WO2024019225A1 - Procédé de traitement de données structurées et de données non structurées dans une pluralité de bases de données différentes, et plateforme de traitement de données fournissant ledit procédé - Google Patents

Procédé de traitement de données structurées et de données non structurées dans une pluralité de bases de données différentes, et plateforme de traitement de données fournissant ledit procédé Download PDF

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WO2024019225A1
WO2024019225A1 PCT/KR2022/014150 KR2022014150W WO2024019225A1 WO 2024019225 A1 WO2024019225 A1 WO 2024019225A1 KR 2022014150 W KR2022014150 W KR 2022014150W WO 2024019225 A1 WO2024019225 A1 WO 2024019225A1
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data
query
unstructured
structured
data processing
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PCT/KR2022/014150
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English (en)
Korean (ko)
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이상수
임정택
윤준영
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스마트마인드 주식회사
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/26—Visual data mining; Browsing structured data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • the present invention relates to a method for processing structured and unstructured data on a plurality of different databases and a data processing platform that provides such method. More specifically, it relates to a method of processing unstructured data in a database that enables processing of unstructured data in a plurality of different databases by expanding the function of the existing database that only processes structured data, and a data processing platform that provides such method. .
  • the purpose of the present invention is to solve all of the above-mentioned problems.
  • the purpose of the present invention is to process structured data and unstructured data using one language based on extended SQL (structured query language) and one platform.
  • the present invention not only enables more accurate modeling of artificial intelligence models by having the operating platform and modeling platform on one platform, but also enables modeling of artificial intelligence models based on structured data and unstructured data without separate batch processing.
  • the purpose is to provide functionality.
  • a representative configuration of the present invention to achieve the above object is as follows.
  • a method of processing structured data and unstructured data on a plurality of different databases includes the steps of a data processing system receiving external data from an external database, the data processing system converting the external data. and the data processing system processing the converted external data.
  • the external data includes structured data and unstructured data
  • the data processing system processes the structured data and the unstructured data based on nested queries
  • the data processing system processes the unstructured data based on queries.
  • Processes unstructured data and the data processing system processes the structured data based on a structured data processing query, wherein the nested query is a query that mixes a first query for unstructured data and a second query for structured data.
  • the unstructured data processing query may be a query for processing only the unstructured data
  • the structured data processing query may be a query for processing only the structured data.
  • the data processing system creates a data table for the structured data and a data table for the unstructured data and processes them in one database, and the data processing system uses artificial intelligence based on the structured data and the unstructured data. Engine modeling can be supported on the single database.
  • a data processing system that processes structured data and unstructured data on a plurality of different databases receives external data from an external database, converts the external data, and processes the converted external data. It can be implemented to do so.
  • the external data includes structured data and unstructured data
  • the data processing system processes the structured data and the unstructured data based on a nested query
  • the data processing system processes the unstructured data based on a query.
  • Processes unstructured data and the data processing system processes the structured data based on a structured data processing query, wherein the nested query is a query that mixes a first query for unstructured data and a second query for structured data.
  • the unstructured data processing query may be a query for processing only the unstructured data
  • the structured data processing query may be a query for processing only the structured data.
  • the data processing system creates a data table for the structured data and a data table for the unstructured data and processes them in one database, and the data processing system uses artificial intelligence based on the structured data and the unstructured data. Engine modeling can be supported on the single database.
  • structured data and unstructured data can be processed using one language based on extended SQL (structured query language) and one platform.
  • extended SQL structured query language
  • the operating platform and modeling platform are located on one platform, which not only enables modeling of a more accurate artificial intelligence (AI) model, but also enables AI based on structured data and unstructured data without separate batch processing.
  • AI artificial intelligence
  • a modeling function of the model may be provided.
  • Figure 1 is a conceptual diagram showing an existing data processing system.
  • Figure 2 is a conceptual diagram showing a data processing system for processing structured data and unstructured data on one platform according to an embodiment of the present invention.
  • Figure 3 is a conceptual diagram showing a data processing system for processing structured data and unstructured data on one platform according to an embodiment of the present invention.
  • Figure 4 is a conceptual diagram showing the operation of a data processing system according to an embodiment of the present invention.
  • Figure 5 is a conceptual diagram showing the operation of a data processing system according to an embodiment of the present invention.
  • Figure 6 is a conceptual diagram showing a data processing method based on a data processing system according to an embodiment of the present invention.
  • Figure 1 is a conceptual diagram showing an existing data processing system.
  • a data processing method for structured data 100 and unstructured data 120 in an existing data processing system is disclosed.
  • Structured data 100 is data that is stored in tables according to schema and can be connected between tables through relationships. Structured data 100 can be displayed in rows and columns with an appropriately defined schema for the information it holds. Each column represents a different property, while each row contains data associated with a single instance of the property. Rows and columns can form a table that can be easily referenced, different tables can be linked, and a relational database 140 can be formed when several tables are sequentially linked.
  • Unstructured data 120 is the opposite of structured data 100, and is data whose meaning is difficult to easily understand because there are no set rules, and may include data such as voice, image, and video.
  • the existing data processing system could only query structured data (100) based on SQL (structured query language), and a NoSQL database without a specific schema was used to process unstructured data (120).
  • the existing data processing system was capable of real-time querying of structured data (100), but real-time querying of unstructured data (120) was not possible.
  • unstructured data 120 is processed through batch processing instead of real time processing. Because of this, real-time search for images, videos, and voices was impossible in existing data processing systems. More specifically, in existing data processing systems, it is difficult to analyze large amounts of unstructured data 120 in real time. Therefore, processing was performed based on the Lambda architecture (150), which combines a data table that can be acquired in real time and a batch table that has been calculated in advance at a fixed time, and structured data (100) and unstructured data (120) are separated. It was processed based on DMBS (database management system).
  • DMBS database management system
  • unstructured data 120 in the existing data processing system In order to learn about unstructured data 120 in the existing data processing system, artificial intelligence learning within the database was not possible.
  • the existing data processing system performed learning on structured data (100) based on an AI engine implemented in the database, but learning on unstructured data (120) was not processed based on SQL within the database, so unstructured data within the database AI engine modeling based on was impossible.
  • the existing data processing system when performing modeling for an AI engine, creates a sample table 160 through sampling from the parameter table of the operating system to perform modeling, and a modeling platform that performs modeling and actual operation are used to perform modeling.
  • the operating platforms are different. In this case, the problem of inaccurate modeling results occurs due to differences between the modeling platform and the operating platform.
  • parameter data can exist in various forms other than tables, it takes time to transform and extract the data, and a considerable amount of time is also required to preprocess the data for modeling.
  • sample data includes both structured and unstructured data
  • Lambda architecture must be applied to existing data processing systems. If you develop through Lambda architecture, you will use various platforms and languages, but you will waste a lot of time integrating them due to differences in characteristics and interoperability issues between platforms.
  • parameter data is managed in one form (table), and the process of extracting sample data is possible through a simple query statement and does not require a lambda architecture.
  • AI modeling for structured and unstructured data also has the advantage of being easy to process without any integration issues using one platform and one language.
  • the data processing platform can process structured data 100 and unstructured data 120 based on one language based on one platform.
  • the data processing platform not only enables more accurate modeling by having an operating platform and a modeling platform on one platform, but also enables structured data 100 and unstructured data 120 without separate batch processing. It can provide AI modeling functions based on .
  • Figure 2 is a conceptual diagram showing a data processing system for processing structured data and unstructured data on one platform according to an embodiment of the present invention.
  • the data processing system is capable of processing unstructured data 220 and structured data 210 on one platform.
  • a data processing syntax for processing unstructured data 220 together with structured data 210 on one platform is newly defined, and an extended SQL (extended SQL) that can use the newly defined data processing syntax is provided. 240) can be defined.
  • General queries for structured data 210 may be processed based on existing SQL such as PostgreSQL, and queries for unstructured data may be processed based on extended SQL 240 newly defined in the present invention.
  • An extended SQL engine 250 may be defined to process the newly defined data processing syntax on the extended SQL 240.
  • the extended SQL engine 250 may be an engine that enables processing of newly defined data processing syntax.
  • Nested query 230 is a mixed query for structured data 210 and unstructured data 220, enabling sequential or complex processing of structured data 210 and unstructured data 220 stored in the database. can do.
  • the structured data 210 and unstructured data 220 are processed on one platform. It is processed based on the extended SQL engine 250, and data processing for structured data 210 and unstructured data 220 is performed simultaneously on one database 260 based on nested query 230. It can be done. Based on this, AI modeling for structured data 210 and unstructured data 220 is also performed on the AI engine 270 of the data processing system.
  • the AI engine may be provided in advance with various AI engines such as classification models, regression models, recommendation models, and voice recognition models, or can be used without restrictions, such as models created by the user or AI engines provided as open source.
  • the data processing system of the present invention can process unstructured data 220 within one platform without separate batch processing, separate language, or separate platform.
  • the data processing system of the present invention is an integrated platform that allows both structured data 210 and unstructured data 220 to be queried using only SQL and enables AI modeling for structured data 210 and unstructured data 220. Therefore, since the modeling platform and the operating platform are the same, the problem of poor modeling accuracy due to different parameters can be reduced.
  • the data processing system of the present invention can apply the functions of RDB (relational database), AI, and big data platform in one platform, and can dramatically reduce inefficiencies that occur during AI-based digital transformation. Based on big data processing and distributed parallel processing technology, it enables data processing more than twice as fast as before.
  • RDB relational database
  • AI AI
  • big data platform in one platform, and can dramatically reduce inefficiencies that occur during AI-based digital transformation.
  • big data processing and distributed parallel processing technology it enables data processing more than twice as fast as before.
  • a method of processing structured data and unstructured data in a database includes the steps of a data processing system receiving a nested query and the data processing system performing processing on the nested query. can do.
  • a nested query may be a query that mixes a first query for unstructured data and a second query for structured data.
  • the step of performing nested query processing is a step in which the data processing system performs processing on unstructured data based on an extended SQL engine that processes extended SQL (extended structured query language), and the data processing system processes Postgre SQL. It may include processing structured data based on a general SQL engine that processes (extended structured query language).
  • the data processing system creates data tables for structured data and data tables for unstructured data and processes them in one database, and the data processing system supports artificial intelligence engine modeling based on structured data and unstructured data in one database. You can.
  • the data processing system may perform individual processing for each of structured data and unstructured data.
  • the data processing system may be implemented to receive unstructured data processing queries and structured data processing queries, and process the unstructured data processing queries and structured data processing queries.
  • An unstructured data processing query may be a query for processing only unstructured data
  • a structured data processing query may be a query for processing only structured data.
  • Unstructured data processing queries can be processed based on extended SQL and extended SQL engines, and structured data processing queries can be processed based on general SQL (Postgre SQL) and general SQL engines.
  • Figure 3 is a conceptual diagram showing a data processing system for processing structured data and unstructured data on one platform according to an embodiment of the present invention.
  • a nested query for processing unstructured data and structured data may be input as the input query 300.
  • a nested query may include a first query 310, a second query 320, and a third query 330, and the first query 310 and the third query 330 are extended queries. 350, and the second query 320 may be a general query 360.
  • the first query 310 may be PRINT IMAGE
  • the second query 320 may be SELECT
  • the third query 330 may be SEARCH IMAGE.
  • the first query 310, the second query 320, and the third query 330 may form an input query in a nested structure.
  • the input query 300 may be parsed through a parser. Based on the lexer, nested queries are divided into general queries (360) and extended queries (350), and the parser can split the general queries (360) and extended queries (350).
  • the first query 310, the second query 320, and the third query 330 may be interpreted and processed through cloud analysis and a query tree.
  • the third query 330, second query 320, and first query 310 may be processed in this order.
  • the first query 310 and the third query 330 are extended queries 350 and can be processed based on an extended SQL engine
  • the second query 320 is a general query, which is PostgreSQL, a SQL engine for general query processing. It can be processed based on the engine.
  • the standardized SQL engine and PostgreSQL engine can be connected to one database and process queries. Artificial intelligence learning based on structured and unstructured data is possible based on one database.
  • Figure 4 is a conceptual diagram showing the operation of a data processing system according to an embodiment of the present invention.
  • the query function for unstructured data can be performed based on the extended SQL below.
  • unstructured data images, audio, video, etc.
  • unstructured data can be created as an unstructured data table converted to a user-defined vector format based on a numerical algorithm.
  • Table 1 below is an example of create table syntax.
  • an image file that exists in a specific path can be created in the database as an unstructured data table using an attribute extraction artificial intelligence model.
  • an image file that exists in a specific path can be created on the database as a data table using an additional attribute extraction artificial intelligence model.
  • Search syntax can be used to search for content, meaning, or similarity in unstructured data.
  • Table 3 below is an example of a search statement.
  • a search statement can be used to search for similar images based on an image quantification artificial intelligence model.
  • the above query syntax is a newly defined syntax for SQL confirmed in the present invention.
  • search image data, audio data, and video data based on keywords or text based on an unstructured data table created based on the above query syntax.
  • search image data, audio data, and video data based on image data, audio data, and video data.
  • real-time search for the above unstructured data is possible in addition to real-time search for existing structured data.
  • nested queries which are a combination of queries on unstructured data and structured data, are also possible, making modeling using both unstructured and structured data possible.
  • Figure 5 is a conceptual diagram showing the operation of a data processing system according to an embodiment of the present invention.
  • ML functions for unstructured data can be performed based on extended SQL as shown below.
  • a user can use the "BUILD MODEL" syntax to create a movie recommendation model that recommends movies using an artificial intelligence model.
  • the "EVALUATE USING" statement can be used to evaluate the classification model that the user created in Learning a Model.
  • FIT MODEL a new model can be created that is trained using a newly added dataset to a model the user previously created.
  • data preprocessing used in an existing classification model can be applied to data preprocessing of a data set for learning another model.
  • Table 9 below is an example of the "PREDICT UDING" syntax.
  • the movie recommendation model that the user created in model training based on the "DELETE MODEL" statement may be deleted from the database.
  • AI modeling based on unstructured data and structured data can be performed on a single platform, a data processing system, without a separate batch process.
  • a pre-generated AI model and an AI model created by a user may be located.
  • various AI models such as classification models, regression models, recommendation systems, and voice recognition models can be created.
  • Figure 6 is a conceptual diagram showing a data processing method based on a data processing system according to an embodiment of the present invention.
  • processing of structured data and unstructured data may be performed based on the data processing system's own database.
  • users can use their own database and utilize the functions of the extended SQL and extended SQL engine provided by the data processing system based on the API.
  • the processing of structured and unstructured data based on the data processing system's own database can be expressed in the term internal data processing.
  • the processing of structured and unstructured data based on an external database rather than the data processing system's own database can be expressed in the term external data processing.
  • external data In order to use the data processing system according to an embodiment of the present invention from the outside for external data processing, external data must be stored and converted into the data processing system of the present invention using the provided 'API' or 'data transfer method'.
  • the data processing system of the present invention can be utilized using the API. That is, both the internal engine and the PostgreSQL engine can perform data processing by accessing the database according to the embodiment of the present invention rather than an external database.
  • users can perform learning based on separate unstructured data stored in the user's database based on the functions of extended SQL and extended SQL engine through API.
  • a specific user may be a security company and operate a user database that stores CCTV footage.
  • users can perform artificial intelligence learning on CCTV images based on data stored in the user database.
  • Structured data and unstructured data can be inserted from an external database into the database of the data processing system of the present invention based on a query statement for unstructured data for processing structured data and unstructured data defined in the present invention.
  • AI modeling for structured data and unstructured data input to the data processing system according to an embodiment of the present invention can be performed based on the AI engine of the data processing system according to an embodiment of the present invention.
  • the method of processing structured data and unstructured data on a plurality of different databases includes the steps of a data processing system receiving external data from an external database, the data processing system converting the external data, and the data processing system converting the external data. It may include processing the external data.
  • the external data includes structured data and unstructured data
  • the data processing system processes structured data and unstructured data based on nested queries
  • the nested query is the first query for unstructured data and the second query for structured data. It may be a mixed query of 2 queries.
  • a data processing system can process unstructured data based on unstructured data processing queries, and the data processing system can process structured data based on structured data processing queries.
  • a nested query is a query that combines a first query for unstructured data and a second query for structured data
  • an unstructured data processing query is a query for processing only the unstructured data
  • a structured data processing query is a query for processing only structured data. It could be a query for
  • the data processing system creates data tables for structured data and data tables for unstructured data and processes them in one database, and the data processing system supports artificial intelligence engine modeling based on structured data and unstructured data in one database. You can.
  • the embodiments according to the present invention described above can be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium.
  • the computer-readable recording medium may include program instructions, data files, data structures, etc., singly or in combination.
  • the program instructions recorded on the computer-readable recording medium may be specially designed and configured for the present invention or may be known and usable by those skilled in the computer software field.
  • Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks. medium), and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc.
  • Examples of program instructions include not only machine language code such as that created by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.
  • a hardware device can be converted into one or more software modules to perform processing according to the invention and vice versa.

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Abstract

La présente invention concerne un procédé de traitement de données structurées et de données non structurées dans une pluralité de bases de données différentes, ainsi qu'une plateforme de traitement de données fournissant le procédé. Le procédé de traitement de données structurées et de données non structurées dans une pluralité de bases de données différentes peut comprendre : une étape à laquelle un système de traitement de données reçoit des données externes provenant d'une base de données externe; une étape à laquelle le système de traitement de données convertit les données externes; et une étape à laquelle le système de traitement de données traite les données externes converties.
PCT/KR2022/014150 2022-07-21 2022-09-22 Procédé de traitement de données structurées et de données non structurées dans une pluralité de bases de données différentes, et plateforme de traitement de données fournissant ledit procédé WO2024019225A1 (fr)

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