WO2024019225A1

WO2024019225A1 - Method for processing structured data and unstructured data in a plurality of different databases, and data processing platform providing same method

Info

Publication number: WO2024019225A1
Application number: PCT/KR2022/014150
Authority: WO
Inventors: 이상수; 임정택; 윤준영
Original assignee: 스마트마인드 주식회사
Priority date: 2022-07-21
Filing date: 2022-09-22
Publication date: 2024-01-25
Also published as: KR102605931B1

Abstract

The present invention relates to a method for processing structured data and unstructured data in a plurality of different databases, and a data processing platform providing the method. The method for processing structured data and unstructured data in a plurality of different databases may comprise: a step in which a data processing system receives external data from an external database; a step in which the data processing system converts the external data; and a step in which the data processing system processes the converted external data.

Description

Methods for processing structured and unstructured data on multiple different databases and a data processing platform that provides such methods

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. .

Due to the rapid non-face-to-face environment and mobile-first strategy, the explosive increase and creation of structured and unstructured data every year is demanding new decisions and services utilizing big data in all fields.

As such, the rapid increase and consumption of data is expected to accelerate further in the future, and finding future growth engines by collecting, refining and analyzing various patterns contained in not only structured data but also unstructured data will become a new business model for companies. It is becoming.

Existing prior art includes domestic application number 10-2014-0036626.

The purpose of the present invention is to solve all of the above-mentioned problems.

Additionally, 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.

In addition, 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.

According to an embodiment of the present invention, 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.

Meanwhile, 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, and 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, and the structured data processing query may be a query for processing only the structured data.

In addition, 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.

According to another embodiment of the present invention, 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.

Meanwhile, 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, and 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, and the structured data processing query may be a query for processing only the structured data.

According to the present invention, structured data and unstructured data can be processed using one language based on extended SQL (structured query language) and one platform.

In addition, according to the present invention, 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. 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.

The detailed description of the present invention described below refers to the accompanying drawings, which show by way of example specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that the various embodiments of the invention are different from one another but are not necessarily mutually exclusive. For example, specific shapes, structures and characteristics described herein may be implemented with changes from one embodiment to another without departing from the spirit and scope of the invention. Additionally, it should be understood that the location or arrangement of individual components within each embodiment may be changed without departing from the spirit and scope of the present invention. Accordingly, the detailed description described below is not intended to be limited, and the scope of the present invention should be taken to encompass the scope claimed by the claims and all equivalents thereof. Like reference numbers in the drawings indicate identical or similar elements throughout various aspects.

Hereinafter, several preferred embodiments of the present invention will be described in detail with reference to the attached drawings in order to enable those skilled in the art to easily practice the present invention.

Figure 1 is a conceptual diagram showing an existing data processing system.

In Figure 1, a data processing system that processes existing structured data and unstructured data is disclosed.

Referring to FIG. 1, 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).

In addition, 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. In existing database processing systems, 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).

Additionally, existing data processing systems used various pipelines, various frameworks, and various languages for batch processing of unstructured data 120. Therefore, processing of data based on a single governance was impossible, and maintenance after development was difficult.

Additionally, 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.

In addition, when performing modeling for an AI engine, the existing data processing system 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.

In existing data processing systems, it takes a lot of time to perform AI modeling using sample data.

In existing data processing systems, the process of extracting sample data from a parameter table is performed. Because 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.

In addition, in the AI modeling process of existing data processing systems, sample data includes both structured and unstructured data, and in order to perform structured/unstructured AI modeling, 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.

In addition, while extracting data from the parameter table and doing AI modeling on the Lambda architecture, new data is accumulated in the parameter table/data in real time. Then, when applying the AI model created in the existing data processing system, the prediction result (model's There is a problem that the result value is not accurate. In that case, it takes a lot of time to do modeling again, going through

processes

1 and 2.

When the data processing system according to the embodiment of the present invention is used, 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.

Therefore, the data processing platform according to an embodiment of the present invention can process structured data 100 and unstructured data 120 based on one language based on one platform.

In addition, the data processing platform according to an embodiment of the present invention 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 .

Hereinafter, the functions of the data processing platform according to a more specific embodiment of the present invention are disclosed.

In Figure 2, a data processing system for processing structured data and unstructured data on one platform is disclosed.

Referring to FIG. 2, the data processing system is capable of processing unstructured data 220 and structured data 210 on one platform. In the present invention, 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.

Unlike existing data processing systems, nested queries (230) are possible based on the extended SQL engine (250). 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.

That is, unlike the existing structured data 210 and unstructured data 220 that are processed based on separate DMBS (database management system), in the present invention, 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.

In addition, 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.

That is, according to an embodiment of the present invention, 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.

Additionally, according to an embodiment of the present invention, 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, and 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.

In Figure 3, a previously defined general query and an extended query defined based on extended SQL for unstructured data form a nested query, and a method of processing the nested query in a data processing system is disclosed.

Referring to FIG. 3, a nested query for processing unstructured data and structured data may be input as the input query 300.

For example, 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, and 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, and 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.

In Figure 4, an extended SQL query function for simultaneously processing structured data and unstructured data on one platform is disclosed.

Referring to FIG. 4, the query function for unstructured data can be performed based on the extended SQL below.

(1) Check storage model (LIST) (410)

Users can use the "LIST" syntax to check pre-built models and user-created models for unstructured data tables for processing unstructured data.

For example, it is possible to check user-generated models created by users through the LIST MODEL function, and it is possible to check pre-created models using the LIST PREBUILT MODEL function.

(2) Unstructured data conversion (create table) (420)

Using the "create table" syntax, 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.

Table 1 below is an example of create table syntax.

CREATE TABLE [name of custom data table]

USING [AI model to use]

AS [dataset to use]

For example, using the create table function, 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.

(3) Add unstructured characteristics (convert using) (430)

Using the "convert using" statement, users can use information from unstructured data such as images, videos, and voices to convert it into vector format using a numerical algorithm and add this value to the data set to be used.

Table 2 below is an example of the convert using statement.

CONVERT USING [AI model to use]

OPTIONS(

Table_name=[table name to be saved]

)

AS

[Dataset to use]

For example, by using the convert using function, 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.

(4) Unstructured data search (440)

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.

SEARCH [custom data table name]

USING [AI model to use]

AS [dataset to use]

For example, a search statement can be used to search for similar images based on an image quantification artificial intelligence model.

(5) Print result (PRINT) (450)

Users can output image, audio, and video files using the "PRINT" syntax. Additionally, you can use a subquery to immediately output the results obtained through the "PRINT" statement.

Table 4 below is an example of the "PRINT" syntax.

PRINT IMAGE, AUDIO, VIDEO

AS [data set to output]

For example, you can use the PRINT query statement to output image files/video files/audio files in a data table.

The above query syntax is a newly defined syntax for SQL confirmed in the present invention.

It is possible to 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. In addition, it is possible to search image data, audio data, and video data based on image data, audio data, and video data.

That is, in the data processing system according to an embodiment of the present invention, real-time search for the above unstructured data is possible in addition to real-time search for existing structured data. In addition, based on the above extended SQL, 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.

In Figure 5, the ML (machine learning) function of extended SQL for simultaneously processing structured data and unstructured data on one platform is disclosed.

Referring to FIG. 5, ML functions for unstructured data can be performed based on extended SQL as shown below.

(1) Model learning (BUILD MODEL) (510)

Users can develop artificial intelligence models using the “BUILD MODEL” statement.

Table 5 below is an example of the “BUILD MODEL” syntax.

BUILD MODEL [custom model name]

USING [Artificial intelligence model to use]

OPTIONS([Option values required when creating an artificial intelligence model])

AS [dataset to use]

For example, a user can use the "BUILD MODEL" syntax to create a movie recommendation model that recommends movies using an artificial intelligence model.

(2) EVALUATE USING (520)

Users can perform performance evaluation of artificial intelligence models using the “EVALUATE USING” statement.

Table 6 below is an example of the "EVALUATE USING" statement.

EVALUATE USING [Name of previously learned model]

OPTIONS ([Option values required when evaluating each model])

AS

[Dataset to use]

For example, the "EVALUATE USING" statement can be used to evaluate the classification model that the user created in Learning a Model.

(3) Model retraining (FIT MODEL) (530)

Users can use the "FIT MODEL" syntax to perform training based on newly added datasets to the model.

Table 7 below is an example of the “FIT MODEL” syntax.

FIT MODEL [custom model name]

USING [Name of previously learned model | Pre-trained artificial intelligence model name]

OPTIONS ([Option values required when creating an artificial intelligence model])

AS

[Dataset to use]

For example, using “FIT MODEL”, a new model can be created that is trained using a newly added dataset to a model the user previously created.

(4) Data preprocessing (TRANSFORM USING) (540)

Users can use the "TRANSFORM USING" statement to apply the same preprocessing method used to create the artificial intelligence model to the test data set.

Table 8 below is an example of the "TRANSFORM USING" syntax.

TRANSFORM USING [Name of previously learned model]

AS

[Test dataset to use]

For example, in learning a model using the "TRANSFORM USING" syntax, data preprocessing used in an existing classification model can be applied to data preprocessing of a data set for learning another model.

(5) Applying the model (PREDICT USING) (550)

Users can use the "PREDICT UDING" syntax to apply artificial intelligence models to test data sets to perform tasks such as prediction, classification, and recommendations.

Table 9 below is an example of the "PREDICT UDING" syntax.

PREDICT USING [Previously learned model name]

OPTIONS ([Option values required for inference for each model])

AS

[Test dataset to use]

For example, using the “PREDICT USING” syntax, it is possible to recommend a list of movies that the user with user ID 31 might like using the existing recommendation model created in the previous model training.

(6) Deleting a model (DELETE MODEL) (560)

Users can delete models created in the database using the "DELETE MODEL" statement.

Table 10 below is an example of the “DELETE MODEL” statement.

DELETE MODEL [model name to delete]

For example, the movie recommendation model that the user created in model training based on the "DELETE MODEL" statement may be deleted from the database.

Based on the above extended SQL, 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.

In the data processing system, a pre-generated AI model and an AI model created by a user may be located. Through this AI model creation, various AI models such as classification models, regression models, recommendation systems, and voice recognition models can be created.

In Figure 6, a method of processing data on a separate database based on the data processing system described above is disclosed.

Referring to FIG. 6, as described above with reference to FIGS. 1 to 5, processing of structured data and unstructured data may be performed based on the data processing system's own database. However, 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.

In the case of internal data processing, it can be processed based on the process disclosed in FIGS. 1 to 5 described above.

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'. For data that has been stored and converted, 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.

In the case of external data processing, 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.

For example, a specific user may be a security company and operate a user database that stores CCTV footage. Based on the extended SQL of the data processing system of the present invention, 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.

That is, 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.

At this time, the external data includes structured data and unstructured data, the data processing system processes structured data and unstructured data based on nested queries, and 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, and a structured data processing query is a query for processing only structured data. It could be a query for

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.

In the above, the present invention has been described in terms of specific details, such as specific components, and limited embodiments and drawings, but this is only provided to facilitate a more general understanding of the present invention, and the present invention is not limited to the above embodiments. Anyone with ordinary knowledge in the technical field to which the invention pertains can make various modifications and changes from this description.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and the scope of the patent claims described below as well as all scopes equivalent to or equivalently changed from the scope of the claims are within the scope of the spirit of the present invention. It will be said to belong to

Claims

Methods for processing structured and unstructured data on multiple different databases include:

A data processing system receiving external data from an external database;

converting the external data by the data processing system;

and processing the converted external data by the data processing system.
According to paragraph 1,

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 an unstructured data processing query,

The data processing system processes the structured data based on a structured data processing query,

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 is a query for processing only the unstructured data,

The structured data processing query is a query for processing only the structured data.
According to clause 2,

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,

The data processing system supports artificial intelligence engine modeling based on the structured data and the unstructured data on the one database.
A data processing system that processes structured and unstructured data on multiple different databases,

Receive external data from an external database,

Convert the external data,

A data processing system characterized in that it is implemented to process the converted external data.
According to clause 4,

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 an unstructured data processing query,

The data processing system processes the structured data based on a structured data processing query,

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 is a query for processing only the unstructured data,

A data processing system, characterized in that the structured data processing query is a query for processing only the structured data.
According to clause 5,

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,

The data processing system supports artificial intelligence engine modeling based on the structured data and the unstructured data on the one database.