Meta generation and database optimization system according to questioner information, and method thereof

Abstract

A meta generation and database optimization device according to questioner information of the present disclosure comprises: a processor; and a memory in which at least one command executed through the processor is stored, wherein the at least one command is configured to cause the processor to perform: a natural language query delivery step in which, when a user inputs a query, the query is delivered to a transformation matrix through a result platform; a query processing step in which the transformation matrix analyzes a natural language query through a large-scale language model and generates a meta natural language query by referring to relevant data in an embedded vector store; and a meta information generation step in which a database query is generated on the basis of meta information generated by an optimal database generation unit and an optimized result is derived.

Description

Meta-generation and database optimization system and method based on questioner information

[0001] The present disclosure relates to the field of artificial intelligence technology and database query processing technology, and more particularly, to a system and method for realizing personalized data access by optimizing a metadata structure based on a user's profile information and behavior history and automatically generating an efficient database query through an optimal database generation unit.

[0002] Modern businesses accumulate vast amounts of data, and effectively managing and analyzing this data is emerging as a key element of corporate competitiveness. Most database systems provide a uniform metadata structure to all users.

[0003] Patent Document 1 (Korean Patent Publication No. 10-2023-0075864) discloses a method for optimizing queries. While this conventional query optimization method helps reduce the complexity of system management, it is inefficient for users in various departments and positions to retrieve the data they need for their respective tasks. Furthermore, since the conventional query optimization method does not consider individual user profile information or behavioral history, users must set complex search conditions or manually select desired information from a large amount of data to find the data they need. Furthermore, the conventional query optimization method requires users to have knowledge of the SQL language when directly accessing the database to retrieve data. This burdens non-experts or beginners with no experience in writing complex SQL queries, resulting in low data usability.

[0004] Meanwhile, there has been a recent increase in attempts to utilize artificial intelligence and machine learning technologies to enhance database management and data analysis. Natural language processing (NLP)-based question-and-answer systems are being developed that utilize these technologies to understand users' natural language queries and provide relevant data. However, these systems often fail to adequately reflect user intent and domain information. Recommendation systems that analyze user behavior patterns and recommend necessary information have been introduced, but these systems have primarily focused on content consumption. Furthermore, while automated tuning tools exist to improve database query performance, they fall short of providing personalized data to users.

[0005] In this way, the conventional query optimization method was designed as a data retrieval system that provided the same metadata structure to all users and did not reflect the needs and preferences of each user, which had limitations in data accessibility and usability.

[0006] [Prior Art Literature]

[0007] [Patent Document]

[0008] (Patent Document 1) Republic of Korea Publication No. 10-2023-0075864

[0009] The present disclosure is conceived in response to the aforementioned background technology, and provides a system and method for automatically generating an efficient database query by adjusting the exposure order of metadata items based on the user's profile information and behavior history to enable various users within a company to efficiently access and analyze massive data, and transmitting a command to an optimal database generation unit based on the generated metadata structure.

[0010] 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 description below.

[0011] In order to solve the above-described problem, a method performed by a computing device according to some embodiments of the present disclosure for achieving the above-described object includes a device for generating meta-data and optimizing a database according to questioner information, the device including: a processor; and a memory storing at least one command executed by the processor; wherein the at least one command is configured to cause the processor to perform: a natural language query transmission step in which, when a user inputs a query, the query is transmitted to a transformation matrix through a result platform; a query processing step in which the transformation matrix analyzes the natural language query through a large-scale language model and generates a meta-natural language query by referencing related data in an embedded vector store; and a meta-information generation step in which a database query is generated based on the meta-information generated by an optimal database generation unit and an optimized result is derived.

[0012] A system for generating metadata and optimizing a database based on questioner information according to another aspect of the present disclosure may include a questioner information collection module that supports understanding a user's role through user information and classifying data based on the user information; a metadata generation module that logically structures various objects within a corporate database and classifies them into dimensions, attributes, and measures; a metadata optimization module that optimizes the display order of metadata items based on the collected questioner information and provides the most relevant business data to the user first; 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 database query.

[0013] The questioner information collection module may include a profile information collector responsible for collecting basic profile information such as user ID, department, and position; a behavior 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 behavior history.

[0014] The metadata generation module may include: a database object relationship mapper that specifies and maps relationships between objects in a company's database; a metadata structuring engine that creates metadata composed of dimensions, attributes, and measures by creating a hierarchical classification system in the form of folders; and a metadata repository that stores and manages the generated metadata structure.

[0015] The metadata optimization module may include a priority decision engine that dynamically determines the display order of metadata items based on collected user profiles and behavioral histories; 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.

[0016] The database creation module based on the optimal database creation 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 an optimized SQL query based on the mapped information; a query optimization engine that optimizes the database query by applying techniques such as index utilization, join optimization, and efficient arrangement of filter conditions; and a result provision module that provides the results of the executed query to the user in the form of a visualization tool or report.

[0017] A method for generating meta-language and optimizing a database according to questioner information according to another aspect of the present disclosure may include: a step in which a user inputs a query into a system in natural language; a step in which a transformation matrix converts the natural language into a meta-language and transmits the query content; a step in which a natural language processing module receives the meta-language query, analyzes the natural language query, and presents a meta-language answer accordingly; and a step in which an optimal database generation unit receives a meta-language command, generates an optimal database query, prepares a query result answer from a data set result accordingly, and transmits the result answer to the user.

[0018] According to another aspect of the present disclosure, a method for generating meta-data and optimizing a database based on questioner information is provided, which is performed by a computing device including at least one processor, and may include a natural language query transmission step in which, when a user inputs a query, a query is transmitted to a transformation matrix through a result platform; a query processing step in which the transformation matrix analyzes the natural language query through a large-scale language model and generates a meta-natural language query by referencing related data in an embedded vector store; and a meta-information generation step in which an optimal database generation unit generates a database query based on the generated meta-information and derives an optimized result.

[0019] The above method may further include a result provision step in which the final result based on the metadata is delivered to the user in real time in a streaming manner.

[0020] According to another aspect of the present disclosure, a computer program stored in a computer-readable storage medium is provided, wherein the computer program, when executed by one or more processors, performs a method of generating meta data and optimizing a database according to queryer information, the method comprising: a natural language query transmission step in which, when a user inputs a query, the natural language query is transmitted to a transformation matrix through a result platform; a query processing step in which the transformation matrix analyzes the natural language query through a large-scale language model and generates a meta natural language query by referencing related data in an embedded vector store; and a meta information generation step in which a database query is generated based on the meta information generated by an optimal database generation unit and an optimized result is derived.

[0021] The present disclosure is conceived in response to the aforementioned background technology, and provides a system and method for generating an optimized metadata structure by dynamically adjusting the exposure order of metadata items based on a user's profile information and behavior history, and automatically generating an efficient database query by transmitting a command to an optimal database generation unit based on this structure.

[0022] By providing a metadata structure optimized for each user, users can quickly and accurately access the data they need. This reduces data retrieval times, improves work efficiency, and enhances user productivity.

[0023] By analyzing user profiles and behavioral histories, we prioritize the most relevant metadata for each user, providing personalized data services. This contributes to increased user satisfaction and maximizing data usability.

[0024] By automatically generating efficient database queries by sending commands to the optimal database generation unit based on an optimized metadata structure, database response speed is improved. This enables efficient use of system resources, reducing server load and operating costs.

[0025] Further scope of the applicability of the present disclosure will become apparent from the detailed description below. However, since various modifications and variations within the spirit and scope of the present invention will become apparent to those skilled in the art, it should be understood that the detailed description and specific examples, such as preferred embodiments of the present invention, are given by way of example only.

[0026] Various aspects are now described with reference to the drawings, wherein like reference numerals are used to refer to similar elements throughout. In the following examples, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of one or more aspects. However, it will be apparent that such aspects may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form to facilitate the description of one or more aspects.

[0027] FIG. 1 is a block diagram illustrating a computing device according to one embodiment of the present disclosure.

[0028] FIG. 2 is a block diagram illustrating the configuration of a meta-generation and database optimization system based on questioner information according to an embodiment of the present disclosure.

[0029] Figure 3 is a configuration diagram of a meta-generation and database optimization system according to questioner information of the present disclosure.

[0030] FIG. 4 is a schematic diagram illustrating an example of the operation of a meta-generation and database optimization system according to questioner information of the present disclosure.

[0031] FIG. 5 is a flowchart illustrating information processing of a meta-generation and database optimization system according to questioner information of the present disclosure.

[0032] FIG. 6 is a flowchart illustrating a method for generating meta data and optimizing a database according to questioner information of the present disclosure.

[0033] FIG. 7 illustrates a general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0034] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate understanding of the present disclosure. However, it will be apparent that these embodiments may be practiced without these specific details.

[0035] As used herein, the terms "component," "module," "system," and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or an 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 a thread of execution. A component may be localized within a single computer. A component may be distributed between two or more computers. Furthermore, these components may execute from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, for example, by signals comprising one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or data transmitted to another system via a network such as the Internet via signals).

[0036] The suffixes “module” and “part” used for components in the following description are given or used interchangeably only for the convenience of writing specifications, and do not have distinct meanings or roles in themselves.

[0037] Additionally, the terms “information” and “data” as used herein may often be used interchangeably.

[0038] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0039] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean either of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, "X employs A or B" can apply to any of these cases. Furthermore, 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 items listed.

[0040] Additionally, the terms "comprises" and / or "comprising" should be understood to imply the presence of the features and / or components. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Furthermore, unless otherwise specified or clear from the context to refer to the singular form, the singular in the specification and claims should generally be construed to mean "one or more."

[0041] Hereinafter, regardless of the drawing numbers, identical or similar components are assigned the same reference numbers, and redundant descriptions thereof are omitted. Furthermore, when describing the embodiments disclosed in this specification, if a detailed description of a related known technology is judged to obscure the gist of the embodiments disclosed in this specification, the detailed description thereof will be omitted. Furthermore, the attached drawings are only intended to facilitate easy understanding of the embodiments disclosed in this specification, and the technical ideas disclosed in this specification are not limited by the attached drawings.

[0042] And, the term “at least one of A or B” should be interpreted to mean “if it includes only A”, “if it includes only B”, or “if it is combined in the composition of A and B”.

[0043] Those skilled in the art should further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0044] The description of the disclosed embodiments is provided to enable a person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to a person skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments set forth herein. The present disclosure is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0045]

[0046] Hereinafter, FIGS. 1 to 6 describe a system and method for generating meta data and optimizing a database according to questioner information in the present disclosure.

[0047]

[0048] FIG. 1 is a block diagram illustrating a computing device according to one embodiment of the present disclosure.

[0049] Referring to FIG. 1, the configuration of the computing device (100) illustrated in FIG. 1 is merely a simplified example. For example, the computing device (100) may include other configurations for performing the computing environment of the computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0050] A computing device (100) may include a processor (110), memory (130), and network unit (150).

[0051] According to one embodiment of the present disclosure, the processor (110) may typically include all types of devices capable of processing operations and data of the computing device (100). For example, it may refer to a data processing device built into hardware that has a physically structured circuit to perform a function expressed by a code or command included in a program. Examples of such data processing devices built into hardware include processing devices such as a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated circuit (ASIC), and a field programmable gate array (FPGA), but the scope of the present invention is not limited thereto.

[0052] The above processor (110) may be composed of one or more cores and may include a central processing unit (CPU) of a computing device. In addition, it may further include a processor for data analysis and deep learning, such as a general purpose graphics processing unit (GPGPU) and a tensor processing unit (TPU).

[0053] According to one embodiment of the present disclosure, the memory (130) can store any form of information generated or determined by the processor (110) and any form of information received by the network unit (150).

[0054] According to one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may also operate in relation to web storage that performs the storage function of the memory (130) on the internet. The description of the above-described memory is merely an example, and the present disclosure is not limited thereto.

[0055] The network unit (150) according to one embodiment of the present disclosure can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0056] 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.

[0057] 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 local area network (LAN), a personal area network (PAN), and a wide area network (WAN). In addition, the network may be the well-known World Wide Web (WWW), and may also utilize a wireless transmission technology used for short-distance communication, such as infrared (IrDA: Infrared Data Association) or Bluetooth.

[0058] The techniques described in this specification can be used in other networks as well as the networks mentioned above.

[0059] In this specification, the database may be controlled by at least one relational database management system (RDBMS) among ORACLE, PostgreSQL, MyDatabase, Database(SQL) Server (MS-Database(SQL)), or Database(SQL)ite. The database may be one in which data input and output are performed by database statements.

[0060] Meanwhile, the application of the present specification may include a separate statement execution module. When the database statement performs data input / output for the database management system (RDBMS), the statement execution module may generate a customized statement that conforms to the syntax depending on the type of the database. For example, through the present disclosure, a user can conveniently generate, confirm, and control statements through abstracted formulas provided in the user interface (UI) provided by the application, regardless of the type of database.

[0061] Additionally, the application may be associated with a database (SQL) statement that performs one or more of the following operations: Create, Read, Update, Delete (CRUD). Here, the database (SQL) statement may be a signal associated with one of the commands Insert, Update, or Delete, and the signal may be a name or abbreviation of the database (SQL) statement that is entered or displayed in a cell of a spreadsheet. For example, the display may appear as shown in Table 1 below.

[0062] Code Description AllInsert, Update Includes the column in all operations. InsertOnly Includes the column only in the Insert statement. UpdateOnly Includes the column in the Update statement.

[0063]

[0064] Additionally, user input information may be used to set columns related to syntax generation or specify record conditions. For example, SQL statements can be executed even when the column order or column names between the source and output data change. Furthermore, the user input information may display comments on the output data.

[0065]

[0066] FIG. 2 is a block diagram of a meta-generation and database optimization system according to questioner information of the present disclosure.

[0067] Referring to FIG. 2, the meta data generation and database optimization system according to the questioner information of the present disclosure may be composed of a questioner information collection module (10), a meta data generation module (20), a meta data optimization module (30), and an optimal database generation unit (i-META)-based database generation module (40).

[0068] The questioner information collection module (10) is a module that understands the user's role through user information and supports data classification based on this. The questioner information collection module (10) collects basic profile information, such as the user's ID, department, and position. This information is used to determine the user's position and role within the organization and predict the required data categories. Furthermore, the system analyzes the user's previous search history, click patterns, frequently used keywords, and areas of interest to identify the user's data usage patterns and preferences. This builds a deeper understanding of each user and lays the foundation for providing personalized data services. For example, if a user in the finance department frequently views data on profit and loss analysis, the system learns this and prioritizes the user's preferred metadata items, thereby improving accessibility.

[0069] The metadata creation module (20) is a module that logically structures various objects (tables, views, stored procedures) within a corporate database and classifies them into dimensions, attributes, and measures. The metadata creation module (200) clearly specifies and logically structures the relationships between various objects (tables, views, stored procedures, etc.) within a corporate database (DB). By introducing a hierarchical classification system in the form of folders, classification items consisting of dimensions, attributes, and measures are created.

[0070] Dimension is a standard element when analyzing data, and is used to classify data, such as time, region, product, and customer.

[0071] Attributes represent the detailed characteristics of a dimension and enable more detailed data classification. For example, attributes for the "Product" dimension include product category, brand, and model.

[0072] Measure is the numerical data that you actually want to measure, such as sales, sales quantity, and profit rate.

[0073] This structure clearly defines the meaning and relationships of the data, allowing users to understand and explore it more intuitively. Furthermore, the hierarchical structure facilitates data exploration, enabling users to quickly find the information they need.

[0074] The metadata optimization module (30) optimizes the display order of metadata items based on collected questioner information, and prioritizes providing the most relevant business data to users. The metadata optimization module (30) dynamically adjusts the display order of metadata classification items based on collected questioner information to create an optimized metadata structure. This is to prioritize providing the most relevant business data to each user.

[0075] The metadata optimization module (30) adjusts the display order of metadata items in the user-specific priority setting, taking into account the user's department, position, area of ​​interest, etc. For example, a user in the sales department may place dimensions and measures related to sales performance at the top to improve accessibility.

[0076] The metadata optimization module (30) analyzes the user's previous data search history and interaction patterns in the behavior history-based optimization items and places frequently used metadata items at the top.

[0077] The metadata optimization module (30) monitors user behavior changes in real-time and continuously updates the metadata exposure order in real-time update items. This allows for flexible response to changing user needs.

[0078] The database creation module (40) based on the optimal database creation unit (i-META) is a module in which the optimal database creation unit (i-META) creates an SQL query based on optimized metadata and executes it on the database.

[0079] The database creation module (40) based on the optimal database creation unit (i-META) automatically creates an efficient SQL query by transmitting a command to the optimal database creation unit (i-META) based on the optimized metadata structure.

[0080] The database creation module (40) based on the optimal database creation unit (i-META) accurately maps the metadata items selected by the user and the actual tables and fields of the database in the metadata mapping items.

[0081] The database creation module (40) based on the optimal database creation unit (i-META) generates an optimized SQL query based on the mapped information in the SQL query creation item. At this time, various query optimization techniques such as index utilization, join optimization, and efficient arrangement of filter conditions are applied to improve data retrieval speed.

[0082] The database creation module (40) based on the optimal database creation unit (i-META) executes the generated SQL query on the database in the query execution and result provision item and provides the result to the user in the form of a visualization tool or report.

[0083] For example, if a user of the marketing department wants to analyze customer response by campaign in the last quarter, the database creation module (40) based on the optimal database creation unit (i-META) can present related metadata items by utilizing the user's profile and optimized metadata structure, and can generate an efficient SQL query based on the selected items to provide results.

[0084]

[0085] The system for generating metadata and optimizing a database according to the questioner information of the present disclosure can generate an optimized metadata structure by adjusting the exposure order of metadata items based on the profile information and behavior history of the user (questioner) so that various users within the company can efficiently access and analyze massive data, and can generate an efficient database (SQL) through a command to the optimal database generation unit (i-META) based on the generated metadata structure.

[0086] Therefore, the meta-generation and database optimization system according to the questioner information of the present disclosure can overcome the limitations of existing data query systems and realize user-tailored data access, thereby improving work efficiency and data usability.

[0087]

[0088] Figure 3 is a configuration diagram of a meta-generation and database optimization system according to questioner information of the present disclosure.

[0089] Referring to FIG. 3, a meta-generation and database optimization system based on questioner information according to one embodiment of the present disclosure may be a data service system based on natural language processing (NLP) technology that can process natural language queries using meta-language, generate and optimize a database (SQL), and derive suitable results from the database.

[0090] In one embodiment, the meta-generation and database optimization system according to the questioner information of the present disclosure may be a system that converts user queries in natural language into a meta-language and processes them. The meta-language analyzes the user queries, automatically generates and executes appropriate SQL queries, and provides the resulting data back to the user in natural language. The system may include morphological analysis and similar query management functions to process natural language queries.

[0091] The meta generation and database optimization system according to the questioner information of the present disclosure may 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).

[0092] A user (USER) enters a query into the system in natural language.

[0093] Transformation Matrix (G-MATRIX) converts natural language into meta-language and transmits the query content.

[0094] The natural language processing module (NLP) receives a metalanguage query, analyzes the natural language query, and provides a metalanguage answer accordingly.

[0095] The optimal database generation unit (i-META) receives meta language commands, generates an optimal SQL query, prepares a query result answer from the data SET result, and sends the result answer to the user (USER).

[0096] The meta-generation and database optimization system based on questioner information of the present disclosure, when a user enters a query in natural language, extracts key nouns and verbs through morphological analysis and processes them for similarity processing. The extracted information is converted into a meta-language, which is then used to automatically generate an SQL query. The generated SQL query is executed in a database to produce results, which are then converted into natural language through the meta-language and provided to the user. The meta-language plays a crucial role in the query transmission, command transmission, and result reception processes, and the user ultimately receives the results processed in natural language.

[0097] For example, the meta-generation and database optimization system according to the questioner information of the present disclosure can convert a user's natural language query, such as "Show me this year's sales department sales status," into meta-information such as "x=this year," "y=sales department," and "sales=sales amount." Based on this information, an SQL query can be generated, and ultimately, the sales status desired by the user can be provided.

[0098] The meta-generation and database optimization system according to the questioner information of the present disclosure may include a function for managing similar queries, thereby detecting queries similar to previously used queries and processing data more efficiently based on these queries. Furthermore, by utilizing database (SQL) optimization and data caching technologies, query performance is improved and system response time is shortened. Furthermore, meta-language-based query processing allows users to easily obtain desired information without having to directly write database (SQL) statements. Furthermore, by processing database queries based on natural language, a system can be provided that allows users to easily retrieve data without having to write SQL queries, thereby increasing data accessibility and making it easier for non-experts to use.

[0099]

[0100] FIG. 4 is a schematic diagram illustrating an example of the operation of a meta-generation and database optimization system according to questioner information of the present disclosure.

[0101] Referring to Figure 4, the meta-generation and database optimization system based on questioner information of the present disclosure analyzes a user's query expressed in natural language, generates an SQL query based on the query, retrieves data from a database, and provides the results in a user-understandable format. This system is primarily used for data retrieval, such as sales management, in specific business units, such as the electric power division of a company.

[0102] In one embodiment, the meta-generation and database optimization system according to the questioner information of the present disclosure may include the following components.

[0103] A user queries the sales status of a specific business division using natural language. For example, they might ask, "Please tell me how much we sold this year" or "What were the sales of the electrical business division in 2023?"

[0104] G-MATRIX is a module that analyzes users' natural language queries and generates SQL queries. For example, it processes a query for "Sales of S Corporation's Electric Power Division from January 1, 2023 to today."

[0105] The optimal database generation unit (i-META) converts natural language queries into meta-language and optimizes the database (SQL) based on this meta-language.

[0106] The natural language processing module (NLP) analyzes the user's natural language queries, extracts the necessary data, and converts it into meta-language.

[0107] In one embodiment, the meta-generation and database optimization system according to the questioner information of the present disclosure may operate as follows.

[0108] When a user enters a query in natural language, such as "What will be the sales of the electric power division in 2023?", the NLP module analyzes the query and extracts important time periods and departments.

[0109] The G-MATRIX module generates a specific SQL query, "Sales of S Corporation's Electricity Division from January 1, 2023 to today," based on the analyzed data.

[0110] The generated SQL query is passed to the database through the optimal database generation unit (i-META) and executed.

[0111] The execution results are organized in a data format such as Table A and provided to the user in the form of Template B.

[0112] In one embodiment, the meta-generation and database optimization system according to the questioner information of the present disclosure can efficiently manage sales data of a specific business division, such as the electric power division of a company, using a natural language processing (NLP) algorithm, and provide a data service that converts a user's natural language query into a database (SQL) and extracts related information from the database.

[0113] In one embodiment, the meta-generation and database optimization system according to the questioner information of the present disclosure may provide an automated system that allows a user to request data in natural language and analyzes and converts the data into an accurate SQL query.

[0114] In one embodiment, the meta-generation and database optimization system according to the questioner information of the present disclosure may include a module optimized for generating SQL queries and retrieving data for a specific department or period.

[0115] In one embodiment, the resulting data of the meta-generation and database optimization system according to the questioner information of the present disclosure may be provided in a user-customizable manner and may also generate visual reports through templates.

[0116] In one embodiment, the meta-generation and database optimization system according to the questioner information of the present disclosure may operate as follows.

[0117] When a user enters a natural language query like "Show me the sales of the Electric Power Division this year," the system translates the query into "Sales of Company S's Electric Power Division from January 1, 2023 to today," then executes an SQL query to produce the results. These results are presented in Table A format and displayed to the user as visualized data using Template B.

[0118] In one embodiment, the meta-generation and database optimization system according to the questioner information of the present disclosure provides a convenient system that allows users to retrieve necessary data through natural language queries without having to understand complex query languages ​​such as SQL, thereby enabling non-experts to easily access data and efficiently manage sales and create reports.

[0119]

[0120] FIG. 5 is a flowchart illustrating information processing of a meta-generation and database optimization system according to questioner information of the present disclosure.

[0121] Referring to FIG. 5, a system for generating metadata and optimizing a database based on questioner information according to one embodiment of the present disclosure may be a data service system that analyzes a user's natural language queries by combining natural language processing (NLP) technology and a large-scale language model (LLM) and provides an optimal response based on metadata. Thus, the system for generating metadata and optimizing a database based on questioner information according to the present disclosure can utilize learned metadata to generate SQL queries and provide results in real time.

[0122] In one embodiment, the system and method for generating metadata and optimizing a database based on query information of the present disclosure may be a system that receives a user's natural language query, learns metadata through a large-scale language model, and automatically generates SQL queries based on the metadata to retrieve data from a database. Here, the metadata is managed in an embedded vector store, and the system provides real-time responses through continuous query processing and caching functions.

[0123] In one embodiment, the meta generation and database optimization system according to the questioner information of the present disclosure may be composed of a user, a result platform (AUD7 Platform), a transformation matrix (G-MATRIX), an optimal database generation unit (i-META), and an LLM (large-scale language model).

[0124] It receives natural language queries from users. For example, it includes queries like "What were the sales of the electric power division in 2023?"

[0125] The result platform (AUD7 Platform) passes the user's natural language query to the transformation matrix (G-MATRIX) and returns the result back to the user.

[0126] Transformation Matrix (G-MATRIX) processes users' natural language queries to generate metadata and uses a large-scale language model (LLM) to transform natural language queries into embedded vector form.

[0127] A large-scale language model (LLM) converts a user's natural language query into metadata and passes the metadata to the optimal database generation unit (i-META), which then uses the learned data to generate and optimize SQL queries.

[0128] The optimal database generation unit (i-META) configures templates based on metadata generated from the transformation matrix (G-MATRIX) and optimizes SQL queries.

[0129]

[0130] FIG. 6 is a flowchart illustrating a method for generating meta data and optimizing a database according to questioner information of the present disclosure.

[0131] Referring to FIG. 6, the method for generating meta information and optimizing a database according to the questioner information of the present disclosure may include a natural language query transmission step (S1000), a query processing step (S2000), a meta information generation step (S3000), and a result provision step (S4000).

[0132] In the natural language query transmission step (S1000), when a user inputs a query, it is transmitted to the conversion matrix (G-MATRIX) through the AUD7 platform.

[0133] The query processing step (S2000) analyzes a natural language query through a large-scale language model using a transformation matrix (G-MATRIX) and generates a meta natural language query by referencing related data in an embedded vector store.

[0134] The meta information generation step (S3000) generates an SQL query based on the meta information generated in the optimal database generation unit (i-META) and derives an optimized result.

[0135] The result provision step (S4000) delivers the final result based on metadata to the user in real time through streaming.

[0136]

[0137] In one embodiment, the method of generating metadata and optimizing a database based on query information of the present disclosure includes: when a user enters a query, such as "What are the sales of the electric power division in 2023?", the system analyzes the query in an embedded vector store and generates metadata. The transformation matrix (G-MATRIX) executes an SQL query based on the generated metadata, and the optimal database generation unit (i-META) provides the user with an optimized template. This result is responded to in real time via streaming.

[0138] The method for generating meta-information and optimizing a database according to the questioner information of the present disclosure can store meta-information learned through a large-scale language model in a vector store and provide an accurate response to a query based on the meta-information.

[0139] The method for generating meta data and optimizing a database according to the questioner information of the present disclosure can provide a quick response by utilizing cached data related to a query and can ensure accuracy by checking the cached data in real time.

[0140] The method of generating metadata and optimizing a database based on query information of the present disclosure can efficiently process data when a user continuously enters queries by considering correlations with previous queries. SQL queries can be automatically generated based on metadata, optimized, and executed on a database.

[0141] The method of generating metadata and optimizing a database according to the questioner information of this disclosure can significantly shorten the data search and analysis process by processing natural language queries using a large-scale language model and metadata and providing accurate data in real time, and can maximize efficiency through a cache function and continuous query processing function.

[0142]

[0143] FIG. 7 illustrates a general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0144] Referring to FIG. 7, the computer program, when executed by one or more processors, can perform an action for user authorization.

[0145] Additionally, a computer-readable medium storing a data structure according to one embodiment of the present disclosure is disclosed.

[0146] A data structure can refer to the organization, management, and storage of data to enable 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, or data modification in the shortest possible time). A data structure can also be defined as the physical or logical relationships between data elements designed to support specific data processing functions. The logical relationships between data elements can include connections between user-defined data elements. The physical relationships between data elements can include actual relationships between data elements physically stored on a computer-readable storage medium (e.g., persistent storage). Specifically, a data structure can include a collection of data, relationships between data, and functions or commands applicable to the data. An effectively designed data structure allows a computing device to perform operations while minimizing the use of its resources. Specifically, a computing device can improve the efficiency of operations, reading, inserting, deleting, comparing, exchanging, and searching through an effectively designed data structure.

[0147] Data structures can be categorized as linear or nonlinear, depending on their form. A linear data structure can be a structure in which only one data item is linked to the next. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a series of data items with an internal order. Lists can also include linked lists. A linked list is a data structure in which data items are linked in a single line, each with a pointer. In a linked list, a pointer can contain information about the next or previous item. Linked lists can be expressed as singly linked lists, doubly linked lists, or circular linked lists, depending on their form. A stack can be a data list structure with limited data access. A stack can be a linear data structure in which data operations (e.g., insertion or deletion) can only be performed at one end of the data structure. Data stored in a stack can be a Last-in-First-out (LIFO) data structure. A queue is a data structure with limited access to data. Unlike a stack, it can be a first-in, first-out (FIFO) data structure, with later data being retrieved later. A deck can be a data structure that can process data at both ends.

[0148] A nonlinear data structure can be a structure in which multiple pieces of data are connected behind a single piece of data. Nonlinear data structures can include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. A graph data structure can include a tree data structure. A tree data structure can be a data structure in which there is only one path connecting two different vertices among multiple vertices included in the tree. In other words, it can be a data structure that does not form a loop in a graph data structure.

[0149] A data structure may include data input to a neural network. A data structure including data input to a neural network may be stored on a computer-readable medium. The data input to a neural network may include training data input during a neural network training process and / or input data input to a neural network after training has been completed. The data input to a neural network may include data that has undergone preprocessing and / or data that is the target of preprocessing. Preprocessing may include a data processing process for inputting data to a neural network. Accordingly, the data structure may include data that is the target of preprocessing and data generated by preprocessing. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0150] The data structure may include weights of a neural network. In this specification, the terms "weight" and "parameter" may be used interchangeably. Furthermore, a data structure including the weights of a neural network may be stored in a computer-readable medium. The neural network may include multiple weights. The weights may be variable and may be varied by a user or an algorithm so that the neural network can 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 the data value output from the output node based on the values ​​input to the input nodes connected to the output node and the weights set for the links corresponding to each input node. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0151] Although the present disclosure has been described above as being generally implemented by a computing device, those skilled in the art will appreciate that the present disclosure may be implemented in combination with computer-executable instructions and / or other program modules that may be executed on one or more computers and / or as a combination of hardware and software.

[0152] Generally, program modules include routines, programs, components, data structures, and the like that perform particular tasks or implement particular abstract data types. Furthermore, those skilled in the art will appreciate that the methods of the present disclosure can be implemented with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively connected to one or more associated devices.

[0153] The described embodiments of the present disclosure can also be implemented in a distributed computing environment, where remote processing devices are connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0154] Computers typically include a variety of computer-readable media. Computer-readable media can be any media that can be accessed by a computer, and includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable 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 includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable 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 includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store the desired information.

[0155] 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 any information delivery medium. The term modulated data signal means a signal that has one or more of its characteristics set or changed so 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, or other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

[0156] An exemplary environment (1100) implementing various aspects of the present disclosure is illustrated, including a computer (1102) comprising a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects 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 multiprocessor architectures may also be utilized as the processing unit (1104).

[0157] The system bus (1108) may be any of several types of bus structures that may be additionally interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. The system memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). A basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, or EEPROM, and includes 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.

[0158] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA) - which may also be configured for external use within a suitable chassis (not shown), a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from or writing to a CD-ROM disk (1122) or other high-capacity optical media such as a DVD). The hard disk drive (1114), the magnetic disk drive (1116), and the optical disk drive (1120) may be connected 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 implementing an external drive includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

[0159] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of the computer (1102), the drives and media correspond to storing any data in a suitable digital format. While the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will appreciate that other types of computer-readable media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, 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.

[0160] 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 in the drive and RAM (1112). All or portions of the operating system, applications, modules, and / or data may also be cached in RAM (1112). It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0161] A user may enter commands and information into the computer (1102) via one or more wired / 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, and the like. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) that is connected to the system bus (1108), but may be connected by other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, and the like.

[0162] The term "user input" in this disclosure may refer to any form of user input related to a user request performed within a user interface (or within a web page). For example, user input may include user input for moving a pointer object. As another example, user input may include user input for selecting a specific object on the user interface. For example, user input for an object (e.g., a module, a tab, etc.) may be made by touching or clicking on the object. When user input related to selection is received, a new object may be displayed on the user interface or web page in response to the input, or the properties of the object may be changed and displayed.

[0163] As another example, user input may include information such as language, letters, numbers, and symbols entered through various input methods. User input is not limited to the examples described above, and various user actions are possible, such as mouse cursor control, mouse wheel scrolling, keyboard arrow keys, mouse clicks, and touch.

[0164] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface such as a video adapter (1146). In addition to the monitor (1144), the 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-liquidcrystal display (TFT LCD), an organic light-emitting diode (OLED), a flexible display, a 3D display, and an e-ink display. The display unit outputs (displays) data processed by the processor (110).

[0165] The computer (1102) may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) (1148) via wired and / or wireless communications. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and typically include many or all of the components described for the computer (1102), but for simplicity, only the memory storage device (1150) is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) (1152) and / or a larger network, such as a wide area network (WAN) (1154). These LAN and WAN networking environments are common in offices and companies, facilitating enterprise-wide computer networks such as intranets, all of which can be connected to computer networks worldwide, such as the Internet.

[0166] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communications to the LAN (1152), which may also include a wireless access point installed therein for communicating with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), or may be connected to a communications computing device on the WAN (1154), or have other means for establishing communications over the WAN (1154), such as via the Internet. The modem (1158), which may be internal or external and wired or wireless, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, program modules or portions thereof described for the computer (1102) may be stored in a remote memory / storage device (1150). It will be appreciated that the network connections depicted are exemplary and other means of establishing a communications link between the computers may be used.

[0167] The computer (1102) operates to communicate with any wireless device or object that is arranged and operates via wireless communication, such as a printer, a scanner, a desktop and / or portable 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. Accordingly, the communication may be a predefined structure as in a conventional network, or may simply be an ad hoc communication between at least two devices.

[0168] Wi-Fi (Wireless Fidelity) enables connections to the Internet and other devices without wires. Wi-Fi is a wireless technology that allows devices, such as computers, to send and receive data anywhere within the coverage area of ​​a base station, both indoors and outdoors, similar to cell phones. Wi-Fi networks use wireless technologies called IEEE 802.11 (a, b, g, etc.) 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, at data rates of, for example, 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual-band).

[0169] 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 referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0170] In addition, the computer (1102) may be implemented as a user terminal. Therefore, the method according to one embodiment of the present disclosure can be borrowed without limitation for a terminal as hardware capable of loading software. The user terminal described in the present disclosure may include a mobile phone, a smart phone, a laptop computer, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, a slate PC, a tablet PC, an ultrabook, a wearable device (e.g., a smartwatch, a smart glass, a head mounted display (HMD)), and the like. In addition, 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 the user, and a device capable of wired / wireless communication. For example, the computing device (100) may be a desktop, a laptop, a tablet PC, a portable terminal, and the like.

[0171] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, various forms of programs or design code (referred to herein, for convenience, as software), or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0172] The various embodiments presented herein can be implemented as 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 from any computer-readable storage 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.). Furthermore, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0173] It should be understood that the specific order or hierarchy of steps in the presented processes is merely an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but are not intended to be limited to the specific order or hierarchy presented.

[0174] The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments disclosed herein, but is to be construed in the broadest scope consistent with the principles and novel features disclosed herein.

[0175] [Explanation of symbols]

[0176] 10: Questioner Information Reception Module

[0177] 20: Metadata Generation Module

[0178] 30: Metadata Optimization Module

[0179] 40: Database creation module based on optimal database creation unit

Claims

1. Processor; and A memory (memory) in which at least one instruction executed by the processor is stored; At least one instruction of the above causes the processor to: A natural language query delivery step where a user inputs a query and the query is passed through the results platform to a transformation matrix; A query processing step in which the transformation matrix analyzes the natural language query through a large-scale language model and generates a meta natural language query by referencing related data in the embedded vector store; and A meta information generation step for generating a database query based on the meta information generated in the optimal database generation unit and deriving an optimized result; configured to perform; Meta generation and database optimization device based on questioner information.

2. A questioner information collection module that helps understand the user's role through user information and classify data based on this; A metadata creation module that logically structures various objects within 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 collected questioner information and provides the most relevant business data to users first; and A database creation module based on an optimal database creation unit that generates a database query based on optimized metadata and executes it on a database; Meta generation and database optimization system based on questioner information.

3. In claim 2, the questioner information collection module, Profile information collector, which is responsible for collecting basic profile information such as user ID, department, and position: Behavioral history analyzer that collects and analyzes users' 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 collected profile information and behavioral history: Meta generation and database optimization system based on questioner information.

4. In claim 2, the metadata generation module, Database Object Relationship Mapper: Specifies and maps the relationships between objects within a company's database. A metadata structuring engine that creates metadata consisting of dimensions, attributes, and measures by creating a hierarchical classification system in the form of folders: and A metadata repository that stores and manages the generated metadata structures: Meta generation and database optimization system based on questioner information.

5. In claim 2, the metadata optimization module, A prioritization engine that dynamically determines the order in which metadata items are displayed based on collected user profiles and behavioral history: Real-time update monitor that monitors user behavior changes in real time and updates metadata structures: and A personalized metadata provider that provides users with an optimized metadata structure: Meta generation and database optimization system based on questioner information.

6. In claim 2, the database creation module based on the optimal database creation 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 mapped information: A query optimization engine that optimizes database queries by applying index utilization, join optimization, and efficient placement of filter conditions: and ·Result provision module: Including: that provides the results of the executed query to the user in the form of a visualization tool or report. Meta generation and database optimization system based on questioner information.

7. A step where the user inputs a query into the system in natural language; A step in which a transformation matrix converts natural language into a meta-language and transmits the query content; A step in which a natural language processing module receives a metalanguage query, analyzes the natural language query, and presents a metalanguage answer accordingly; A step of generating an optimal database by receiving a meta language command, generating an optimal database query, preparing a query result answer from the data set result, and transmitting the result answer to the user; including; How to create meta and optimize database based on questioner information.

8. A method for generating meta-data and optimizing a database based on questioner information, performed by a computing device including at least one processor, When a user enters a query, a natural language query delivery step is performed, which is then passed through the results platform to the transformation matrix; A query processing step in which the transformation matrix analyzes the natural language query through a large-scale language model and generates a meta natural language query by referencing related data in the embedded vector store; and A meta information generation step for generating a database query based on the generated meta information and deriving an optimized result; including; How to create meta and optimize database based on questioner information.

9. In claim 8, the method comprises: The final results based on the metadata further include a result delivery step that is delivered to the user in real time in a streaming manner. How to create meta and optimize database based on questioner information.

10. A computer program stored in a computer-readable storage medium, wherein the computer program, when executed by one or more processors, performs a method of meta generation and database optimization according to questioner information, the method comprising: When a user enters a query, a natural language query delivery step is performed, which is then passed through the results platform to the transformation matrix; A query processing step in which the transformation matrix analyzes the natural language query through a large-scale language model and generates a meta natural language query by referencing related data in the embedded vector store; and A meta information generation step for generating a database query based on the meta information generated in the optimal database generation unit and deriving an optimized result; A computer program stored on a computer-readable storage medium.

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