WO2024071504A1 - Procédé de traitement de données structurées et de données non structurées par attribution de ressources de différents processus, et système de traitement de données pour fournir un procédé - Google Patents

Procédé de traitement de données structurées et de données non structurées par attribution de ressources de différents processus, et système de traitement de données pour fournir un procédé Download PDF

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WO2024071504A1
WO2024071504A1 PCT/KR2022/015608 KR2022015608W WO2024071504A1 WO 2024071504 A1 WO2024071504 A1 WO 2024071504A1 KR 2022015608 W KR2022015608 W KR 2022015608W WO 2024071504 A1 WO2024071504 A1 WO 2024071504A1
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data
query
extended
processing system
structured
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PCT/KR2022/015608
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English (en)
Korean (ko)
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이상수
임정택
윤준영
백인욱
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스마트마인드 주식회사
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Publication of WO2024071504A1 publication Critical patent/WO2024071504A1/fr

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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
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • 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
    • G06F16/2453—Query optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]

Definitions

  • the present invention relates to a method for processing structured data and unstructured data by allocating different processor resources, and a data processing system that provides such method. More specifically, it relates to a method of processing structured data and unstructured data by allocating different processor resources to improve efficient use of computing resources and data processing speed, and a data processing system that provides such method.
  • the purpose of the present invention is to solve all of the above-mentioned problems.
  • the present invention processes structured data and unstructured data using one language based on extended SQL (structured query language) and one platform, and uses different computing resources in processing structured data and unstructured data.
  • the purpose is to efficiently utilize computing resources and increase data processing speed by processing data based on data processing.
  • the present invention provides data processing and artificial intelligence model modeling functions based on structured data and unstructured data without separate batch processing, but selectively uses a CPU (central processing unit) and GPU (graphics processing unit).
  • CPU central processing unit
  • GPU graphics processing unit
  • a representative configuration of the present invention to achieve the above object is as follows.
  • a method of processing structured data and unstructured data by allocating different processor resources includes the steps of a parser unit of a data processing system parsing a nested query into a general query and an extended query, the data processing system Cloud analyzer (requiring wording check) of determining computing resources to be used for processing the general query and the extended query, wherein the nested query includes the extended query for unstructured data and the extended query for structured data. It may be a mixed query of the above general queries.
  • the general query is processed based on a PostgreSQL (structured query language) engine
  • the extended query is processed based on an extended SQL engine
  • the computing resource is a CPU (central processing unit) or GPU (graphics processing unit).
  • the general query and the extended query may be processed through the CPU or GPU based on whether processing based on the GPU essential usage model is required and based on CPU execution ability and GPU execution ability.
  • the general query is processed based on a PostgreSQL (structured query language) engine
  • the extended query is processed based on an extended SQL engine
  • the computing resource is a CPU (central processing unit) or GPU (graphics processing unit).
  • the general query and the extended query may be processed through the CPU or GPU based on whether processing based on the GPU essential usage model is required and based on CPU execution ability and GPU execution ability.
  • structured data and unstructured data are processed through one language based on extended SQL (structured query language) and one platform, and in processing structured data and unstructured data, based on different computing resources.
  • extended SQL structured query language
  • computing resources can be utilized efficiently and data processing speed can be improved.
  • data processing and artificial intelligence model modeling functions based on structured data and unstructured data are provided without separate batch processing, and a CPU (central processing unit) and GPU (graphics processing unit) are optionally used.
  • CPU central processing unit
  • GPU graphics processing unit
  • 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 7 is a conceptual diagram showing a method of analyzing queries and processing data in a data processing system according to an embodiment of the present invention.
  • Figure 8 is a conceptual diagram showing a resource distribution algorithm 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 DBMS (database management system).
  • DBMS 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 able to be easily processed without integration problems 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 processing a nested query includes a step in which the data processing system performs processing on unstructured data based on an extended SQL engine that processes extended structured query language (SQL), and a data processing system that processes PostgreSQL (PostgreSQL). It may include processing structured data based on a general SQL engine that processes structured query language.
  • SQL extended structured query language
  • PostgreSQL PostgreSQL
  • 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 (PostgreSQL) 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, the second query 320, and the 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.) can be created as an unstructured data table converted to a user-defined vector format based on a numerical algorithm.
  • an image file that exists in a specific path can be created on the database as an unstructured data table using an attribute extraction artificial intelligence model.
  • image files that exist in a specific path can be created on the database as a data table using an artificial intelligence model to extract additional attributes.
  • the SEARCH syntax can be used to search for content, meaning, or similarity in unstructured data.
  • Table 3 below is an example of the SEARCH syntax.
  • the 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.
  • Figure 7 is a conceptual diagram showing a method of analyzing queries and processing data in a data processing system according to an embodiment of the present invention.
  • a method that analyzes an input query and processes unstructured data and structured data in one data processing system, but utilizes different processing resources when processing the query.
  • a query for processing unstructured data and/or structured data may be input as an input query.
  • the entered query may be a general query for unstructured data or structured data, or a nested query for mixed processing of unstructured data and structured data.
  • the input query may be parsed through the parser 700.
  • Nested queries are divided into general queries and extended queries, and the parser unit 700 can split the general queries and extended queries.
  • a plurality of queries included in the input query may be analyzed and processed through the cloud analyzer 710 and the query tree 720.
  • a nested query is input as an input query, and the nested query may include a first query 715, a second query 725, and a third query 735.
  • the cloud analyzer 710 may determine which computing resources should be used to process the first query 715, second query 725, and third query 735. More specifically, the cloud analyzer 710 can determine whether the query should be executed on the CPU 770 or the GPU 780 through analysis of a plurality of queries included in the nested query.
  • the cloud analyzer 710 determines the expected execution efficiency and expected execution speed of each of a plurality of queries based on the resource distribution algorithm according to an embodiment of the present invention, and determines the expected execution efficiency and expected execution speed of the plurality of queries based on the expected execution efficiency and expected execution speed.
  • Each can be assigned to the CPU 770 or GPU 780.
  • first query 715 and the third query 735 are extended queries
  • the second query 725 is a general query.
  • the first query 715 and the third query 735 can be processed based on the extended SQL engine 760
  • the second query 725 is a general query
  • the PostgreSQL engine (750) is a SQL engine for general query processing. ) can be processed based on.
  • the first query 715 may be processed based on the GPU 780
  • the third query 735 may be processed based on the CPU 770
  • the second query 725 may be processed based on the GPU 780.
  • the processing speed of nested queries can be improved and computing resources can be utilized more efficiently.
  • This resource distribution algorithm can be applied and utilized to both the PostgreSQL engine (750), a SQL engine for general queries, and the extended SQL engine (760) for extended queries, and modeling is also performed using GPU, providing fast modeling results. can be derived.
  • Figure 8 is a conceptual diagram showing a resource distribution algorithm according to an embodiment of the present invention.
  • a method for determining whether to use the CPU 860 or the GPU 850 when processing an extended query 800 and a general query 820 is disclosed.
  • the first type expansion query 803 which must be processed based on a model that essentially requires the use of the GPU 850 on the expansion engine, can be processed based on the GPU 850.
  • the second type extended query 806, which can be processed without necessarily using the GPU 850 on the expansion engine, is processed by the CPU 860 or It can be processed based on GPU 850.
  • CPU execution ability can be determined based on query processing speed, resource usage, and query cost when processing queries based on CPU.
  • GPU execution ability can be determined based on query processing speed, resource usage, and query cost when processing queries based on GPU.
  • the query cost to determine CPU execution ability is determined based on data processing volume (row processed) and CPU operation cost
  • the query cost to determine GPU execution ability is determined based on data processing volume (row processed) and GPU operation cost.
  • General queries can be processed based on CPU or GPU, considering CPU execution ability and GPU execution ability among computing resources.
  • CPU execution ability and GPU execution ability for extended queries and general queries can be determined by considering the overall cost. The smaller the overall cost, the more advantageous the resource may be.
  • the total cost may be the sum of the start-up cost and run cost. The smaller the overall cost, the more advantageous the resource may be when used.
  • Startup cost may be a cost incurred before the first tuple is fetched.
  • a tuple is a collection of attribute values related to a given list in a database.
  • the startup cost of an index node scan is the cost of reading the index page to access the first tuple of the target table.
  • the run cost may be the cost of accessing all tuples.
  • CPU run cost and GPU run cost can be determined as shown in the equation below.
  • GPU run cost (gpu_tuple_cost + gpu_operator_cost) x N tuple + seq_page_cost x N page
  • CPU run cost (cpu_tuple_cost + cpu_operator_cost) x N tuple + seq_page_cost x N page
  • gpu_tuple_cost is the cost of GPU processing table rows during operation.
  • gpu_operator_cost is the cost for the GPU to process table tuples as operators or functions.
  • cpu_tuple_cost is the cost for the CPU to process table rows during operation.
  • cpu_operator_cost is the cost for the CPU to process table tuples as operators or functions.
  • N tuple is the number of table tuples.
  • seq_page_cost is the cost of retrieving a page.
  • N page is the number of index pages.
  • 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.
  • 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 par attribution de ressources de différents processus, ainsi qu'un système de traitement de données pour fournir le procédé. Le procédé de traitement de données structurées et de données non structurées par attribution de ressources de différents processus comprend les étapes dans lesquelles : un analyseur du système de traitement de données analyse des interrogations imbriquées en interrogations normales et en interrogations étendues ; et un analyseur en nuage du système de traitement de données détermine des ressources informatiques à utiliser pour traiter les interrogations normales et les interrogations étendues, les interrogations imbriquées pouvant être des interrogations dans lesquelles une interrogation étendue pour des données non structurées et une interrogation normale pour des données structurées sont mélangées.
PCT/KR2022/015608 2022-09-29 2022-10-14 Procédé de traitement de données structurées et de données non structurées par attribution de ressources de différents processus, et système de traitement de données pour fournir un procédé WO2024071504A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20050000342A (ko) * 2003-06-23 2005-01-03 마이크로소프트 코포레이션 분산 쿼리 엔진 파이프라인 방법 및 시스템
KR101542195B1 (ko) * 2014-03-28 2015-08-05 주식회사 솔트룩스 비정형 데이터로부터 특성을 추출하여 지식 베이스를 구축하는 시스템 및 방법
KR20160096258A (ko) * 2015-02-04 2016-08-16 주식회사 유누스 데이터베이스 시스템 통합관리방법
CN114116856A (zh) * 2022-01-25 2022-03-01 中电云数智科技有限公司 基于数据治理全链路的字段级血缘关系分析方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20050000342A (ko) * 2003-06-23 2005-01-03 마이크로소프트 코포레이션 분산 쿼리 엔진 파이프라인 방법 및 시스템
KR101542195B1 (ko) * 2014-03-28 2015-08-05 주식회사 솔트룩스 비정형 데이터로부터 특성을 추출하여 지식 베이스를 구축하는 시스템 및 방법
KR20160096258A (ko) * 2015-02-04 2016-08-16 주식회사 유누스 데이터베이스 시스템 통합관리방법
CN114116856A (zh) * 2022-01-25 2022-03-01 中电云数智科技有限公司 基于数据治理全链路的字段级血缘关系分析方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
FLORATOS SOFOKLIS; XIAO MENGBAI; WANG HAO; GUO CHENGXIN; YUAN YUAN; LEE RUBAO; ZHANG XIAODONG: "NestGPU: Nested Query Processing on GPU", 2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), IEEE, 19 April 2021 (2021-04-19), pages 1008 - 1019, XP033930210, DOI: 10.1109/ICDE51399.2021.00092 *

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