WO2025110706A1 - Meta generation and database optimization system according to questioner information, and method thereof - Google Patents

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

The present disclosure relates to the fields of artificial intelligence technology and database query processing technology, and more specifically, 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.

Modern enterprises 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 the same metadata structure to all users.

Patent Document 1 (Korean Patent Publication No. 10-2023-0075864) discloses a method for optimizing a query. This conventional method for optimizing a query helps reduce the complexity of system management, but it is inefficient for users in various departments and positions to retrieve the data they need for their respective tasks. In addition, since the conventional method for optimizing a query does not consider the profile information or behavioral history of each user, the user must set complex search conditions to find the data they need or manually select the desired information from a large amount of data. In addition, the conventional method for optimizing a query requires knowledge of the SQL language when the database must be accessed directly to retrieve data, which burdens non-experts or beginners with having to write complex SQL queries, and the usability of the data is low.

Meanwhile, attempts to improve database management and data analysis by utilizing artificial intelligence and machine learning technologies have been increasing recently, and natural language processing (NLP)-based question-answering systems have been developed that utilize these artificial intelligence and machine learning technologies to understand questions entered by users in natural language and provide corresponding data. However, there have been many cases where user intentions and domain information were not sufficiently reflected. In response, recommendation system functions that analyze user behavior patterns and recommend necessary information have been introduced, but recommendation systems have been mainly focused on the field of content consumption. In addition, although automated tuning tools exist to improve the performance of database queries, they have been far from providing user-tailored data.

In this way, the conventional method of optimizing queries was designed as a data query system that provided the same metadata structure to all users and did not reflect the needs and preferences of each individual user, which limited the accessibility and usability of data.

[Prior art literature]

[Patent Document]

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

The present disclosure has been made 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 a 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.

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.

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 purpose includes a meta-generation and database optimization device according to questioner information, 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 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.

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 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 preferentially provides 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 database query.

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 behavior history analyzer responsible for collecting and analyzing 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.

The metadata creation 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 created metadata structure.

The metadata optimization module may include: a priority decision 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.

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 index utilization, join optimization, and efficient arrangement techniques 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.

According to another aspect of the present disclosure, a method for generating meta-language and optimizing a database according to questioner information 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.

According to another aspect of the present disclosure, a method for generating meta data and optimizing a database according to 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 result platform transmits the natural language query 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 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.

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

In accordance with another aspect of the present disclosure, a computer program stored in a computer-readable storage medium is provided, which computer program, when executed by one or more processors, performs a meta generation and database optimization method according to questioner information, wherein the method 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 a database query is generated based on the meta information generated by an optimal database generation unit and an optimized result is derived.

The present disclosure has been made 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.

By providing metadata structures optimized for each user, users can quickly and accurately access the data they need. This leads to a reduction in data search time and improved work efficiency, thereby increasing users' work productivity.

We provide personalized data services by analyzing user profiles and behavior history and prioritizing the exposure of metadata items most relevant to each user. This contributes to improving user satisfaction and maximizing data usability.

It improves database response speed by automatically generating efficient database queries by sending commands to the optimal database generation unit based on the optimized metadata structure. This enables efficient use of system resources, reduces server load, and reduces operating costs.

Further scope of the applicability of the present disclosure will become apparent from the detailed description below. However, since various changes and modifications 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 embodiments, such as preferred embodiments of the present invention, are given by way of example only.

Various aspects are now described with reference to the drawings, wherein like reference numerals are used to refer to like elements generally. In the following examples, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. It will be apparent, however, that such aspect(s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects.

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

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.

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

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

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

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

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

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are set forth to provide an understanding of the present disclosure. However, it will be apparent that these embodiments may be practiced without these specific descriptions.

The terms "component," "module," "system," and the like, as used herein, refer to a computer-related entity, 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, an application running on a computing device and the computing device may both be components. 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, such components may execute from various computer-readable media having various data structures stored therein. The components may communicate via local and/or remote processes, for example, by a signal 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 via a network such as the Internet to another system via the signal).

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

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

When it is said that a component is “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 in between. On the other hand, when it is said that a component is “directly connected” or “connected” to another component, it should be understood that there are no other components in between.

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 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.

Also, the terms "comprises" and/or "comprising" should be understood to mean 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. Also, unless otherwise specified or clear from the context to refer to the singular form, the singular form as used in the specification and claims should generally be construed to mean "one or more."

Hereinafter, regardless of the drawing symbols, identical or similar components are given the same reference numerals, and redundant descriptions thereof are omitted. In addition, when describing the embodiments disclosed in this specification, if it is determined that a detailed description of a related known technology may obscure the gist of the embodiments disclosed in this specification, the detailed description thereof is omitted. In addition, 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.

And, the term “at least one of A or B” should be interpreted to mean “including only A,” “including only B,” or “combined as a composition of A and B.”

Those skilled in the art should additionally recognize 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 combinations of electronic hardware, computer software, or 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.

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.

Below, 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.

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

Referring to FIG. 1, the configuration of the computing device (100) illustrated in FIG. 1 is only 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 configure the computing device (100).

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

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

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).

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).

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 (for example, an 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 memory described above is merely an example, and the present disclosure is not limited thereto.

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).

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.

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 with 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.

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

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

Meanwhile, the application of the present specification may include a separate statement execution module. The statement execution module may generate a customized statement that conforms to the syntax according to the type of the database when the database statement performs input/output of data for the database management system (RDBMS). For example, through the present disclosure, a user can conveniently generate, confirm, and control a statement through an abstracted formula provided in a user interface (UI) provided by the application regardless of the type of database.

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

cord explanation All Include the column in all Insert and Update actions. InsertOnly Include columns only in Insert statements. UpdateOnly Include columns in the Update statement.

In addition, the user input information may be for setting columns related to syntax generation or specifying record conditions. For example, it may be possible to enable SQL syntax to be executed even when the column order between the original data and the output data is changed or the column names are different. In addition, the user input information may display a comment on the output data.

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

Referring to FIG. 2, the meta 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).

The questioner information collection module (10) is a module that supports understanding the user's role through user information and classifying data based on this. The questioner information collection module (10) collects basic profile information such as the user's ID, department, and position. This is used to predict the required data category by identifying the user's position and role within the organization. In addition, the user's previous search history, click pattern, frequently used keywords, and areas of interest are analyzed to identify the user's data usage pattern and preference. Through this, an in-depth understanding of each user is built and a foundation is laid for providing personalized data services. For example, if a user in the finance department frequently searches for data on profit and loss analysis, the system learns this and preferentially displays metadata items preferred by the user to improve accessibility.

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). It introduces a hierarchical classification system in the form of folders and creates classification items consisting of dimensions, attributes, and measures.

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

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

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

This structure clearly defines the meaning and relationships of the data, helping users understand and explore the data more intuitively. In addition, the hierarchical structure facilitates data exploration, helping users quickly find the information they need.

The metadata optimization module (30) is a module that optimizes the exposure order of metadata items based on the collected questioner information and provides the most relevant business data to the user with priority. The metadata optimization module (30) dynamically adjusts the exposure order of metadata classification items based on the collected questioner information to create an optimized metadata structure. This is to provide the most relevant business data to each user with priority.

The metadata optimization module (30) adjusts the display order of metadata items in consideration of the user's department, position, area of interest, etc. in the user-specific priority setting items. For example, a user in the sales department places dimensions and measures related to sales performance at the top to improve accessibility.

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.

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

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.

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.

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

The database creation module (40) based on the optimal database creation unit (i-META) creates 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 the data retrieval speed.

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 items and provides the results to the user in the form of a visualization tool or report.

For example, if a user in the marketing department wants to analyze customer responses 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 generate an efficient SQL query based on the selected items to provide results.

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

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.

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

Referring to FIG. 3, a meta-generation and database optimization system according to 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.

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 a user's query into a meta-language and processes it when the user queries in natural language, and the meta-language analyzes the user's query, automatically generates and executes an appropriate SQL query, 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.

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).

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

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

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

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

In the system for meta-generation and database optimization according to questioner information of the present disclosure, when a user inputs a query in natural language, the system extracts major nouns and verbs through morphological analysis and goes through a similar word processing process. The extracted information is converted into a meta-language, and an SQL query is automatically generated based on the extracted information. The generated SQL query is executed in a database to derive a result, and this result is then converted into natural language through the meta-language and provided to the user. The meta-language plays an important role in the process of query transmission, command transmission, and result reception, and the user ultimately receives the result processed in natural language.

For example, the system for meta generation and database optimization according to questioner information of the present disclosure can convert a 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 is generated, and ultimately the sales status desired by the user can be provided.

Such a meta-generation and database optimization system according to the questioner information of the present disclosure may include a function for managing similar queries, and may detect queries similar to previously used queries and process data more efficiently based on the queries. In addition, query performance is improved and the response time of the system is shortened by utilizing database (SQL) optimization and data caching technology. In addition, query processing based on meta-language allows users to easily obtain desired information without having to directly write a database (SQL). In addition, by processing database queries based on natural language, a system can be provided that allows users to easily search data without having to write SQL queries, thereby improving data accessibility and making it easy for non-experts to use.

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

Referring to FIG. 4, the meta generation and database optimization system according to the questioner information of the present disclosure is a system that analyzes a user's query expressed in natural language, generates an SQL query based on the query, searches for data in a database, and provides the results in a form that the user can understand. This system is mainly used for data searches such as sales management in a specific business division such as the electric power business division of a company.

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

The user (USER) queries the sales status of a specific business division in natural language. For example, "Please tell me how much was sold this year" or "What are the sales of the electric division in 2023?"

Transformation Matrix (G-MATRIX) is a module that analyzes the user's natural language queries and generates SQL queries. For example, it processes queries for "Sales of S Company's Electric Power Division from 2023.1.1 to today."

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

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

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

When a user inputs a query in natural language, such as “What will be the sales of the electric division in 2023?”, the NLP module analyzes the query and extracts important time periods and departments.

The G-MATRIX module generates a specific SQL query called “Sales of S Corporation Electric Business Division from 2023.1.1 to today” based on the analyzed data.

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

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

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, by 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.

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 the request and converts it into an accurate SQL query.

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.

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

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

When a user enters a natural language query such as “Show me the sales of the electric power division this year,” the system converts the query into “Sales of the S company’s electric power division from 2023.1.1 to today,” and then executes a SQL query to produce the results. The results are provided in the form of Table A, and are output to the user as visualized data using Template B.

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 search for necessary data through natural language queries without having to understand complex query languages such as SQL, thereby allowing non-experts to easily access data and efficiently manage sales and create reports.

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

Referring to FIG. 5, a system for meta generation and database optimization according to questioner information according to one embodiment of the present disclosure may be a data service system that analyzes a user's natural language query 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 meta generation and database optimization according to questioner information according to the present disclosure can generate an SQL query by utilizing learned metadata and provide the result in real time.

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

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).

It receives natural language queries from users. For example, it includes queries such as "What are the sales of the electric division in 2023?"

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.

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 forms.

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), so that the optimal database generation unit (i-META) can use the learned data in the process of generating and optimizing SQL queries.

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

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

Referring to FIG. 6, the method for generating meta information and optimizing a database according to 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).

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.

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.

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.

The result provision step (S4000) provides the final result based on metadata in real time to the user in a streaming manner.

In one embodiment, the method for generating meta data and optimizing a database according to questioner information of the present disclosure is such that when a user inputs a query such as "What is the sales of the electric power division in 2023?", the system analyzes the query in 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 generation unit (i-META) provides an optimized template to the user. The result is responded to in real time in a streaming manner.

The method for generating meta-information and optimizing a database according to 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.

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

The method of generating meta data and optimizing a database according to questioner information of the present disclosure can efficiently process data by considering the correlation with previous queries when a user continuously inputs queries. An SQL query can be automatically generated based on meta information, optimized, and executed in a database.

The method of generating meta data 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 a continuous query processing function.

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

Referring to FIG. 7, the computer program, when executed by one or more processors, may cause the user to perform an action for authorization.

Also disclosed is a computer-readable medium storing a data structure according to one embodiment of the present disclosure.

A data structure can refer to the organization, management, and storage of data that enables efficient access and modification of data. A data structure can refer to the organization of data to solve a specific problem (e.g., data retrieval in the shortest time, data storage, data modification). A data structure can also be defined as a physical or logical relationship between data elements designed to support a specific data processing function. A logical relationship between data elements can include a connection relationship between user-defined data elements. A physical relationship between data elements can include an actual relationship between data elements that are physically stored in a computer-readable storage medium (e.g., a persistent storage device). A data structure can specifically include a set of data, a relationship between data, and a function or command that can be applied to the data. An effectively designed data structure enables a computing device to perform operations while using minimal resources of the computing device. Specifically, a computing device can increase the efficiency of operations, reading, inserting, deleting, comparing, exchanging, and searching through an effectively designed data structure.

Data structures can be divided into linear data structures and nonlinear data structures depending on the form of the data structure. A linear data structure can be a structure in which only one data is connected after another. Linear data structures can include lists, stacks, queues, and deques. A list can mean a series of data sets that have an internal order. A list can include a linked list. A linked list can be a data structure in which data is connected in a way that each data has a pointer and is connected in a single line. In a linked list, a pointer can include information on the connection to the next or previous data. A linked list can be expressed as a singly linked list, a doubly linked list, or a circular linked list depending on its form. A stack can be a data listing structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (e.g., inserted or deleted) only at one end of the data structure. Data stored in a stack can be a data structure in which the later it enters, the sooner it comes out (LIFO - Last in First Out). A queue is a data list structure that allows limited access to data, and unlike a stack, it can be a data structure in which data stored later comes out later (FIFO - First in First Out). A deck can be a data structure that can process data at both ends of the data structure.

A nonlinear data structure can be a structure in which multiple data are connected behind one data. A nonlinear data structure can include a graph data structure. 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.

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

The data structure may include weights of the neural network. In this specification, weights and parameters may be used with the same meaning. And the data structure including the weights of the neural network may be stored in a computer-readable medium. The neural network may include a plurality of weights. The weights may be variable and may be variable by a user or an algorithm so that the neural network may perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node may determine a data value output from the output node based on values input to the input nodes connected to the output node and weights set for links corresponding to each of the input nodes. The above-described data structure is only an example, and the present disclosure is not limited thereto.

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 also 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.

In general, 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 practiced 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.

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

Computers typically include a variety of computer-readable media. Any media that can be accessed by a computer can be a computer-readable media, and such computer-readable media 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.

Computer-readable transmission media typically includes any information delivery media that 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. 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, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An exemplary environment (1100) implementing various aspects of the present disclosure is illustrated, including a computer (1102) including 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 multiprocessor architectures may also be utilized as the processing unit (1104).

The system bus (1108) may be any of several types of bus structures that may additionally be 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 nonvolatile memory (1110), such as ROM, EPROM, EEPROM, and the BIOS contains 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.

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. An interface (1124) for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide nonvolatile storage of data, data structures, computer-executable instructions, and the like. In the case of a computer (1102), the drives and media correspond to storing any data in a suitable digital format. While the description of computer-readable media above has referred 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 media readable by a computer, 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.

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 a variety of commercially available operating systems or combinations of operating systems.

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 coupled 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.

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

As another example, user input may include information such as language, letters, numbers, and symbols entered by various input means. User input is not limited to the examples described above, and various forms of user actions are possible, such as mouse cursor control, mouse wheel scroll, keyboard arrow keys, mouse clicks, and touch.

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, a printer, 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 then outputs (displays) data processed by the processor (110).

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.

When used in a LAN networking environment, the computer (1102) is connected to the local network (1152) via a wired and/or wireless communications 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 may 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 described for the computer (1102) or portions thereof 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.

The computer (1102) is configured to communicate with any wireless device or object that is configured and operates in 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 wireless 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.

Wi-Fi (Wireless Fidelity) allows you to connect to the Internet and other things without wires. Wi-Fi is a wireless technology that allows devices, such as computers, to send and receive data indoors and outdoors, anywhere within the coverage area of a base station, just like cell phones. Wi-Fi networks use a wireless technology 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).

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.

In addition, the computer (1102) may be implemented as a user terminal. Accordingly, the method according to one embodiment of the present disclosure may 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.

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 program 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.

The various embodiments presented herein can be implemented as a method, an apparatus, or an article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, a carrier, or a medium 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.). Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of exemplary approaches. It is to 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 meant to be limited to the specific order or hierarchy presented.

The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the 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 disclosure. Thus, the disclosure is not intended to be limited to the embodiments disclosed herein, but is to be construed in the widest scope consistent with the principles and novel features disclosed herein.

[Explanation of symbols]

10: Questioner Information Reception Module

20: Metadata Generation Module

30: Metadata Optimization Module

40: Database creation module based on optimal database creation part

Claims (10)

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 and deriving an optimized result based on the meta information generated in the optimal database generation unit; configured to perform; Meta generation and database optimization device based on questioner information. 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. 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. 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. 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. In claim 2, the database creation module based on the optimal database creation unit, 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. A step in which a 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. A method of meta-generation and database optimization based on interrogator 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. 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. 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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