Graph search device, graph search system, and graph search method

Abstract

To provide an accurate graph device, a graph search system, and a graph search method that can provide a reliable search result.SOLUTION: A graph search device 210 includes: an existing information database 232 that stores existing information including a set of existing process graphs and a set of existing process tables; a query data management unit 222 that acquires query data including a query graph and a query table; a structure comparison unit 224 that determines, from the set of existing process graphs, an existing process graph satisfying a predetermined structural similarity threshold for the query graph as a first search candidate; a detail comparison unit 226 that determines, from the set of existing process tables, an existing process graph associated with an exiting process table satisfying a predetermined detail similarity threshold for the query table as a second search candidate; and a result management unit 228 that determines a search result data for the query data from the first search candidate and the second search candidate.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a graph search device, a graph search system, and a graph search method. [Background technology]

[0002] In the manufacturing industry, products are produced by processes that consist of various manufacturing steps. As an example, chemicals produced in a chemical plant may be produced by a process that consists of manufacturing steps such as processing, blending, heating, etc.

[0003] To visualize and manage such processes, a graphical representation is a useful tool. In a graphical representation of a product, each step in producing the product can be represented as a series of connected nodes. Such a graphical representation may also be associated with a table that stores information about each node in the graph.

[0004] By using the graphs and tables described above to manage the manufacturing process of products, it is easy to grasp the overall flow of the manufacturing process of various products and information about each process in the manufacturing process. Furthermore, by analyzing such graphs, new insights can be gained about the relationships between the nodes in the graph.

[0005] One means of analyzing graphs is described, for example, in U.S. Patent Application Publication No. 2022 / 0247662 (Patent Document 1). Patent Document 1 discloses a technique for determining predictions from a graph of a network dataset. The graph of the network dataset may include nodes representing entities and edges representing connections or links between the entities. Predictions can be made by using a dual-path convolutional network that considers both node connectivity and node topology. Node topology includes an assessment of the similarity of topological roles between nodes in the graph, even for nodes that reside in different parts of the graph. A multi-head attention network can be used to align node connectivity and node topology in the dual-path convolution. Outputs from a previous layer of the multi-head attention network may be provided as inputs to subsequent layers of the dual-path convolution to mutually reinforce the convolutions that determine node connectivity and node topology toward alignment. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] US Patent Application Publication No. 2022 / 0247662 Summary of the Invention [Problem to be solved by the invention]

[0007] In the above-mentioned manufacturing industry, when designing a manufacturing process for a new product, it may be desirable to refer to the manufacturing process of an existing product. If an existing product whose manufacturing process is similar to that of the new product being designed can be identified, the manufacturing process of the existing product can be used to efficiently design the manufacturing process for the new product. Thus, there is a need for a means to search for existing manufacturing processes for products, which may be stored, for example, in a graph format.

[0008] The above-mentioned Patent Document 1 relates to making predictions based on graphs of network data sets related to computer-implemented services. As an example, when a graph shows the relationship between buyers and sellers in a trading relationship, it is said that by using the graph analysis means described in Patent Document 1, it is possible to predict sellers who are at high risk of revenue loss. In addition, the graph analysis method described in Patent Document 1 evaluates the similarity of the graphs.

[0009] In order to calculate the graph similarity with high accuracy, it is desirable to take into account various features of the graph. However, the graph analysis method described in Patent Document 1 uses a graph similarity that takes into account the connectivity between nodes in the graph and the topology (structure) of the graph, but does not assume a graph search that takes into account the similarity of information about each node in the graph (for example, table information stored in a table associated with the graph).

[0010] For this reason, for highly complex graphs associated with various pieces of information about the manufacturing process of a product, for example, the accuracy of the graph analysis and graph similarity techniques described in Patent Document 1 is limited, and reliable search results may not be obtained.

[0011] Therefore, an object of the present disclosure is to provide a highly accurate graph search means capable of providing reliable search results by using a similarity comparison that takes into account not only the graph structure but also table information stored in a table associated with the graph. [Means for solving the problem]

[0012] In order to solve the above problem, one representative graph search device of the present invention comprises a processor, a memory, and a storage unit, the storage unit includes an existing information database that stores existing information including a set of existing process graphs that show process flows in a graph format, and a set of existing process tables that are associated with the set of existing process graphs and store information related to the set of existing process graphs in a tabular format, the memory includes a query data management unit that acquires query data that is a search query for the existing information and includes a query graph and a query table, and a query data management unit that calculates a structural similarity between the query graph and the set of existing process graphs by comparing the query graph included in the query data with the set of existing process graphs, and a pre-processing unit that calculates a structural similarity between the query graph and the set of existing process graphs. The system includes a structural comparison unit that determines, from the set of existing process graphs, an existing process graph that satisfies a predetermined structural similarity threshold with respect to the query graph as a first search candidate, a content comparison unit that calculates a content similarity between the query graph and the set of existing process tables by comparing the query table included in the query data with the set of existing process tables, and determines, from the set of existing process tables, an existing process graph that satisfies a predetermined content similarity threshold with respect to the query table as a second search candidate, and a processing instruction for causing the processor to function as a result management unit that determines and outputs search result data for the query data from among the first search candidates and the second search candidates based on a predetermined comprehensive evaluation criterion. Effect of the Invention

[0013] According to the present disclosure, by using a similarity comparison that takes into account not only the graph structure but also table information stored in a table associated with the graph, it is possible to provide a highly accurate graph search means that can provide reliable search results. Other objects, configurations and effects will become apparent from the following description of the preferred embodiment of the invention. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 illustrates a computer system for implementing an embodiment of the present disclosure. [Diagram 2] FIG. 2 is a diagram illustrating an example of a configuration of a graph search system according to an embodiment of the present disclosure. [Diagram 3] FIG. 3 is a diagram illustrating an example of a manufacturing process according to an embodiment of the present disclosure. [Figure 4] FIG. 4 is a diagram illustrating an example of query data and existing information according to an embodiment of the present disclosure. [Diagram 5] FIG. 5 is a diagram showing the correspondence between a graph and a table according to an embodiment of the present disclosure. [Figure 6] FIG. 6 is a diagram illustrating an example of various databases stored in the storage unit according to the embodiment of the present disclosure. [Figure 7] FIG. 7 is a diagram showing an example of an overall flow of a manufacturing process in which a graph search means according to an embodiment of the present disclosure is used. [Figure 8] FIG. 8 is a diagram illustrating an example of the flow of a graph search process according to an embodiment of the present disclosure. [Figure 9] FIG. 9 is a diagram showing an example of the flow of the structural similarity comparison process according to the embodiment of the present disclosure. [Figure 10] FIG. 10 is a diagram illustrating an example of the flow of a content similarity comparison process according to an embodiment of the present disclosure. [Figure 11] FIG. 11 is a diagram illustrating an example of a query data setting screen according to an embodiment of the present disclosure. [Figure 12] FIG. 12 is a diagram illustrating an example of a search result confirmation screen according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that the present invention is not limited to the embodiment. In addition, in the description of the drawings, the same parts are denoted by the same reference numerals. In addition, although terms such as "first," "second," and "third" may be used in the present disclosure to describe various elements or components, it will be understood that these elements or components should not be limited by these terms. These terms are used only to distinguish one element or component from another element or component. Thus, a first element or component discussed below can also be referred to as a second element or component without departing from the teachings of the inventive concept.

[0016] First, referring to Fig. 1, a computer system 100 for implementing an embodiment of the present disclosure will be described. The mechanisms and devices of the various embodiments disclosed herein may be applied to any suitable computing system. The main components of the computer system 100 include one or more processors 102, memory 104, a terminal interface 112, a storage interface 113, an I / O (input / output) device interface 114, and a network interface 115. These components may be interconnected via a memory bus 106, an I / O bus 108, a bus interface unit 109, and an I / O bus interface unit 110.

[0017] Computer system 100 may include one or more general purpose programmable central processing units (CPUs) 102A and 102B, collectively referred to as processors 102. In some embodiments, computer system 100 may include multiple processors, while in other embodiments, computer system 100 may be a single CPU system. Each processor 102 executes instructions stored in memory 104 and may include an on-board cache.

[0018] In one embodiment, memory 104 may include random access semiconductor memory, storage devices, or storage media (either volatile or non-volatile) for storing data and programs. Memory 104 may store all or part of the programs, modules, and data structures that implement the functions described herein. For example, memory 104 may store a graph search application 150. In one embodiment, graph search application 150 may include instructions or descriptions that execute on processor 102 the functions described below.

[0019] In some embodiments, graph search application 150 may be implemented in hardware via semiconductor devices, chips, logic gates, circuits, circuit cards, and / or other physical hardware devices instead of or in addition to a processor-based system. In some embodiments, graph search application 150 may include data other than instructions or descriptions. In some embodiments, cameras, sensors, or other data input devices (not shown) may be provided to communicate directly with bus interface unit 109, processor 102, or other hardware of computer system 100.

[0020] Computer system 100 may include a bus interface unit 109 that provides communication between processor 102, memory 104, display system 124, and I / O bus interface unit 110. I / O bus interface unit 110 may couple to an I / O bus 108 for transferring data to and from various I / O units. I / O bus interface unit 110 may communicate via I / O bus 108 with multiple I / O interface units 112, 113, 114, and 115, also known as I / O processors (IOPs) or I / O adapters (IOAs).

[0021] Display system 124 may include a display controller, a display memory, or both. The display controller may provide video, audio, or both data to display device 126. Computer system 100 may also include one or more sensors or other devices configured to collect data and provide the data to processor 102.

[0022] For example, computer system 100 may include biometric sensors to collect heart rate data, stress level data, etc., environmental sensors to collect humidity data, temperature data, pressure data, etc., and motion sensors to collect acceleration data, movement data, etc. Other types of sensors may also be used. Display system 124 may be connected to a display device 126, such as a standalone display screen, a television, a tablet, or a handheld device.

[0023] The I / O interface unit provides the ability to communicate with various storage or I / O devices. For example, the terminal interface unit 112 may be attached to user I / O devices 116, such as user output devices, such as a video display, a television with speakers, and user input devices, such as a keyboard, a mouse, a keypad, a touchpad, a trackball, buttons, a light pen, or other pointing device. A user may use a user interface to enter input data or instructions to the user I / O devices 116 and the computer system 100, and receive output data from the computer system 100, by manipulating the user input devices. The user interface may be displayed on a display, played through speakers, or printed via a printer, for example, via the user I / O devices 116.

[0024] Storage interface 113 allows attachment of one or more disk drives or direct access storage device 117 (usually a magnetic disk drive storage device, but may be an array of disk drives or other storage devices configured to appear as a single disk drive). In some embodiments, storage device 117 may be implemented as any secondary storage device. Contents of memory 104 may be stored in storage device 117 and retrieved from storage device 117 as needed. I / O device interface 114 may provide an interface to other I / O devices such as printers, fax machines, etc. Network interface 115 may provide a communications path to allow computer system 100 and other devices to communicate with each other. This communications path may be, for example, network 130.

[0025] In one embodiment, computer system 100 may be a device that receives requests from other computer systems (clients) without a direct user interface, such as a multi-user mainframe computer system, a single-user system, or a server computer, etc. In other embodiments, computer system 100 may be a desktop computer, a portable computer, a laptop, a tablet computer, a pocket computer, a telephone, a smartphone, or any other suitable electronic device.

[0026] Next, a graph search system according to an embodiment of the present disclosure will be described with reference to FIG.

[0027] 2 is a diagram illustrating an example of a configuration of a graph search system 200 according to an embodiment of the present disclosure. The graph search system 200 is a system for searching for graphs that have a high similarity to a given process expressed in a graph format and providing the graphs to a user. As shown in FIG. 2, the graph search system 200 includes a graph search device 210, a communication network 250, and a user terminal 260. In the graph search system 200, the graph search device 210 and the user terminal 260 may be connected to each other via the communication network 250.

[0028] The graph search device 210 is a device for searching for graphs corresponding to processes that have a high similarity to a given process expressed in graph format, and as shown in FIG. 2, mainly includes a memory 220, a storage unit 230, a processor 244, and an input / output unit 246. In one embodiment, the graph search device 210 may be implemented by the computer system 100 shown in FIG.

[0029] The memory 220 may be a memory for storing a graph search application 150 for implementing the functions of a graph search means according to an embodiment of the present disclosure. The graph search application 150 may include processing instructions for implementing the functions of software modules such as a query data manager 222, a structure comparator 224, a content comparator 226, and a result manager 228, as shown in FIG.

[0030] The query data management unit 222 is a functional unit for acquiring and managing query data indicating a search query for existing information stored in an existing information database 232 described later. This query data may be information for searching for a graph having a high similarity to a predetermined process expressed in a graph format, and may be input by a user via a user terminal 260 described later. In addition, this query data may include a query graph indicating the predetermined process in a graph format, and a query table indicating information related to the query graph in a tabular format.

[0031] The structural comparison unit 224 is a functional unit for calculating the structural similarity between a query graph included in the query data acquired by the query data management unit 222 and a set of existing process graphs included in existing information stored in the existing information database 232 described below. In the present disclosure, "structural similarity" refers to similarity taking into consideration structural features such as the positions of the nodes constituting the graph, the connections between the nodes, and the paths in the graph. In an embodiment, the structural similarity in the present disclosure may be calculated by a predetermined graph analysis method or a predetermined natural language processing.

[0032] The content comparison unit 226 is a functional unit for calculating the content similarity between a query table included in the query data acquired by the query data management unit 222 and a set of existing process tables included in the existing information stored in the existing information database 232 described below. In this disclosure, "content similarity" refers to a similarity that takes into account the syntactic and semantic features of table information that stores information related to a graph in a tabular form. In an embodiment, the content similarity in this disclosure may be calculated by a predetermined natural language processing.

[0033] The result management unit 228 is a functional unit that determines search result data for the query data acquired by the query data management unit 222, based on the similarity comparison by the above-mentioned structure comparison unit 224 and content comparison unit 226. In an embodiment, the result management unit 228 may determine the search result data by evaluating the search candidates determined by the structure comparison unit 224 and content comparison unit 226, based on a predetermined comprehensive evaluation criterion.

[0034] The memory unit 230 is a memory area that contains a database (hereinafter referred to as "DB") for storing various information related to an embodiment of the present disclosure, and may include a process DB 231, an existing information DB 232, a graph similarity information DB 233, and a search result DB 234, as shown in FIG. 2.

[0035] The process DB 231 is a database that stores information about existing processes.

[0036] The existing information DB 232 is a database that stores existing information for which a search is performed based on query data, and includes an existing process graph DB 620 and an existing process table DB 630, as described below.

[0037] The graph similarity information DB 233 is a database that stores information regarding similarity comparison (structural similarity and content similarity) according to an embodiment of the present disclosure.

[0038] The search result DB 234 is a database that stores information about search results for query data.

[0039] The processor 244 is a processing unit for executing processing instructions that define the functionality of each functional unit of the graph search application 150 stored by the memory 220 .

[0040] The input / output unit 246 is a functional unit for accepting information input to the graph search device 210 and outputting information such as search result data generated by the graph search device 210. In an embodiment, the input / output unit 246 may display a query data setting screen G01 that accepts information for setting query data from a user, and a search result data confirmation screen G02 that displays search result data for query data, via the user terminal 260. In an embodiment, the input / output unit 246 may include, for example, a keyboard, a mouse, a display that displays a GUI (Graphical User Interface), and the like.

[0041] Communications network 250 may include, for example, a local area network (LAN), a wide area network (WAN), a satellite network, a cable network, a WiFi network, or any combination thereof.

[0042] The user terminal 260 is a terminal device that can be used by a user of the graph search device 210. By using the user terminal 260, for example, using a GUI provided by the input / output unit 246, the user can input query data to request a graph search from the graph search device 210, or check search result data output from the graph search device 210. As an example, the user terminal 260 may include, for example, a smartphone, a smart watch, a tablet, a personal computer, etc., and is not particularly limited. For convenience of explanation, FIG. 2 illustrates an example of a configuration including one user terminal 260, but the number of user terminals 260 is not particularly limited.

[0043] According to the graph search system 200 described above, by using a similarity comparison that takes into account not only the graph structure but also the table information stored in the table associated with the graph, it is possible to provide a highly accurate graph search means that can provide reliable search results.

[0044] Next, an example process according to an embodiment of the present disclosure will be described with reference to FIG.

[0045] 3 is a diagram illustrating an example of a manufacturing process 300 according to an embodiment of the present disclosure. As described above, for example, in the manufacturing industry, products are manufactured by a process consisting of various manufacturing steps. In this disclosure, a "process" means a process consisting of a series of steps.

[0046] 3 shows a manufacturing process 300 for a chemical agent, for example, manufactured in a chemical plant, as an example of a process according to an embodiment of the present disclosure. As shown in FIG. 3, the manufacturing process 300 includes a step S301 of obtaining a first raw material, a step S302 of obtaining a second raw material, a step S303 of mixing the first raw material and the second raw material, a step S304 of freezing the output of step S303, a step S305 of grinding the output of step S304, a step S306 of heating the output of step S305, a step S307 of polishing the output of step S306, and a step S308 of cleaning the output of step S307.

[0047] As described above, a process such as manufacturing process 300 shown in Figure 3 can be represented in a graph format. In this case, each step constituting manufacturing process 300, such as obtaining raw materials, blending raw materials, freezing, grinding, heating, polishing, and cleaning, can be represented as a connected node. Furthermore, the node representing each of these steps may be associated with a table that stores information about the step. For example, the step of "freezing" may be associated with a table that stores information about the temperature used in the freezing process, the time until freezing, etc.

[0048] FIG. 3 shows, as an example, a manufacturing process 300 of a chemical agent produced in a chemical plant, but the present disclosure is not limited thereto, and the graph search means according to the embodiment of the present disclosure may be appropriately applied to any industry or field.

[0049] Next, graphs and tables according to an embodiment of the present disclosure will be described with reference to FIG.

[0050] As described above, one aspect of the present disclosure relates to searching for a graph that is highly similar to a given process expressed in a graph format. More specifically, the graph search device 210 according to an embodiment of the present disclosure uses query data indicating a user's search query to perform a search in an existing information database that stores existing information including a set of existing process graphs that indicate the flow of existing processes in a graph format and a set of existing process tables that are associated with the existing process graphs and store information about the set of existing process graphs in a tabular format, and determines search result data for the query data.

[0051] FIG. 4 is a diagram illustrating an example of query data 410 and existing information 420 according to an embodiment of the present disclosure. The query data 410 is information indicating a user's search query, and may include a query graph 412 that indicates a predetermined process in a graph format, and a query table 414 that indicates information related to the query graph 412 in a tabular format. In an embodiment, a user (e.g., a user of the user terminal 260) may set the query data 410 by defining the query graph 412 and the query table 414 using a query data setting screen G01 described later.

[0052] The existing information 420 is information for which a search is performed based on the query data 410, and may include a set of existing process graphs consisting of a first existing process graph 422 and a second existing process graph 426 that show the flow of an existing process in graphical form, and a set of existing process tables consisting of a first existing process table 424 and a second existing process table 428 that store information about each of the first existing process graph 422 and the second existing process graph 426 in tabular form.

[0053] 4, each of the query graph 412, the first existing process graph 422, and the second existing process graph 426 is composed of a plurality of connected nodes (X, Y, Z, A, B, C, etc.). Each node includes node name information indicating the name of the node. Furthermore, the query table 414, the first existing process table 424, and the second existing process table 428 store tabular information regarding each node in the query graph 412, the first existing process graph 422, and the second existing process graph 426 in one or more columns.

[0054] The columns stored in each of the first existing process table 424 and the second existing process table 428 include column name information indicating the name of the column, and table information stored in the column. For ease of explanation, FIG. 4 illustrates an example in which the first existing process table 424 and the second existing process table 428 include one column for each node in the first existing process graph 422 and the second existing process graph 426. However, the present disclosure is not limited to this, and in practice, the first existing process table 424 and the second existing process table 428 may store multiple columns for each node in the first existing process graph 422 and the second existing process graph 426.

[0055] As described below, a graph search device according to an embodiment of the present disclosure calculates the structural similarity between the query graph 412 and a set of existing process graphs consisting of a first existing process graph 422 and a second existing process graph 426, and calculates the content similarity between the query table 414 and a set of existing process tables consisting of a first existing process table 424 and a second existing process table 428, thereby determining search result data for the query data 410 based on the calculated structural similarity and content similarity.

[0056] Next, with reference to FIG. 5, a correspondence relationship between a graph and a table according to an embodiment of the present disclosure will be described.

[0057] 5 is a diagram illustrating the correspondence between graphs and tables according to an embodiment of the present disclosure. As described above, a process graph (e.g., a manufacturing process for producing a product) may include multiple nodes representing steps in the process, and each node may be associated with a process table that stores information about the node.

[0058] 5, a given process graph 500 may include nodes R1, R2, M1, F1, G1, H1, B1, and C1. In this case, node R1 may be associated with process table 531, node R2 with process table 532, node M1 with process table 533, node F1 with process table 534, node G1 with process table 535, node H1 with process table 536, node B1 with process table 537, and node C1 with process table 538. In this disclosure, "R1, R2, M1" and the like indicating the names of the nodes are referred to as "node names," and each column in the process table associated with each node is referred to as a "column name."

[0059] Next, various databases stored in the storage unit according to the embodiment of the present disclosure will be described with reference to FIG.

[0060] 6 is a diagram illustrating an example of various databases stored in the storage unit 230 according to the embodiment of the present disclosure. As illustrated in FIG. 6, the storage unit 230 according to the embodiment of the present disclosure may include a process DB231, an existing information DB232, a query graph DB233, a graph similarity information DB233, and a search result DB234.

[0061] Process DB231 is a database that stores information about existing processes, and may include information such as a process ID 611 for identifying a specific process, a process order 612 indicating the order of the steps that make up the process, and a process name 613 indicating the name of the process.

[0062] The existing information DB 232 is a database that stores existing information for which a search is performed based on query data, and includes an existing process graph DB 620 that stores existing process graphs, and an existing process table DB 630 that stores information related to the existing process graphs in a tabular format.

[0063] Existing process graph DB620 is a database that stores existing process graphs, and may include information such as a graph ID621 that identifies a specific existing process graph, a node ID622 that identifies each node in the graph, an edge ID623 that identifies each edge connecting nodes in the graph, a start node ID624 that identifies a start node in the graph, and an end node ID625 that identifies an end node in the graph.

[0064] The existing process table DB630 is an existing process table that stores information about the existing process graphs stored in the existing process graph DB620 in tabular form, and may include information such as a node ID 631 that identifies a specific node in a specific existing process graph, a table ID 632 that identifies a table associated with the node, and columns 633 included in the table.

[0065] The graph similarity information DB233 is a database that stores information related to similarity comparison (structural similarity and content similarity) according to an embodiment of the present disclosure, and may include a process ID 651 that identifies a predetermined process (e.g., a process stored in the process DB231), an existing process graph ID 642 that identifies a specific existing process graph used in the comparison, a query graph ID 643 that identifies a specific query graph used in the comparison, and a similarity 644 that indicates the similarity between the existing process graph and the query graph. The similarity 644 may indicate the structural similarity of the compared graphs and the content similarity of the tables associated with these graphs.

[0066] The search result DB 234 is a database that stores information about search results for query data, and may include information such as an existing process graph ID 651 that identifies a specific existing process graph to be used in the comparison, a query graph ID 652 that identifies a specific query graph to be used in the comparison, a maximum similarity between these graphs (e.g., the highest similarity between structural similarity and content similarity) 653, and a selected graph 654 that indicates the process graph selected as the search result (the existing process graph selected as the search result).

[0067] Next, with reference to FIG. 7, an overall flow of a manufacturing process in which the graph search means according to the embodiment of the present disclosure is used will be described.

[0068] As described above, in the manufacturing industry, when designing a manufacturing process for a new product, it may be desirable to refer to a manufacturing process for an existing product. If an existing product whose manufacturing process is similar to that of a new product being designed can be identified, the manufacturing process for the new product can be efficiently designed by using the manufacturing process of the existing product. Therefore, one aspect of the present disclosure relates to a graph search means for identifying with high accuracy a graph corresponding to a manufacturing process of an existing product whose manufacturing process is similar to that of a new product being designed. FIG. 7 is a diagram illustrating an example of an overall flow of a manufacturing process 700 in which a graph search tool according to an embodiment of the present disclosure is used.

[0069] First, in step S702, the manufacturer designs a new product. As an example, the manufacturer may design the product by determining the performance, function, mechanism, structure, etc. of the product and planning the materials, shape, dimensions, processing method, steps, etc. to be used. In addition, the manufacturer may display all or part of the steps of the manufacturing process of the product in a graph or table format. In one embodiment, the manufacturer may transmit the tentative manufacturing process displayed in a graph format as query data to the graph search device 210 from a user terminal shown in FIG. 2.

[0070] Next, in step S704, the graph search device 210 according to an embodiment of the present disclosure performs a graph search process according to an embodiment of the present disclosure based on the query data received from the manufacturer in step S702, thereby determining whether or not there is an existing process graph that satisfies a predetermined similarity criterion for the manufacturing process specified in the query data. Note that details of the graph search process according to the embodiment of the present disclosure will be described later with reference to FIG. 8, and therefore will not be described here.

[0071] Next, in step S706, if an existing process graph that satisfies a predetermined similarity criterion for the manufacturing process defined in the query data can be identified in step S704, the process proceeds to step S708. On the other hand, if an existing process graph that satisfies a predetermined similarity criterion for the manufacturing process defined in the query data cannot be identified in step S704, the process returns to step S702 and adjusts the design of the product.

[0072] Next, in step S708, the search results identified in step S704 are evaluated. Here, the manufacturer or the graph search device 210 determines whether the manufacturing process shown in the existing process graph included in the identified search results meets a predetermined efficiency standard regarding time, cost, or quality. The efficiency standard may be determined based on, for example, the product specifications or the manufacturer's requirements. In one embodiment, the evaluation may be performed by a predetermined simulation method or modeling method.

[0073] Next, in step S710, if it is determined that the search results identified in step S704 satisfy the efficiency criterion for the evaluation performed in step S708, the process proceeds to step S712. On the other hand, if it is determined that the search results identified in step S704 do not satisfy the efficiency criterion for the evaluation performed in step S708, the process returns to step S704, and the graph search is performed again.

[0074] Next, in step S712, the manufacturer manufactures the product based on the existing process graph included in the search result determined to satisfy the efficiency criterion of the evaluation performed in step S708.

[0075] Next, in step S714, the manufacturer monitors and performs quality inspection on the products manufactured in step S712.

[0076] Next, in step S716, the manufacturer determines whether or not the product is defective based on the results of the monitoring and quality inspection in step S714. If it is determined that the product is defective, the process proceeds to step S718. If it is determined that the product is not defective, the process proceeds to step S720.

[0077] Next, in step S718, the manufacturer determines whether or not a process control that suppresses the occurrence of defects can be applied to the product determined to have a defect. If it is determined that the process control that suppresses the occurrence of defects can be applied, the manufacturer applies the process control and resumes manufacturing in step S712. On the other hand, if it is determined that the process control that suppresses the occurrence of defects cannot be applied, the process returns to step S704 and performs the graph search again.

[0078] Next, in step S720, the manufacturer outputs the manufactured product.

[0079] According to the manufacturing process 700 described above, by using the graph search process according to an embodiment of the present disclosure, it is possible to identify an existing process graph corresponding to a manufacturing process of an existing product whose manufacturing process is similar to that of a new product being designed, and by using the process graph of the existing manufacturing process with high similarity, it is possible to efficiently design the manufacturing process for the new product.

[0080] Next, a graph search process according to an embodiment of the present disclosure will be described with reference to FIG.

[0081] 8 is a diagram showing an example of the flow of a graph search process 800 according to an embodiment of the present disclosure. The graph search process 800 according to an embodiment of the present disclosure is a process for searching for a graph having a high similarity to a given process expressed in a graph format and providing it to a user, and is executed by each functional unit stored in the graph search device 210 shown in FIG.

[0082] First, in step S802, the query data management unit 222 acquires query data indicating a search query for existing information stored in the above-mentioned existing information database 232. As described above, this query data may be information for searching for a process graph having a high similarity to a predetermined process expressed in a graph format, and may be input by a user via a user terminal 260 described later. In addition, this query data may include a query graph indicating the predetermined process in a graph format, and a query table associated with the query graph and indicating information related to the query graph in a tabular format.

[0083] Next, in step S804, the structural comparison unit 224 calculates the structural similarity between the query graph included in the query data acquired by the query data management unit 222 in step S802 and the set of existing process graphs stored in the existing process graph DB 620. The structural comparison unit 224 may store the structural similarity calculated here in the graph similarity information DB 233 described above. The details of the process for calculating the structural similarity will be described later with reference to FIG. 9, and therefore will not be described here.

[0084] Next, in step S806, the structural comparison unit 224 determines whether or not an existing process graph that satisfies a predetermined structural similarity threshold for the query graph included in the query data exists in the set of existing process graphs based on the structural similarity comparison result of step S804. If the structural comparison unit 224 determines that an existing process graph that satisfies a predetermined structural similarity threshold for the query graph included in the query data exists in the set of existing process graphs, the process proceeds to step S808. On the other hand, if the structural comparison unit 224 determines that there is no existing process graph in the set of existing process graphs that satisfies a predetermined structural similarity threshold for the query graph included in the query data, the process returns to step S802, and the query data management unit 222 adjusts or re-acquires the query data.

[0085] Next, in step S808, the structure comparison unit 224 sets an existing process graph that is determined to satisfy a predetermined structural similarity threshold with respect to the query graph included in the query data as a first search candidate. Here, the "first search candidate" refers to an existing process graph that has a high structural similarity (satisfies the structural similarity threshold) with respect to the query graph included in the query data, and is one of the candidates for the search result for the query data. In addition, the first search candidate here is not limited to one existing process graph, and may be multiple existing process graphs that satisfy the structural similarity with respect to the query graph.

[0086] Next, in step S810, the content comparison unit 226 calculates the content similarity between the query table included in the query data acquired by the query data management unit 222 in step S802 and the set of existing process tables stored in the existing process table DB 630. The content comparison unit 226 may store the content similarity calculated here in the graph similarity information DB 233 described above. The details of the process for calculating the content similarity will be described later with reference to FIG. 10, and therefore will not be described here.

[0087] Next, in step S812, the content comparison unit 226 determines whether or not an existing process table that satisfies a predetermined content similarity threshold for the query table included in the query data exists in the set of existing process tables based on the result of the content similarity comparison in step S810. If the content comparison unit 226 determines that an existing process table that satisfies a predetermined content similarity threshold for the query table included in the query data exists in the set of existing process graphs, the process proceeds to step S814. On the other hand, if the content comparison unit 226 determines that there is no existing process table in the set of existing process tables that satisfies a predetermined content similarity threshold with respect to the query table included in the query data, the process returns to step S802, and the query data management unit 222 adjusts or re-acquires the query data.

[0088] Next, in step S814, the content comparison unit 226 sets the existing process graph associated with the existing process table determined to satisfy a predetermined content similarity threshold with respect to the query table included in the query data as a second search candidate. Here, the "second search candidate" refers to an existing process graph associated with an existing process table having high content similarity with respect to the query table included in the query data, and is one of the search result candidates for the query data. In addition, the second search candidate here is not limited to one existing process graph, and may be multiple existing process graphs that satisfy the content similarity with respect to the query graph.

[0089] Next, in step S816, the result management unit 228 selects a search candidate that satisfies a predetermined overall evaluation criterion from among the first search candidate determined by the structural similarity comparison and the second search result determined by the content similarity comparison. The overall evaluation criterion here is information used to determine the suitability of the search candidate for the query data. In one embodiment, the overall evaluation criterion may specify a predetermined similarity threshold (e.g., 70%, 80%). In another embodiment, the overall evaluation criterion may specify "selecting the search candidate that has the highest similarity to the query data among the first search candidate and the second search result". In another embodiment, the overall evaluation criterion may specify "selecting the search candidate that has the highest average structural similarity and content similarity among the first search candidate and the second search result". Here, the results manager 228 may select any number of candidate searches that meet the overall evaluation criteria.

[0090] As an example, the result management unit 228 may refer to the graph similarity information DB 233 described above, compare the structural similarity calculated for the first search candidate with the content similarity calculated for the second search candidate, and select the search candidate that satisfies the similarity threshold specified in the overall evaluation criteria (or the search candidate with the highest similarity).

[0091] Next, in step S818, the result manager 228 performs an evaluation of the search candidate selected in step S816. Here, the result manager 228 determines whether the process represented by the existing process graph included in the search candidate selected in step S816 satisfies a predetermined efficiency criterion regarding time, cost, or quality. The efficiency criterion may be determined based on, for example, the product specifications or the manufacturer's requirements. In one embodiment, the evaluation may be performed by a predetermined simulation method or modeling method. This step substantially corresponds to step S708 in the manufacturing process 700 shown in FIG.

[0092] Next, in step S820, if it is determined that the search candidates evaluated in step S818 satisfy the above-mentioned evaluation criteria (e.g., the above-mentioned efficiency criteria), the process proceeds to step S822. On the other hand, if it is determined that the search results evaluated in step S818 do not satisfy the above-mentioned evaluation criteria (e.g., the above-mentioned efficiency criteria), the process returns to step S802 and adjusts or reacquires the query data.

[0093] Next, in step S822, the result management unit 228 sets the search candidates determined to satisfy the above-mentioned evaluation criteria as search result data for the query data, and transmits the search results to the user terminal 260 of the user who set the query data. Here, the result management unit 228 may output the existing process graph determined to satisfy the evaluation criteria and the process table associated with the existing process graph as search result data.

[0094] According to the graph search process 800 of the embodiment of the present disclosure described above, for any process expressed in a graph format, a process graph corresponding to a process with a high similarity can be obtained. The graph search process 800 of the embodiment of the present disclosure performs a search taking into consideration the content similarity based on table information stored in a table associated with each node in the graph, in addition to the structural similarity based on the node configuration in the graph, and can provide a more accurate existing process graph for the process specified in the query as a search result.

[0095] Next, a structural similarity comparison process according to an embodiment of the present disclosure will be described with reference to FIG.

[0096] 9 is a diagram showing an example of the flow of a structural similarity comparison process 900 according to an embodiment of the present disclosure. The structural similarity comparison process 900 is a process for calculating the structural similarity between a query graph included in query data acquired from a user and each existing process graph included in a set of existing process graphs, and is performed by the structural comparison unit 224 of the graph search device 210 shown in FIG.

[0097] As described above, in this disclosure, "structural similarity" refers to similarity taking into consideration structural features such as the positions of the nodes constituting the graph, the connections between the nodes, the paths in the graph, etc. As will be described later, in the structural similarity comparison process 900, the structural similarity between the query graph and the existing process graph is calculated based on natural language information regarding the nodes constituting each graph.

[0098] First, in step S902, the structure comparison unit 224 uses a predetermined graph analysis method to determine a set of paths that exist in each existing process graph included in the existing process graph DB 620. Here, the "path" means a path used to move from a start node to an end node of an existing process graph. For example, to explain an example with reference to the first existing process graph 422 shown in FIG. 4, the existing process graph 422 has four paths: [in the order of A, B, C, D, E, O], [in the order of A, B, C, M, E, O], [in the order of A, N, C, D, E, O], and [in the order of A, N, C, M, E, O]. Here, the structure comparison unit 224 may determine the set of paths present in each existing process graph by analyzing each existing process graph included in the existing process graph DB 620 using, for example, the so-called Hamiltonian algorithm.

[0099] Next, in step S904, the structure comparison unit 224 determines, for each of the paths determined in step S902, first node name information indicating the node names of each node included in the path for each existing process graph. Here, the structure comparison unit 224 may determine the first node name information indicating the node names of each node by using a predetermined natural language processing method. For example, by applying a predetermined natural language processing method to the first existing process graph 422 shown in FIG. 4, the first node name information indicating the node names of each node included in each path in the first existing process graph 422 may be determined to be [A, B, C, D, E, O], [A, B, C, M, E, O], [A, N, C, D, E, O], or [A, N, C, M, E, O].

[0100] Next, in step S906, the structure comparison unit 224 determines second node name information indicating the node name of a node in a query graph included in the query data acquired from a user. Here, the structure comparison unit 224 may determine the second node name information by applying the natural language processing method used in step S904 to the query graph. In addition, when the query graph includes a plurality of paths, the structure comparison unit 224 may determine the second node name information for each path identified after identifying each path in the query graph by the Hamilton algorithm, as in step S902. For example, by applying a predetermined natural language processing method to the query graph 412 shown in FIG. 4, [X, Y, Z] and [X, P, Z] may be determined as the second node name information indicating the node name of each node included in each path in the query graph 412.

[0101] Next, in step S908, the structural comparison unit 224 compares the first node name information determined in step S904 with the second node name information determined in step S906 by a predetermined natural language processing method to calculate the structural similarity between the query graph and each existing process graph included in the set of existing process graphs for each path, and obtains a value (probability value) of the structural similarity of the query graph with respect to each path in each existing process graph. As an example, if the existing process graph has paths 1, 2, 3, and 4, the structural comparison unit 224 may determine that the structural similarity of the query graph with respect to path 1 is "0.8", the structural similarity of the query graph with respect to path 2 is "0.7", the structural similarity of the query graph with respect to path 3 is "0.6", and the structural similarity of the query graph with respect to path 4 is "0.5".

[0102] Here, any existing means may be used as a natural language processing method for comparing the first node name information and the second node name information, and is not particularly limited in the present disclosure. The value of the structural similarity calculated here may be stored in, for example, the graph similarity information DB 233.

[0103] Next, in step S910, the structural comparison unit 224 determines, from the set of existing process graphs, an existing process graph that satisfies a predetermined structural similarity threshold with respect to the query graph as a first search candidate. This predetermined structural similarity threshold is information that specifies a lower limit of the structural similarity threshold with respect to the query graph (e.g., 0.8 or more), and may be set according to the degree of similarity that is sought. In one embodiment, the structural comparison unit 224 may determine, as the first search candidate, an existing process graph that has a path that satisfies the similarity threshold with respect to the query graph (e.g., a path with the highest similarity with respect to the query graph). In addition, in one embodiment, the structural comparison unit 224 may calculate an average similarity for each existing process graph using the structural similarity calculated for each path in the existing process graph, and determine an existing process graph whose average similarity satisfies a similarity threshold value for the query graph as a first search candidate.

[0104] According to the structural similarity comparison processing 900 described above, an existing process graph having a high structural similarity to the query graph can be identified as a search candidate based on natural language information regarding the nodes constituting each of the query graph and the existing process graph.

[0105] Next, a content similarity comparison process according to an embodiment of the present disclosure will be described with reference to FIG.

[0106] 10 is a diagram showing an example of the flow of a content similarity comparison process 1000 according to an embodiment of the present disclosure. The content similarity comparison process 1000 is a process for calculating content similarity between a query table included in query data acquired from a user and each existing process table included in a set of existing process tables, and is performed by the content comparison unit 226 of the graph search device 210 shown in FIG. As described above, the "content similarity" in this disclosure means a similarity that takes into account the syntactic and semantic features of table information that stores information about graphs in a tabular format. As will be described later, in the content similarity comparison process 1000, the structural similarity between a query table and an existing process table is calculated based on the table information stored in each table.

[0107] First, in step S1002, the content comparison unit 226 uses a predetermined natural language processing method to determine, for each existing process table, first column name information indicating the column names of columns present in each existing process table included in the existing process table DB 630. Here, the "column" means a column constituting the existing process table in tabular form. For example, to explain an example with reference to the process table 531 shown in FIG. 6, the process table 531 may determine [X1, Y1, P1, Z1] as the first column name information indicating the column names of the process table 531.

[0108] Next, in step S1004, the content comparison unit 226 uses a predetermined natural language processing method to determine second column name information indicating the column name of a column present in the query table. Here, the content comparison unit 226 may use the same natural language processing method as in step S1002.

[0109] Next, in step S1006, the content comparison unit 226 calculates the content similarity between the first column name information determined for each existing process table and the second column name information determined for the query table, and obtains a value (probability value) of the content similarity of the query table to each existing process table. Here, any existing means may be used as a natural language processing method for comparing the first column name information and the second column name information, and is not particularly limited in the present disclosure. The content similarity value calculated here may be stored in the graph similarity information DB 233, for example. The natural language processing technique used in this process is a technique for determining similarity based on syntactic and semantic features of natural language information such as column name information and table information, and is not particularly limited in this disclosure. In this manner, columns with similar column names can be identified between the query table and each existing process table.

[0110] Next, in step S1008, the content comparison unit 226 determines, based on the content similarity between the first column name information and the second column name information calculated in step S1006, from among each existing process table, a process graph associated with an existing process table that satisfies a predetermined content similarity criterion, as a second search candidate.

[0111] More specifically, the content comparison unit 226 identifies a first column set from each existing process table that satisfies a predetermined content similarity threshold for a specific column set (referred to as a second column set) included in the query table. Then, the content comparison unit 226 compares the first table information stored in the identified first column set from each existing process table with the second table information stored in the second column set included in the query table, thereby determining the content similarity between the first table information and the second table information for each existing process table. Thereafter, the content comparison unit 226 may determine, from the set of existing process tables, an existing process graph associated with an existing process table that includes first table information that satisfies a predetermined content similarity threshold with respect to the second table information as a second search candidate.

[0112] As a result, the content comparison unit 226 can identify an existing process table that includes a column that has high content similarity with the column included in the query table based on the content similarity comparison in step S1006, and then determine the process graph associated with the existing process table as a second search candidate.

[0113] According to the content similarity comparison process 1000 described above, by comparing the table information stored in the query table and the existing process table, it is possible to identify, as a search candidate, an existing process graph associated with an existing process table having a high content similarity to the query table.

[0114] Next, a query data setting screen according to an embodiment of the present disclosure will be described with reference to FIG.

[0115] 11 is a diagram illustrating an example of a query data setting screen G01 according to an embodiment of the present disclosure. The query data setting screen G01 is a user interface screen for setting query data according to an embodiment of the present disclosure, and may be provided to a user via, for example, the user terminal 260 illustrated in FIG.

[0116] As shown in FIG. 11, the query data setting screen G01 includes a graph ID setting window 1110, a table ID 1120, a node ID setting window 1130, a similarity threshold setting window 1140, a search button 1150, and a cancel button 1160.

[0117] The graph ID setting window 1110 is an input window for specifying a process graph to be used as a query graph in the query data. The user can set a query graph by inputting a graph ID for identifying a previously created graph to be used as a query graph into the graph ID setting window 1110.

[0118] The table ID setting window 1120 is an input window for specifying a process table to be used as a query table in the query data. The user can set a query table by inputting a table ID (for example, a table ID of a table associated with the query graph set in the graph ID setting window 1110) that identifies a previously created table to be used as a query table in the table ID setting window 1120.

[0119] The node ID setting window 1130 is an input window for specifying each node in the query graph specified in the graph ID setting window 1110 and the query table specified in the table ID setting window 1120. The user can set the nodes that make up the query graph and the query table by inputting node IDs that identify each node in the query graph and the query table into the node ID setting window 1130.

[0120] The similarity threshold setting window 1140 is an input window for setting a similarity threshold required by the user. By inputting a predetermined similarity value into the similarity threshold setting window 1140, the user can set the similarity threshold used in the above-mentioned structural similarity comparison and content similarity comparison.

[0121] The search button 1150 is a button for performing a graph search on the query data according to conditions set by the user.

[0122] The cancel button 1160 is a button for clearing the data input on the query data setting screen G01 and canceling the graph search.

[0123] Next, a search result confirmation screen according to an embodiment of the present disclosure will be described with reference to FIG.

[0124] 12 is a diagram showing an example of a search result confirmation screen G02 according to an embodiment of the present disclosure. The search result confirmation screen G02 is a user interface screen for confirming similarity information and search results according to an embodiment of the present disclosure, and may be provided to a user via, for example, the user terminal 260 shown in FIG.

[0125] As shown in FIG. 12, the search result confirmation screen G02 includes a similarity comparison result table 1210 and a search result data table 1220.

[0126] The similarity comparison result table 1210 is a table showing search candidates determined by a similarity comparison between predetermined query data and existing information stored in the existing information DB 232. As shown in Fig. 12, the similarity comparison result table 1210 may include graph IDs 1211 of process graphs that are search candidates for the query data, node IDs 1212 for identifying nodes included in each process graph, structural similarity 1213 of the process graph to the query graph, content similarity 1214 of the process table associated with the graph to the query table, and a table ID for identifying the table associated with the process graph.

[0127] The search result data table 1220 is a table showing information on a specific search candidate (e.g., a process graph selected as search result data for query data) among the search candidates shown in the similarity comparison result table 1210. As shown in Fig. 12, the search result data table 1220 may include a graph ID 1221 for identifying a selected graph, a node ID 1222 for identifying each node in the process graph, a table ID 1223 for identifying a process table associated with the process graph, and table information 1224 stored in each column in the process table.

[0128] As mentioned above, one aspect of the graph searching means according to embodiments of the present disclosure relates to a similarity comparison that takes into account the structure of the process graph and table information stored in a table associated with the process graph. More specifically, by identifying node name information of nodes included in a process graph and comparing the node name information of different process graphs using natural language processing techniques, it is possible to identify existing process graphs that have a similar structure and contain similar nodes to a specified process graph (e.g., a query graph). In addition, by comparing table information stored in process tables associated with different process graphs using natural language processing techniques, it is possible to identify existing process tables with similar content that store similar information as a specific process table (e.g., a query table), and the process graphs associated with those existing process tables.

[0129] In this way, by performing a graph search based on multiple perspectives, such as structural similarity and content similarity, it is possible to provide reliable search results even when a highly complex graph associated with various information related to a manufacturing process of a product is used. For example, the graph search means according to the embodiment of the present disclosure can provide search results that include process graphs that do not include similar nodes (i.e., are not structurally similar) but are similar in content.

[0130] As described above, the graph search means according to the embodiment of the present disclosure includes the following aspects.

[0131] (Aspect 1) A graph search device, comprising: A processor, a memory, and a storage unit are provided, The storage unit is an existing information database for storing existing information including a set of existing process graphs showing process flows in a graph format and a set of existing process tables associated with the set of existing process graphs and storing information about the set of existing process graphs in a tabular format; The memory includes: a query data management unit that acquires query data, the query data being a search query for the existing information, the query data including a query graph and a query table; a structural comparison unit that calculates a structural similarity between the query graph included in the query data and the set of existing process graphs by comparing the query graph with the set of existing process graphs, and determines, from the set of existing process graphs, an existing process graph that satisfies a predetermined structural similarity threshold value with respect to the query graph as a first search candidate; a content comparison unit that calculates a content similarity between the query graph and the set of existing process tables by comparing the query table included in the query data with the set of existing process tables, and determines, from the set of existing process tables, an existing process graph associated with an existing process table that satisfies a predetermined content similarity threshold with respect to the query table as a second search candidate; a result management unit that determines and outputs search result data for the query data from among the first search candidates and the second search candidates based on a predetermined comprehensive evaluation criterion; The graph search device according to claim 1, further comprising: a processing instruction for causing the processor to function as a

[0132] (Aspect 2) The structural comparison unit includes: analyzing the set of existing process graphs using a predetermined graph analysis technique to determine a set of paths that exist in each existing process graph in the set of existing process graphs; determining, for each path in the set of determined paths, first node name information for each existing process graph that indicates a node name of each node in the path by using a predetermined natural language process technique; determining second node name information indicative of a node name for each node included in the query graph by using a predetermined natural language process technique; calculating the structural similarity between the query graph and the set of existing process graphs by comparing the first node name information determined for each existing process graph with the second node name information using a predetermined natural language processing method, and determining, from among the set of existing process graphs, existing process graphs that satisfy a predetermined structural similarity threshold value with respect to the query graph as first search candidates; 2. The graph search device according to aspect 1.

[0133] (Aspect 3) The content comparison unit is determining, for each existing process table, first column name information indicating a column name of a column present in each existing process table included in the set of existing process tables by using a predetermined natural language processing technique; determining second column name information indicating column names of columns included in the query table by using a predetermined natural language processing technique; calculating a content similarity between the first column name information and the second column name information by comparing the first column name information and the second column name information determined for each existing process table using a predetermined natural language processing technique, and identifying, from each existing process table, a set of first columns that satisfies a predetermined content similarity threshold with respect to a set of second columns included in the query table; determining, for each existing process table, a content similarity between the first table information and the second table information by comparing first table information stored in the first column set identified from each existing process table with second table information stored in a second column set included in the query table, and determining, from the set of existing process tables, an existing process graph associated with an existing process table including first table information that satisfies a predetermined content similarity threshold with respect to the second table information, as a second search candidate; 3. The graph search device according to aspect 1 or 2.

[0134] (Aspect 4) The result management unit: comparing the structural similarity calculated for the first search candidate with the content similarity calculated for the second search candidate, and determining the search candidate that satisfies the comprehensive evaluation criterion as the search result data; 4. The graph search device according to any one of aspects 1 to 3.

[0135] (Aspect 5) The result management unit: determining, as the search result data, search candidates among the first search candidates and the second search candidates that satisfy a predetermined efficiency criterion related to time, cost, or quality; 5. The graph search device according to any one of aspects 1 to 4.

[0136] (Aspect 6) The result management unit: displaying, as the result data, on a user interface screen, a graph ID that identifies a specific existing process graph that is a search candidate that satisfies the comprehensive evaluation criterion, a table ID that identifies an existing process table associated with the specific existing process graph, a node ID that identifies a node included in the specific existing process graph, the structural similarity calculated for the specific existing process graph, and the content similarity calculated for the specific existing process graph. 6. The graph search device according to any one of aspects 1 to 5.

[0137] Although the embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and various modifications are possible without departing from the gist of the present invention. [Explanation of symbols]

[0138] 150 Graph Search Applications 200 Graph Search System 210 Graph Search Device 220 Memory 222 Query Data Management Unit 224 Structure comparison section 226 Content comparison section 228 Results Management Department 230 Storage section 231 Process DB 232 Existing Information DB 233 Graph Similarity Information DB 234 Search result DB 244 processors 246 Input / output section 250 Communication Network 260 User Terminals

Claims

1. A graph search device, comprising a processor, a memory, and a storage unit, wherein the storage unit includes an existing information database that stores existing information including a set of existing process graphs that represent the flow of a process in graph form and a set of existing process tables that are associated with the set of existing process graphs and store information regarding the set of existing process graphs in tabular form, wherein the memory includes a query data management unit that acquires query data which is a search query for the existing information and includes a query graph and a query table, a structure comparison unit that calculates a structural similarity between the query graph included in the query data and the set of existing process graphs by comparing the query graph with the set of existing process graphs, and determines, from the set of existing process graphs, an existing process graph that satisfies a predetermined structural similarity threshold with respect to the query graph as a first search candidate, a content comparison unit that calculates a content similarity between the query table included in the query data and the set of existing process tables by comparing the query table with the set of existing process tables, and determines, from the set of existing process tables, an existing process graph associated with an existing process table that satisfies a predetermined content similarity threshold with respect to the query table as a second search candidate, a result management unit that determines and outputs search result data for the query data based on a predetermined comprehensive evaluation criterion among the first search candidate and the second search candidate, and includes processing instructions for causing the processor to function as such, characterized by the graph search device.

2. The structure comparison unit analyzes the set of existing process graphs using a predetermined graph analysis method to determine a set of paths existing in each existing process graph included in the set of existing process graphs, By using a predetermined natural language processing method, for each path included in the determined set of paths, first node name information indicating the node name of each node included in the path is determined for each existing process graph. By using a predetermined natural language processing method, second node name information indicating the node name of each node included in the query graph is determined. By comparing the first node name information determined for each existing process graph with the second node name information by a predetermined natural language processing method, the structural similarity between the query graph and the set of existing process graphs is calculated, and from the set of existing process graphs, an existing process graph that satisfies a predetermined structural similarity threshold with respect to the query graph is determined as the first search candidate. The graph search device according to claim 1, characterized in that.

3. The content comparison unit By using a predetermined natural language processing method, first column name information indicating the column name of each column existing in each existing process table included in the set of existing process tables is determined for each existing process table. By using a predetermined natural language processing method, second column name information indicating the column name of the column included in the query table is determined. By comparing the first column name information determined for each existing process table with the second column name information by a predetermined natural language processing method, the content similarity between the first column name information and the second column name information is calculated, and from each existing process table, a set of first columns that satisfies a predetermined content similarity threshold with respect to the set of second columns included in the query table is identified. By comparing the first table information stored in the set of the first columns specified from among the existing process tables with the second table information stored in the set of the second columns included in the query table, the content similarity between the first table information and the second table information is determined for each existing process table, and from among the set of the existing process tables, an existing process graph associated with an existing process table including the first table information that satisfies a predetermined content similarity threshold with respect to the second table information is determined as the second search candidate. The graph search device according to claim 1, characterized in that.

4. The result management unit Compares the structural similarity calculated for the first search candidate with the content similarity calculated for the second search candidate, and determines a search candidate that satisfies the comprehensive evaluation criteria as the search result data. The graph search device according to claim 1, characterized in that.

5. The result management unit Among the first search candidate and the second search candidate, a search candidate that satisfies a predetermined efficiency criterion regarding time, cost, or quality is determined as the search result data. The graph search device according to claim 1, characterized in that.

6. The result management unit Displays, on the user interface screen as the search result data, a graph ID for identifying a specific existing process graph that becomes a search candidate satisfying the comprehensive evaluation criteria, a table ID for identifying an existing process table associated with the specific existing process graph, a node ID for identifying a node included in the specific existing process graph, the structural similarity calculated for the specific existing process graph, and the content similarity calculated for the specific existing process graph. The graph search device according to claim 1, characterized in that.

7. A graph search device that performs graph search and, In a graph search system in which a user terminal is connected via a communication network, The graph search device, Includes a processor, a memory, and a storage unit, The storage unit, Includes an existing information database that stores existing information including a set of existing process graphs that represent the flow of a process in graph form and a set of existing process tables that are associated with the set of existing process graphs and store information about the set of existing process graphs in tabular form, The memory, A query data management unit that acquires query data including a query graph and a query table, which is a search query for the existing information, from the user terminal, By comparing the query graph included in the query data with the set of existing process graphs, calculates the structural similarity between the query graph and the set of existing process graphs, and determines an existing process graph that satisfies a predetermined structural similarity threshold for the query graph as a first search candidate from among the set of existing process graphs; a structure comparison unit, By comparing the query table included in the query data with the set of existing process tables, calculates the content similarity between the query graph and the set of existing process tables, and determines an existing process graph associated with an existing process table that satisfies a predetermined content similarity threshold for the query table as a second search candidate from among the set of existing process tables; a content comparison unit, Based on a predetermined comprehensive evaluation criterion, determines search result data for the query data among the first search candidate and the second search candidate, and transmits it to the user terminal; a result management unit, A graph search system, characterized by including processing instructions for causing the processor to function as such. In a computer system including a memory for storing processing instructions and a processor, the processing instructions stored in the memory are a step of constructing an existing information database storing existing information including a set of existing process graphs showing a process flow in a graph format and a set of existing process tables associated with the set of existing process graphs and storing information about the set of existing process graphs in a table format; a step of obtaining query data which is a search query for the existing information and includes a query graph and a query table; a step of analyzing the set of existing process graphs using a predetermined graph analysis method to determine a set of paths existing in each existing process graph included in the set of existing process graphs; a step of determining, for each path included in the determined set of paths, first node name information indicating the node name of each node included in the path for each existing process graph by using a predetermined natural language processing method; a step of determining second node name information indicating the node name of each node included in the query graph by using a predetermined natural language processing method; a step of calculating the structural similarity between the query graph and the set of existing process graphs by comparing the first node name information determined for each existing process graph with the second node name information by using a predetermined natural language processing method, and determining, from the set of existing process graphs, an existing process graph that satisfies a predetermined structural similarity threshold for the query graph as a first search candidate; a step of determining, for each existing process table included in the set of existing process tables, first column name information indicating the column name of each column existing in the existing process table by using a predetermined natural language processing method; a step of determining second column name information indicating the column name of the column included in the query table by using a predetermined natural language processing method; By comparing the first column name information and the second column name information determined for each existing process table by a predetermined natural language processing method, the content similarity between the first column name information and the second column name information is calculated, and a set of first columns that satisfies a predetermined content similarity threshold for the set of second columns included in the query table is identified from among each existing process table; By comparing the first table information stored in the set of first columns identified from among each existing process table with the second table information stored in the set of second columns included in the query table, the content similarity between the first table information and the second table information is determined for each existing process table, and an existing process graph associated with an existing process table that includes the first table information that satisfies a predetermined content similarity threshold for the second table information is determined as a second search candidate from among the set of existing process tables; Based on a predetermined comprehensive evaluation criterion, search result data for the query data is determined and output from among the first search candidate and the second search candidate; A graph search method, characterized by causing the processor to execute the above.

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