KR102538470B1 - Relational database-based graph processing method and apparatus for performing the same - Google Patents

Abstract

According to one embodiment, a data processing device may comprise: a memory containing instructions; and a processor electrically connected to the memory and configured to execute the instructions. When the instructions are executed by the processor, the processor may perform a plurality of operations. The operations may comprise: an operation of receiving a view definition language (VDL), which utilizes a relational database to tabulate and store entities and relationships of an entity-relationship model (ER model); and generating a metagraph, which is metadata in the form of a graph, based on the view definition language.

Description

Relational database-based data processing method and apparatus for performing the same

The disclosure below relates to a relational database-based data processing method and an apparatus for performing the same.

Relational database (RDB, Relational Database), which is currently most used, is a database that guarantees strong consistency in a fixed schema environment. Also, a relational database is a database that shows good performance for relational operations such as aggregation, group by, and sort.

Recently, data in the form of graphs is widely used in the industry. Graph data has characteristics of high connectivity and flexibility. However, relational databases, which are currently most used, are not suitable for managing graph data.

Relational data model has a fixed schema, so it is difficult to process various data types and is not suitable for modeling graph-type data with high degree of connectivity. For this reason, graph databases using graph data models are attracting attention.

However, it is difficult to manage all data only with a graph database. Since there is a lot of data already stored in the relational database, migrating all the data previously stored in the relational database to the graph database requires a lot of migration cost.

A data processing apparatus according to an embodiment includes a memory including instructions; and a processor electrically connected to the memory and executing the instructions. When the instructions are executed by the processor, the processor may perform a plurality of operations. Receiving a view definition language (VDL) using a relational database that stores entities and relationships of an entity-relationship model (ER model) as a table; and generating a metagraph, which is meta data in a graph form, based on the view definition language.

The metagraph may be composed of a vertex corresponding to the entity and an edge corresponding to the relationship. The vertex and the edge may have a label and include a query as a property.

The plurality of operations may further include generating a graph view including vertices and edges by analyzing the metagraph.

The graph view may include a mixed-directed graph supporting directed edges and undirected edges; a property graph in which vertices and edges contain properties; A multi-graph in which a plurality of edges exist for one pair of vertices; and a graph of a polymorphic-labeled graph supporting a plurality of labels for each vertex and edge.

The view definition language includes a syntax for defining a metagraph name; and a syntax for defining vertices and edges of the metagraph based on the names, labels, and properties.

A data processing apparatus according to an embodiment includes a memory including instructions; and a processor electrically connected to the memory and configured to execute the instructions, wherein when the instructions are executed by the processor, the processor may perform a plurality of operations. The plurality of operations may include receiving a graph query; translating the graph query into a structured query language (SQL) query accessible to a relational database, based on a metagraph corresponding to the graph query; and executing the SQL query to return a query result corresponding to the graph query. The metagraph may be metadata in the form of a graph.

The metagraph may be composed of vertices corresponding to entities of the ER model and edges corresponding to relationships between the ER models. The vertex and the edge may have a label and include a query as a property.

The graph query may be based on a graph search language supporting data search in a graph database.

The metagraph may be defined using a view definition language (VDL) that utilizes the relational database.

The view definition language includes a syntax for defining a metagraph name; and a syntax for defining vertices and edges of the metagraph based on the names, labels, and properties.

A data processing method according to an embodiment uses a view definition language (VDL) that utilizes a relational database for storing and tabulating entities and relationships of an ER model (entity-relationship model). receiving action; and generating a metagraph, which is meta data in a graph form, based on the view definition language.

The metagraph may be composed of a vertex corresponding to the entity and an edge corresponding to the relationship. The vertex and the edge may have a label and include a query as a property.

The data processing method may further include generating a graph view including vertices and edges by analyzing the metagraph.

The graph view may include a mixed-directed graph supporting directed edges and undirected edges; a property graph in which vertices and edges contain properties; A multi-graph in which a plurality of edges exist for one pair of vertices; and a graph of a polymorphic-labeled graph supporting a plurality of labels for each vertex and edge.

The view definition language includes a syntax for defining a metagraph name; and a syntax for defining vertices and edges of the metagraph based on the names, labels, and properties.

1 is a schematic block diagram of a data processing apparatus according to an embodiment.
2 shows an example of a relational data and graph view.
3 is a diagram for explaining a relational database.
4 is a diagram for explaining graph types.
5 is a diagram for describing a graph query processing operation according to an exemplary embodiment.
6 is a diagram for explaining a view definition language (VDL) according to an embodiment.
Fig. 7 shows a flow chart of a data processing method according to an exemplary embodiment.
Fig. 8 shows a flow chart of a data processing method according to an embodiment.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only, and may be changed and implemented in various forms. Therefore, the form actually implemented is not limited only to the specific embodiments disclosed, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical idea described in the embodiments.

Although terms such as first or second may be used to describe various components, such terms should only be construed for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element.

It should be understood that when an element is referred to as being “connected” to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle.

Singular expressions include plural expressions unless the context clearly dictates otherwise. In this document, "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C", and "A Each of the phrases such as "at least one of , B, or C" may include any one of the items listed together in that phrase, or all possible combinations thereof. In this specification, terms such as "comprise" or "have" are intended to designate that the described feature, number, step, operation, component, part, or combination thereof exists, but one or more other features or numbers, It should be understood that the presence or addition of steps, operations, components, parts, or combinations thereof is not precluded.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in this specification, it should not be interpreted in an ideal or excessively formal meaning. don't

The term "module" used in this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A module may be an integrally constructed component or a minimal unit of components or a portion thereof that performs one or more functions. For example, according to one embodiment, the module may be implemented in the form of an application-specific integrated circuit (ASIC).

The term '~unit' used in this document means software or a hardware component such as FPGA or ASIC, and '~unit' performs certain roles. However, '~ unit' is not limited to software or hardware. '~bu' may be configured to be in an addressable storage medium and may be configured to reproduce one or more processors. For example, '~unit' includes components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, may include subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Functions provided within components and '~units' may be combined into smaller numbers of components and '~units' or further separated into additional components and '~units'. In addition, components and '~units' may be implemented to play one or more CPUs in a device or a secure multimedia card. Also, '~ unit' may include one or more processors.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. In the description with reference to the accompanying drawings, the same reference numerals are given to the same components regardless of reference numerals, and overlapping descriptions thereof will be omitted.

Fig. 1 is a schematic block diagram of a data processing apparatus according to an embodiment, and Fig. 2 shows an example of relational data and a graph view.

Referring to FIG. 1 , according to an embodiment, the data processing apparatus 100 may generate a graph view based on a relational database (RDB). The relational database may be a database in which entities and relationships of the ER model are tabulated and stored. The graph view may correspond to a graph visually provided to the user. The data processing device 100 may return a graph query processing result based on a relational database.

Referring to FIG. 2 , the data processing device 100 may create a graph view while maintaining a relational database. The data processing device 100 migrates the data 201 included in the relational database to a graph database or physically maps the data 201 included in the relational database to graph data included in the graph database (eg: A graph view 202 can be created without pre-stored data 201 in the relational database being mapped to graph data only when it is newly saved according to a specific rule (e.g., without affecting the data storage method). can The data processing device 100 may utilize a metagraph.

The data processing device 100 may derive the graph view 202 from a relational database by utilizing a metagraph (eg, the metagraph 503 of FIG. 5 ). The data processing device 100 may generate the graph view 202 without migration cost by utilizing a metagraph having a small size. The graph view 202 may correspond to a graph visually provided to the user (eg, the graph view 202 may be visually provided to the user, and other data based on the graph view 202 may be visually provided to the user). may be provided). Graph view 202 may be a virtual graph based on a metagraph. The graph view 202 may include virtual nodes and virtual edges. The graph view 202 may be stored neither in volatile memory nor in non-volatile memory. The data processing apparatus 100 may not be used to convert a relational database into a graph database, but may be used to combine only the advantages of a relational database and graph data.

The data processing apparatus 100 may support modeling of a complex graph based on a complex metagraph. Since a complex metagraph still has a small size, the data processing apparatus 100 can support a graph of high complexity using only a small amount of resources.

The data processing device 100 may create a plurality of metagraphs in advance. The data processing apparatus 100 may support graphs of various views based on a plurality of metagraphs. A metagraph may be defined using a view definition language (VDL) that utilizes a relational database. The metagraph may be metadata in the form of a graph.

The data processing device 100 may generate a graph view including virtual edges and virtual nodes by utilizing a metagraph based on a view definition language without converting a relational database into a graph database based on a fixed logic. . The data processing apparatus 100 may further improve the degree of freedom of data processing by using a view definition language rather than a fixed logic.

The data processing device 100 may create a graph view without migrating or converting data stored in a relational database. The data processing apparatus 100 may support data search based on a graph search language without migration or conversion of data stored in a relational database.

Referring to Table 1, simulation results performed based on the setting of LDBC SNB (Large-Scale Benchmark Council Social Network Benchmark) and scale factor 1 can be confirmed.

[Table 1]

In order for the data processing device 100 to generate a graph view based on 1.4GB of data stored in a relational database (eg, PostgreSQL), only 1MB of additional data (eg, metagraph) (eg, logical mapping information) is required. can When migrating a relational database (eg PostgreSQL) itself to a graph database (eg Neo4j), 1.4GB of additional data is required, and data previously stored in a physical mapping method (eg, relational database must be newly saved according to a specific rule for a graph). Considering that 0.3 GB of additional data is required in case of using the data), the data processing device 100 may utilize the metagraph to provide a graph view without substantially additional overhead.

The data processing device 100 may model a graph by utilizing an external graph database.

The data processing device 100 may be implemented in a personal computer (PC), data server, or portable device.

Portable devices include laptop computers, mobile phones, smart phones, tablet PCs, mobile internet devices (MIDs), personal digital assistants (PDAs), and enterprise digital assistants (EDAs). , digital still camera, digital video camera, portable multimedia player (PMP), personal navigation device or portable navigation device (PND), handheld game console, e-book ( e-book) or a smart device. A smart device may be implemented as a smart watch, a smart band, or a smart ring.

The data processing device 100 may include a processor 110 and a memory 120 .

The processor 110 may process data stored in the memory 120 . The processor 110 may execute computer readable code (eg, software) stored in the memory 120 and instructions triggered by the processor 110 .

The processor 110 may be a data processing device implemented in hardware having a circuit having a physical structure for executing desired operations. For example, desired operations may include codes or instructions included in a program.

For example, a data processing unit implemented in hardware includes a microprocessor, a central processing unit, a processor core, a multi-core processor, and a multiprocessor. , Application-Specific Integrated Circuit (ASIC), and Field Programmable Gate Array (FPGA).

Memory 120 may store relational data and/or metagraphs. The memory 120 may be implemented as a volatile memory device or a non-volatile memory device.

The volatile memory device may be implemented as dynamic random access memory (DRAM), static random access memory (SRAM), thyristor RAM (T-RAM), zero capacitor RAM (Z-RAM), or twin transistor RAM (TTRAM).

Non-volatile memory devices include electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic RAM (MRAM), spin-transfer torque (STT)-MRAM (conductive bridging RAM), and conductive bridging RAM (CBRAM). , FeRAM (Ferroelectric RAM), PRAM (Phase change RAM), Resistive RAM (RRAM), Nanotube RRAM (Polymer RAM (PoRAM)), Nano Floating Gate Memory Memory (NFGM)), holographic memory, molecular electronic memory device (Molecular Electronic Memory Device), or Insulator Resistance Change Memory.

Hereinafter, a relational database will be described with reference to FIG. 3 and graph data will be described with reference to FIG. 4 .

3 is a diagram for explaining a relational database.

Referring to FIG. 3 , data stored in a relational database may be modeled through three steps. A relational database is a set of data items composed of standardized tables, and may be a set of tables having interrelationships. Relational databases can be relatively easy to create and use, and can be easily extensible. After the initial database creation, new data items can be added to the database without changing related applications.

In step 1 (310), ER modeling may be performed first. The ER model 301 may function as a blueprint of a relational database. The ER model 301 may include entities (eg, person, message) and relationships (eg, like, create). An entity (eg person) may include attributes (eg id, name, tag, badge, certified). A relationship (eg, like) may mean a relationship (eg, a 1-1 relationship, a 1-N relationship, an N-1 relationship, or an N-N relationship) between entity pairs (eg, person and message pairs). Relationships (eg like) can include properties (eg date).

In step 2 (320), the ER model (301) may be mapped to a relational model (302). Entities (eg, person, message) and relationships (eg, like, create) of the ER model 301 may each be mapped to one table (eg, person table, message table, like table, create table). A table (eg, person table) corresponding to an entity may include attributes (eg, id, name, tag, badge, certified) of an entity as schema (eg, id, name, tag, badge, certified) . A table corresponding to a relationship (eg, like table) can include key attributes (eg, personId, messageId) of a pair of entities (eg, person, message pair) connected by the relationship as a schema. In addition, the table corresponding to the relationship (eg, like table) can include the attribute of the relationship (eg, date) as a schema.

At step 3 (330), normalization and/or denormalization of the relational model (302) may be performed. For example, there may be constraints such that the number of schemas per table is three. Like the relational data 303, the person table of the relational model 302 can be divided into a person table and a persontag table. The create table of the relational model 302 can be merged with the message table.

Relational data 303 may be stored in the relational database. The relational database may be one in which entities and relationships of an ER model (entity-relationship model) are tabulated and stored.

4 is a diagram for explaining graph types.

Referring to FIG. 4 , graphs may have various characteristics and may be classified into various types. A graph may be a concept in a broad sense including not only a graph in a mathematical sense but also all structures encompassing all fields, for example, a network structure in the computing field. Graphs can be classified according to criterion and/or complexity.

Graphs may be classified according to the presence or absence of directionality. For example, a graph may include an undirected graph containing only undirected edges, a directed graph containing only directed edges, and a mixed directed graph supporting both directed and undirected edges. It can be classified as a mixed-directed graph.

A graph can be classified according to the number of edges connecting a pair of vertices. For example, a graph may be classified into a simple graph in which one edge exists for one vertex pair, and a multi graph in which a plurality of edges exist for one vertex pair.

Graphs can be classified according to whether vertices and/or edges can contain labels. For example, a graph may include an unlabeled graph in which vertices and/or edges do not contain labels, a labeled graph in which vertices and/or edges may contain labels, vertices and/or edges may be classified as a polymorphic-labeled graph that may include a plurality of labels.

Graphs may be classified according to whether vertices and/or edges contain properties. For example, graphs may be classified into a non-property graph in which vertices and/or edges do not contain properties, and a property graph in which vertices and/or edges contain properties. .

A data processing device (eg, the data processing device 100 of FIG. 1 ) may support all of the graphs shown in FIG. 4 . However, the type of graph model supported by the data processing apparatus 100 is not limited thereto. The data processing apparatus 100 may support the high-complexity graph shown in FIG. 4 using only a small number of resources (eg, a metagraph). Hereinafter, graph query processing operations using metagraph will be described in detail.

5 is a diagram for describing a graph query processing operation according to an exemplary embodiment, and FIG. 6 is a diagram for describing a view definition language (VDL) according to an exemplary embodiment.

Referring to FIG. 5, according to an embodiment, a data processing apparatus (eg, the data processing apparatus 100 of FIG. 1) stores entities and relationships of an ER model (entity-relationship model) as a table in a relational database. Based on this, it is possible to support data retrieval based on a graph search language.

The data processing device 100 may receive the graph query 501 . Graph query 501 may be based on a graph navigation language that supports data retrieval in a graph database. For example, graph search languages may include C ypher, SPARQL, Gremlin, and the like, and graph search languages are not limited thereto. The graph query 501 is performed by the user based on the graph view 504 (eg, to acquire additional information after visually checking the graph view 504 (or other data corresponding to the graph view 504)) It may be input to the processing device 100 .

The data processing device 100 may translate the graph query 501 into a structured query language (SQL) query 502 accessible to a relational database, based on the pre-generated metagraph 503 . The metagraph 503 may be metadata in the form of a graph. The metagraph 503 may be any one of a plurality of pre-generated metagraphs. The metagraph 503 may correspond to the graph query 501. A metagraph 503 may correspond to a graph query 501 for finding a person who likes a message created by a celebrity. The metagraph 503 may be stored in a memory (eg, the memory 120 of FIG. 1 ). The metagraph stored in the memory 120 is not limited to the metagraph 503 of FIG. 5 . For example, a metagraph (not shown) for generating graph data (eg graph name: friends_network, vertex: person, edge: friends) showing friendship relationships between people is created and stored in the memory 120. There may be. As another example, a metagraph (not shown) for generating graph data (graph name: works_network, vertex: person, edge: work_at) showing information about a person's workplace is created and stored in the memory 120. may have been The graph view 504 (eg, graph data) may be stored neither in volatile memory nor in non-volatile memory.

)에 기초하여, 메타그래프(503)의 정점을 정의하는 구문(예: CREATE VERTEX)을 포함할 수 있다. 뷰 정의 언어는 이름(예: view 1), 레이블(예: (Person)-[Create]->(Message)) 및 프로퍼티 쿼리(property query)(예:

)에 기초하여 메타그래프(503)의 엣지를 정의하는 구문(예: CREATE EDGE)을 포함할 수 있다. 데이터 프로세싱 장치(100)는 고정된 로직이 아닌 뷰 정의 언어를 활용함으로써, 데이터 프로세싱의 자유도를 더욱 향상시킬 수 있다.Referring to FIG. 6 , a metagraph 503 may be defined using a view definition language (VDL) that utilizes a relational database. The view definition language may include a syntax (eg, CREATE GRAPH) defining a name (eg, view 1) of the metagraph 503 . The view definition language includes names (e.g. view 1), labels (e.g. [Celeb, Person]), and property queries (e.g. [Celeb, Person]).

), a syntax (eg, CREATE VERTEX) for defining vertices of the metagraph 503 may be included. The view definition language includes names (e.g. view 1), labels (e.g. (Person)-[Create]->(Message)) and property queries (e.g.

) may include a syntax (eg, CREATE EDGE) that defines an edge of the metagraph 503 based on. The data processing apparatus 100 may further improve the degree of freedom of data processing by using a view definition language rather than a fixed logic.

The data processing unit 100 executes a SQL query 502 (eg, by accessing a relational database) to respond to a graph query 501 (eg, a graph query to find people who liked a message generated by a celebrity). A query result 505 (eg, James, Nancy, John) may be returned.

The data processing device 100 may create a graph view while maintaining a relational database. The data processing device 100 may generate the graph data 504 without migrating data included in the relational database to the graph database.

The data processing device 100 may utilize the metagraph 503 to derive a query result 505 from a relational database.

7 and 8 each show a flow chart of a data processing method according to an embodiment.

Referring to FIG. 7 , according to an embodiment, operations 710 to 730 may be sequentially performed, but are not limited thereto. For example, two or more operations may be performed in parallel. Operations 710 to 730 may be substantially the same as those of the data processing device (eg, the data processing device 100 of FIG. 1 ) described with reference to FIGS. 1 to 6 . A redundant description thereof will be omitted.

In operation 710, the data processing apparatus 100 may receive a view definition language that utilizes a relational database that stores entities and relationships of an ER model (entity-relationship model) as a table.

In operation 720, the data processing device 100 may generate a metagraph, which is meta data in a graph form, based on the view definition language.

In operation 730, the data processing device 100 may generate a graph view including vertices and edges by interpreting the metagraph (eg, executing queries included in the metagraph).

Referring to FIG. 8 , according to an embodiment, operations 810 to 830 may be sequentially performed, but are not limited thereto. For example, two or more operations may be performed in parallel. Operations 810 to 830 may be substantially the same as those of the data processing apparatus 100 described with reference to FIGS. 1 to 6 . Duplicated explanations will be omitted.

In operation 810, the data processing device 100 may receive a graph query.

In operation 820, the data processing device 100 may translate the graph query into a structured query language (SQL) query accessible to a relational database based on a metagraph corresponding to the graph query.

In operation 830, the data processing device 100 may execute the SQL query and return a query result corresponding to the graph query.

The embodiments described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic units (PLUs), microprocessors, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. The device can be commanded. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. A computer readable medium may store program instructions, data files, data structures, etc. alone or in combination, and program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in the art of computer software. there is. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler.

The hardware device described above may be configured to operate as one or a plurality of software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with limited drawings, those skilled in the art can apply various technical modifications and variations based on this. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (16)

memory containing instructions; and
a processor electrically connected to the memory and configured to execute the instructions;
When the instructions are executed by the processor, the processor performs a plurality of operations;
The plurality of operations,
Receiving a view definition language (VDL) using a relational database that stores entities and relationships of an entity-relationship model (ER model) as a table; and
An operation of generating a metagraph, which is meta data in a graph form, based on the view definition language.
including,
The view definition language,
A syntax defining the name of the metagraph; and
A syntax for defining vertices and edges of the metagraph based on at least one of direction, label, or query;
The direction is implemented as the direction of the edge, the label is implemented as a label of the vertex or a label of the edge,
The query is implemented as a property of the vertex or a property of the edge,
The query includes a query for extracting attributes for each table of the ER model stored in the relational database,
The vertex corresponds to an entity of the ER model,
The edge corresponds to the relationship of the ER model,
data processing device.
delete According to claim 1,
The plurality of operations,
An operation of interpreting the metagraph to create a graph view including vertices and edges
Further comprising a, data processing device.
According to claim 3,
The graph view,
a mixed-directed graph supporting directed and undirected edges;
a property graph in which vertices and edges contain properties;
A multi-graph in which a plurality of edges exist for one pair of vertices; and
A polymorphic-labeled graph that supports multiple labels for each vertex and edge.
A data processing device, comprising a graph of the type
delete memory containing instructions; and
a processor electrically connected to the memory and configured to execute the instructions;
When the instructions are executed by the processor, the processor performs a plurality of operations;
The plurality of operations,
receiving a graph query;
translating the graph query into a structured query language (SQL) query accessible to a relational database, based on a metagraph corresponding to the graph query; and
An operation of executing the SQL query and returning a query result corresponding to the graph query.
including,
The metagraph,
It is metadata in the form of a graph,
The metagraph,
It is defined using a view definition language (VDL) that utilizes the relational database,
The view definition language,
A syntax defining the name of the metagraph; and
A syntax for defining vertices and edges of the metagraph based on at least one of direction, label, or query;
The direction is implemented as the direction of the edge,
The label is implemented as a label of the vertex or a label of the edge,
The query is implemented as a property of the vertex or a property of the edge,
The query includes a query for extracting attributes for each table of the ER model stored in the relational database,
The vertex corresponds to an entity of the ER model,
The edge corresponds to the relationship of the ER model,
data processing device.
delete According to claim 6,
The graph query,
based on a graph navigation language that supports data retrieval in graph databases;
data processing device.
delete delete Receiving a view definition language (VDL) using a relational database that stores entities and relationships of an entity-relationship model (ER model) as a table; and
An operation of generating a metagraph, which is meta data in a graph form, based on the view definition language.
including,
The view definition language,
A syntax defining the name of the metagraph; and
A syntax for defining vertices and edges of the metagraph based on at least one of direction, label, or query;
The direction is implemented as the direction of the edge,
The label is implemented as a label of the vertex or a label of the edge,
The query is implemented as a property of the vertex or a property of the edge,
The query includes a query for extracting attributes for each table of the ER model stored in the relational database,
The vertex corresponds to an entity of the ER model,
The edge corresponds to the relationship of the ER model,
Data processing method.
delete According to claim 11,
An operation of interpreting the metagraph to create a graph view including vertices and edges
Further comprising, data processing method.
According to claim 13,
The graph view,
a mixed-directed graph supporting directed and undirected edges;
a property graph in which vertices and edges contain properties;
A multi-graph in which a plurality of edges exist for one pair of vertices; and
A polymorphic-labeled graph that supports multiple labels for each vertex and edge.
A data processing method comprising a graph of the type
delete A computer program stored in a computer readable recording medium in order to execute the method of any one of claims 11, 13 and 14 in combination with hardware.

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