or other conventional network node, and typically includes many or all of the components described for the computer 1102, although for simplicity, only a memory storage device 1150 is shown. The logical connections shown include wired or wireless connections to a local area network (LAN) 1152 and / or larger networks, e.g., a wide area network (WAN) 1154. Such LAN and WAN networking environments are commonplace in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may be connected to a worldwide computer network, e.g., the Internet.

[0145] When used in a LAN networking environment, the computer 1102 is coupled to the local network 1152 via a wired and / or wireless communication network interface or adapter 1156. The adapter 1156 can facilitate wired or wireless communication to the LAN 1152, which may also include a wireless access point installed for communicating with the wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158 or other means of establishing communications over the WAN 1154, such as coupled to a communications computing device on the WAN 1154 or via the Internet. The modem 1158, which may be internal or external and a wired or wireless device, is coupled to the system bus 1108 via the serial port interface 1142. In a networked environment, program modules described for the computer 1102, or portions thereof, may be stored in the remote memory / storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.

[0146] The computer 1102 operates to communicate with any wireless device or entity that is located and operating in wireless communication, such as a printer, a scanner, a desktop and / or handheld computer, a portable data assistant (PDA), a communication satellite, any equipment or location associated with a radio-detectable tag, and a telephone. This includes at least Wi-Fi® and Bluetooth® wireless technologies. Thus, communication may be in a predefined structure, such as a traditional network, or simply ad hoc communication between at least two devices.

[0147] Wi-Fi® (Wireless Fidelity) enables connections to the Internet and other networks without wires. Wi-Fi® is a wireless technology similar to cell phones, allowing such devices, e.g., computers, to send and receive data indoors or outdoors, i.e., from anywhere within the coverage area of ​​a base station. Wi-Fi® networks use IEEE 802.11 (a, b, g, etc.) radio technologies to provide secure, reliable, and high-speed wireless connections. Wi-Fi® can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi® networks can operate in the unlicensed 2.4 and 5 GHz radio bands, e.g., at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or with products that include both bands (dual band).

[0148] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips referred to in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0149] The computer 1102 may also be embodied as a user terminal. Therefore, the method according to an embodiment of the present disclosure may be applied without limitation to a terminal as hardware capable of installing software. The user terminal described in the present disclosure may include a mobile phone, a smartphone, a laptop computer, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation system, a slate PC, a tablet PC, an ultrabook, a wearable device (e.g., a smartwatch, smart glass, or a head-mounted display (HMD)), and the like. The user terminal may include, but is not limited to, a device capable of inputting and outputting data by a user, a device capable of displaying data to a user, and a device capable of wired or wireless communication. For example, the computing device 100 may be a desktop computer, a laptop computer, a tablet PC, a portable terminal, or the like.

[0150] Those skilled in the art will appreciate that the various illustrative logic blocks, modules, processors, means, circuits, and algorithm steps described in the description of the embodiments disclosed herein can be implemented with electronic hardware, various forms of program or design code (referred to herein as "software" for ease of description), or a combination of all of these. To clearly illustrate this interoperability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been generally described above by focusing on their functionality. Whether such functionality is implemented as hardware or software is determined by the particular application and design constraints imposed on the overall system. Those skilled in the art will appreciate that the described functionality can be implemented in various ways for each particular application, and such implementation decisions should not be interpreted as a departure from the scope of the present disclosure.

[0151] The various embodiments described herein may be implemented by a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" includes a computer program, carrier, or media accessible by any computer-readable device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Additionally, the various storage media described herein may include one or more devices and / or other machine-readable media for storing information.

[0152] It should be understood that the specific order or hierarchy of the process steps presented herein is an example of an exemplary approach. Based on design priorities, it should be understood that the specific order or hierarchy of the process steps can be rearranged within the scope of this disclosure. The accompanying method claims provide sample orders and elements of the various steps, but are not meant to be limited to the specific order or hierarchy presented.

[0153] The description of the embodiments set forth herein is provided to enable any person skilled in the art to use or practice the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited by the embodiments set forth herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. [Explanation of symbols]

[0154] 10 Questioner information receiving module 20 Metadata Generation Module 30 Metadata Optimization Module 40 Optimal Database Generation Unit Based Database Generation Module

Claims

1. a processor; and a memory having stored therein at least one instruction for execution by the processor; The at least one instruction may cause the processor to: a natural language question transmission stage in which a user inputs a question and the result is transmitted to a transformation matrix via a platform; A query processing stage in which the transformation matrix analyzes the natural language query through the large-scale language model and generates a meta-natural language query by referencing relevant data from the embedded vector store; and The meta generation and database optimization device using questioner information is configured to perform a meta information generation step of generating a database query based on the meta information generated by the optimal database generation unit and deriving an optimized result.

2. A questioner information collection module that understands the user's role through user information and helps classify data based on this; A metadata generation module that logically structures various objects in a corporate database and classifies them into dimensions, attributes, and measures; a metadata optimization module that optimizes the exposure order of metadata items based on the collected questioner information to prioritize providing the most relevant business data to the user; and A system for generating meta data and optimizing a database using query information, including: a database generation module based on an optimal database generation unit, in which the optimal database generation unit generates a database query based on the optimized metadata and executes the generated query on a database.

3. The questioner information collection module is A profile information collector responsible for collecting basic profile information such as user ID, department, and job title; A behavioral history analyzer that collects and analyzes a user's previous search history, click patterns, frequently used keywords, and areas of interest; and The system for generating meta data and optimizing databases using questioner information according to claim 2, further comprising: a data store including a database or data lake that stores and manages collected profile information and behavioral history.

4. The metadata generation module: a database object relational mapping device for specifying and mapping relationships between objects in a database of an enterprise; A metadata structuring engine that generates a folder-based hierarchical classification system and generates metadata composed of dimensions, attributes, and measures; and The system for generating meta data and optimizing databases using questioner information according to claim 2, further comprising: a metadata store for storing and managing the generated metadata structure.

5. The metadata optimization module: A prioritization engine that dynamically determines the exposure order of metadata items based on collected user profiles and behavioral history; A real-time update monitor that monitors user behavior changes in real time and updates the metadata structure; and 3. The system for generating meta data and optimizing a database using questioner information according to claim 2, further comprising: a personalized metadata provider that provides an optimized metadata structure to a user.

6. The database generation module based on the optimal database generation unit is A metadata mapping engine that maps user-selected metadata items to actual tables and fields in the database; a database query generator that generates optimized SQL queries based on the mapped information; A query optimization engine that applies index utilization, join optimization, and efficient placement of filter conditions to optimize database queries; and 3. The system for generating meta data and optimizing databases using query information according to claim 2, further comprising a result providing module for providing the results of the executed query to the user in the form of a visualization tool or a report.

7. A user inputs a question into the system in natural language; A stage in which the transformation matrix transforms the natural language into a metalanguage and transmits the query content; a natural language processing module receiving the metalinguistic question, analyzing the natural language question, and presenting a metalinguistic response accordingly; A method for generating meta data and optimizing a database using query information, the method comprising: an optimal database generating unit receiving a metalanguage instruction to generate an optimal database query, preparing a query result response from the resulting data set (DATA SET), and transmitting the result response to a user.

8. 1. A method of meta generation and database optimization using questioner information performed by a computing device including at least one processor, comprising: a natural language question transmission stage in which a user inputs a question and the result is transmitted to a transformation matrix via a platform; A query processing stage in which the transformation matrix analyzes the natural language query through the large-scale language model and generates a meta-natural language query by referencing relevant data from the embedded vector store; and A meta generation and database optimization method using questioner information, comprising: a meta information generation step of generating a database query based on the meta information generated by an optimal database generation unit, and deriving an optimized result.

9. The method comprises: The method for generating meta data and optimizing a database using questioner information according to claim 8, further comprising a result providing step in which a final result based on the meta data is transmitted to a user in real time in a streaming manner.

10. 1. A computer program stored on a computer-readable storage medium, the computer program, when executed by one or more processors, performing a method for meta generation and database optimization using questioner information, the method comprising: a natural language question transmission stage in which a user inputs a question and the result is transmitted to a transformation matrix via a platform; A query processing stage in which the transformation matrix analyzes the natural language query through the large-scale language model and generates a meta-natural language query by referencing relevant data from the embedded vector store; and A computer program stored in a computer-readable storage medium, the computer program comprising: a meta-information generating step of generating a database query based on the meta-information generated by the optimal database generating unit, and deriving an optimized result.

Images