Data management system and method

Abstract

To make it possible to estimate whether or not data can properly execute an application.SOLUTION: In a data management system, a storage device stores ontology database format information indicating a format of a database, acquired data acquired from an analysis object, and an analysis application that performs analysis by using, as an input, a database in which data acquired from the analysis object is recorded and outputs analysis results. A processing device compares a format of the acquired data with a format indicated in ontology database format information, generates a temporary database in which the acquired data is applied to the format indicated by the ontology database format information, executes an analysis application with the temporary database being an input, acquires verification results including a state and / or results of execution of the analysis application, and estimates the degree to which the temporary database is suitable for the analysis application based on the verification results.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to techniques for managing data. [Background technology]

[0002] In manufacturing businesses that use robots to produce products, various data are generated from various processes. For example, in the process of designing manufacturing equipment, data such as work instructions, transport route information, equipment configuration information, equipment control programs, and equipment control parameters are created. In the process of production planning, data such as manufacturing instruction information, manufacturing schedule information, transport instruction information, and manufacturing schedule are created. In the process of production, data on actual results such as production progress information, work time information, information on the worker performing the work, information on the equipment performing the work, and sensor measurement data within the equipment are generated. In the process of analysis, data such as delay information including delay time, frequency, and factors, equipment stop information including equipment stop time, frequency, and factors, return rate information, and product-equipment correlation information are generated. There are existing applications (hereinafter also referred to as "analysis applications") that analyze data to support improvements in production efficiency and responses to GX (Green Transformation). However, data acquired in various processes are inconsistent in type and format, and analysis applications often cannot be executed appropriately. In relation to this, Patent Document 1 discloses a method for estimating the type of a data table. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-35332 A Summary of the Invention [Problem to be solved by the invention]

[0004] The technique disclosed in Patent Document 1 can estimate the type of data, but cannot estimate whether the data is suitable for properly executing an application.

[0005] One objective of the present disclosure is to provide a technique for estimating whether data is suitable for properly executing an application. [Means for solving the problem]

[0006] A data management system according to one aspect included in the present disclosure is a data management system for managing data acquired from an analysis target, and includes a storage device and a processing device. The storage device stores ontology database format information indicating a predetermined database format, acquired data acquired from the analysis target, and an analysis application that performs an analysis using a database in which the data acquired from the analysis target is recorded as an input and outputs an analysis result. The processing device compares the format of the acquired data with a format indicated in the ontology database format information, generates a provisional database in which the acquired data is fitted to the format indicated in the ontology database format information, executes the analysis application using the provisional database as an input, acquires a verification result including a state and / or a result of execution of the analysis application, and estimates how well the provisional database is suited to the analysis application based on the verification result.

[0007] A data management method according to one aspect included in the present disclosure is a data management method for managing data acquired from an analysis target by a data management system having a storage device and a processing device, wherein the storage device stores ontology database format information indicating a predetermined database format, acquired data acquired from the analysis target, and an analysis application that performs analysis using a database in which the data acquired from the analysis target is recorded as input, and outputs analysis results, and the processing device compares the format of the acquired data with the format indicated in the ontology database format information, generates a provisional database in which the acquired data fits the format indicated in the ontology database format information, executes the analytical application using the provisional database as input, obtains verification results including a status and / or results of execution of the analytical application, and estimates how well the provisional database is suited to the analytical application based on the verification results. Effect of the Invention

[0008] According to one aspect of the present disclosure, it is possible to estimate whether data is suitable for properly executing an application. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a conceptual diagram illustrating an example of the relationship between a data management system, an analysis target, and an analysis application. [Diagram 2] FIG. 1 is a conceptual diagram illustrating an example of the configuration of a data management system. [Diagram 3] FIG. 1 is a conceptual diagram illustrating a hardware configuration of a data management system. [Figure 4] 11 is a flowchart illustrating an example of processing by the data management system. [Diagram 5] FIG. 2 is a conceptual diagram illustrating the format of data stored in a customer database. [Figure 6] FIG. 2 is a conceptual diagram illustrating an example of the structure of an ontology database. [Figure 7]FIG. 1 is a conceptual diagram showing a hierarchy of an ontology database. [Figure 8] FIG. 2 is a conceptual diagram illustrating an example of the format of a design model database. [Figure 9] FIG. 1 is a conceptual diagram illustrating an example of the format of a planning model database. [Figure 10] FIG. 13 is a conceptual diagram illustrating an example of the format of a performance model database. [Figure 11] FIG. 13 is a conceptual diagram showing an example of presentation of remaining items. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0011] FIG. 1 is a conceptual diagram illustrating an example of the relationship between a data management system, an analysis target, and an analysis application according to an embodiment of the present disclosure.

[0012] The data management system 1 manages data acquired from an analysis target 2. The analysis target 2 is a system used in a production environment such as a factory (hereinafter, a production environment system). The production environment includes, for example, a production line in a factory. For example, a production line in a manufacturing industry that produces products using robots corresponds to the production environment.

[0013] A production environment system may be composed of multiple systems. For example, a production environment system may be composed of a PLC, a production environment (robot equipment), SCADA, MES, and ERP / PLM. Note that these components are merely examples, and other components may be included in the production environment system.

[0014] Each of the multiple components included in the analysis target 2 stores files, data, and the like.

[0015] A vendor may create an analytical application 3 using various data accumulated in the analysis target 2. Examples of analytical applications include a line configuration application, a KPI production application, a work instruction application, an operation diagnosis application, a product diagnosis application, etc. However, the analytical application 3 is not limited to these.

[0016] When creating a business system using data accumulated in analysis target 2, rather than starting with implementation from the beginning, a proof of concept (PoC) may be performed before the business system is created. The business system in this case includes the above analysis application. There are also cases where the business system is deployed horizontally, that is, applied to other businesses. For this reason, the business system may be operated in a simple manner first.

[0017] Here, the files and data accumulated in the analysis target 2 may be stored in a distributed manner without uniformity of specifications in each component included in the analysis target 2. In fact, the production environment system that is the analysis target 2 may be constructed by combining multiple subsystems manufactured by multiple vendors.

[0018] In that case, when using a business system such as the above-mentioned analytical application for concept implementation or horizontal deployment, it is necessary to decide which files or data should be obtained from where in the analysis target 2 and input into the business system. Conventionally, this decision-making task has often been done manually.

[0019] In order to reduce the costs of concept implementation and horizontal deployment, it is preferable if the system can automate the above decision-making process to some extent. Note that if you are just implementing concepts rather than building an actual system, the business system does not need to be fully operational.

[0020] Fig. 2 is a conceptual diagram illustrating a configuration example of a data management system according to an embodiment of the present disclosure. Fig. 3 is a conceptual diagram illustrating a hardware configuration of the data management system according to an embodiment of the present disclosure.

[0021] The data management system 1 includes a processing device 11 and a storage device 12. The data management system 1 also includes a main memory 13, a communication device 14, and a display device 16.

[0022] The processing device 11 includes a processor. The processing device 11 functionally realizes a data classification unit 111, an application execution unit 112, a determination processing unit 113, and an information presentation unit 114 by reading out a program stored in the storage device 12 to the processor and executing the program.

[0023] The data classification unit 111 compares the format of the acquired data with the format indicated in the ontology database format information, and generates a provisional database in which the acquired data fits into the format indicated in the ontology database format information.

[0024] The data acquired from the analysis target 2 corresponds to the acquired data referred to here. The ontology database format information is information indicating a predetermined format of a database. For example, information indicating a format of a database used in a system to be implemented and information indicating a format of a database expected when a business system is deployed horizontally correspond to the ontology database format information. In other words, the specifications of the ontology database format information are determined by the person who implements the concept and / or deploys the concept, and the ontology database format information has different formats depending on the actual situation of the concept implementation and / or deploys the concept.

[0025] The application execution unit 112 executes the analysis application using the generated provisional database as an input, and obtains a verification result including the state and / or result of the execution of the analysis application.

[0026] The judgment processing unit 113 estimates how well the provisional database is suited to the analysis application based on the verification result.

[0027] The information presenting unit 114 presents the estimation result to the user by displaying the estimation result.

[0028] The storage device 12 stores a customer database 121 and an application 122. The storage device 12 may also store other programs and data.

[0029] The storage device 12 stores ontology database format information indicating a predetermined database format, acquired data acquired from the analysis target 2, and an analysis application 3 that performs analysis using a database in which the data acquired from the analysis target 2 is recorded as input, and outputs an analysis result. In this specification, the above analysis application may be referred to as a first analysis application. The storage device 12 may further store a second analysis application different from the first analysis application. The second analysis application will be described later.

[0030] The main memory 13 may be included in the processing device 11 or in the storage device 12. Data stored in the storage device 12 is loaded into the main memory 13, and the processor of the processing device 11 processes the data.

[0031] The communication device 14 communicates with an external device such as a user terminal 5 via a communication network 4 such as the Internet.

[0032] The input device 15 is a device, such as a keyboard, a mouse, or a touch panel screen, that accepts input from an operator.

[0033] The display device 16 is a device such as a display or a touch panel screen that displays information to an operator.

[0034] FIG. 4 is a flowchart illustrating an example of processing by the data management system according to an embodiment of the present disclosure.

[0035] The processing device 11 acquires data of the analysis target 2 (St101). The processing device 11 classifies the acquired data (St102). The processing device 11 inputs the classified data into the analysis application 3 (St103). The classification in step St102 may be, for example, classification into four types of model databases, a design model database, a plan model database, a performance model database, and an analysis model database, which will be described later.

[0036] The processing device 11 repeats the processes from steps St104 to St106 for each data to be input to the analysis application 3.

[0037] In step St104, the processing device 11 performs a normality determination as to whether or not the analysis application 3 has operated normally based on the input data.

[0038] In step St105, if the input data can be used by the application (St105: Yes), the process proceeds to step St106. If the input data cannot be used by the application (St105: No), the process returns to step St104 to process the next data.

[0039] In step St106, the processor 11 determines the priority order of the multiple generated temporary databases.

[0040] In step St107, the processor 11 displays the remaining items on the display device 16, thereby presenting the remaining items.

[0041] (Generating a temporary database) In step St102, the data classification unit 111 compares the format of the data acquired in step St101 with the format indicated in the ontology database format information, and generates a provisional database in which the acquired data is fitted to the format indicated in the ontology database format information.

[0042] The data classification unit 111 regards the portion of the acquired data that corresponds to at least one of the data structure of the database, the database name of the database, the column names of the columns that make up the database, and the properties of the values ​​that the columns can take, as data feature corresponding portions, and generates a provisional database by fitting the data feature corresponding portions into the format of the ontology database format information.

[0043] If the data feature corresponding portion is within a range of values ​​that are expected in advance as an input for an analysis application, the data classification unit 111 may use the data feature corresponding portion in the provisional database.

[0044] A plurality of provisional databases may be generated instead of a single one. That is, the data classification unit 111 generates a plurality of different provisional databases. In this case, the application execution unit 112 acquires the verification results of each of the plurality of provisional databases. The judgment processing unit 113 ranks the plurality of provisional databases based on the plurality of verification results. The information presentation unit 114 displays the ranking of the provisional databases.

[0045] (Temporary database priority determination) In step St106, the application execution unit 112 acquires the verification results including the normality of the operation, the normality of the output value, the insufficient number of digits, the insufficient number of data, and the insufficient number of items. The determination processing unit 113 ranks the multiple provisional databases based on the verification results.

[0046] The operation normality is a value indicating whether the analysis application operated normally. The output value normality is a value indicating whether the data output from the analysis application is within a predetermined normal range. The number of digits insufficiency is a value indicating whether the analysis application did not operate normally or the data output from the analysis application was outside the normal range because the number of digits in the value of the acquired data was insufficient.

[0047] The data number insufficiency is a value indicating whether or not the cause of an analysis application not operating normally or data output from the analysis application being outside the normal range is an insufficient number of acquired data items.The item insufficiency is a value indicating whether or not the cause of an analysis application not operating normally or data output from the analysis application being outside the normal range is an insufficient item of acquired data.

[0048] The formulas for calculating values ​​indicating operation normality, output value normality, insufficient number of digits, insufficient number of data, and insufficient item may be determined appropriately by those skilled in the art.

[0049] For example, the provisional databases may be prioritized as follows: If the analysis application operates normally and the data output from the analysis application is within the normal range, the determination processing unit 113 ranks the corresponding provisional database first. If the analysis application does not operate normally or the data output from the analysis application is outside the normal range and the cause is an insufficient number of data, the determination processing unit 113 ranks the corresponding provisional database second. If the analysis application does not operate normally or the data output from the analysis application is outside the normal range and the cause is an insufficient number of items, the determination processing unit 113 ranks the corresponding provisional database third.

[0050] (Ranking the hypothetical database using a second analytical application) The storage device 12 is an application that performs analysis using a database in which data acquired from an analysis target is recorded as input, and outputs the analysis results, and further stores a second analysis application different from the first analysis application.

[0051] The application execution unit 112 executes a second analysis application using the provisional database as an input, and obtains a second verification result including a state and / or a result of the execution of the second analysis application. The judgment processing unit 113 further ranks the provisional databases that have the same ranking based on the verification result obtained by the first analysis application, based on the second verification result.

[0052] The criteria for ranking based on the first analysis application and the criteria for ranking based on the second analysis application may be the same or different. For example, when the criteria for ranking based on the first analysis application and the criteria for ranking based on the second analysis application use operation normality, output value normality, digit number deficiency, data number deficiency, and item deficiency, the weights of operation normality, output value normality, digit number deficiency, data number deficiency, and item deficiency may be different between the first analysis application and the second analysis application.

[0053] The storage device 12 further stores analysis results output when the analysis application was normally executed in the past. The determination processing unit 113 calculates a correlation value between the analysis result obtained by executing the analysis application using the provisional database as input and the analysis result stored in the storage device 12. A specific method for calculating the correlation value may be appropriately determined by a person skilled in the art. The determination processing unit 113 further ranks provisional databases that have the same ranking based on the verification result obtained by the analysis application, based on the correlation value.

[0054] (Presentation of remaining items) In step St107, the processor 11 displays the remaining items on the display device 16, thereby presenting the remaining items.

[0055] In step St107, the processor 11 identifies blank spaces in the provisional databases that are ranked at or above a predetermined rank, in which no values ​​have been entered from the acquired data, and presents the provisional databases and the blank spaces.

[0056] The processing device 11 compares the data features, which are at least one of the data structure of the database, the database name of the database, the column names of the columns that make up the database, and the properties of the values ​​that the columns can take, indicated in the ontology database format information, with the data features of the acquired data and presents the differences.

[0057] FIG. 5 is a conceptual diagram illustrating the format of data stored in a customer database in an embodiment of the present disclosure.

[0058] The data shown in FIG. 5 and stored in the customer database 121 has the following information items, or columns: WorkId, ProductId, ProcessId, OperationId, StartDate, EndDate, and ActualOperationTime.

[0059] WorkId means an ID that uniquely identifies the target work. ProductId means an ID that uniquely identifies the target product. OperationId means an ID that uniquely identifies the target operation. StartDate means the start date and time of the operation. EndDate means the start date and time of the operation. ActualOperationTime means the actual time taken for the operation.

[0060] The data format shown in FIG. 5 is an example of data stored in the customer database 121. The customer database 121 stores various data held by various components (systems, etc.) included in the analysis target 2. The customer database 121 includes a plurality of databases, files, etc. In some cases, these databases and files are not completely classified into the four types of design model database, plan model database, performance model database, and analysis model database. In other words, the databases and files stored in the customer database 121 are in a mixed state, and data of different classifications may be mixed in the columns of the same table.

[0061] Fig. 6 is a conceptual diagram showing an example of the structure of an ontology database in an embodiment of the present disclosure. Fig. 7 is a conceptual diagram showing a hierarchy of an ontology database in an embodiment of the present disclosure.

[0062] The ontology database shown in Fig. 6 illustrates the structure of a database used in a typical line management system implemented in the manufacturing industry. The data structure of the system actually differs for each customer.

[0063] The ontology database shown in Fig. 6 and Fig. 7 is composed of a design model database 61, a plan model database 62, a performance model database 63, and an analysis model database 64. In the initial state, the ontology database is empty of data or has provisional values ​​entered. Data acquired from the analysis target 2 and stored in the customer database 121 is provisionally fitted into the ontology database based on file names, column names, etc. The provisional fitting is not necessarily determined in a single way, and various combinations are possible. Therefore, multiple incomplete ontology databases are generated as candidates.

[0064] The processing device 11 defines a data structure using an ontology to define the relationship between the data required for line operation and the KPIs and original data required for the application, thereby extracting data required for the application and simulation in association with each other.

[0065] FIG. 8 is a conceptual diagram illustrating a format of a design model database in the embodiment of the present disclosure.

[0066] The design model database 61 has ProductSerial and Product as information items, i.e., columns. ProductSerial means a serial code of a product. Product indicates the type of product.

[0067] FIG. 9 is a conceptual diagram illustrating a format of a planning model database in an embodiment of the present disclosure.

[0068] The planning model database 62 has Process, order, subclass, and ProcessFlow as information items, or columns. Process means an identifier for the target production process. Order indicates the order of operations in the target production process. Subclass indicates the class to which the target operation belongs. ProcessFlow indicates an identifier that identifies the process flow of the target operation.

[0069] FIG. 10 is a conceptual diagram illustrating an example of the format of the performance model database in the embodiment of the present disclosure.

[0070] The actual result model database 63 has the following information items, or columns: Task, ProductSerial, Job, ProcessSerial, LineModule, start_plan, finish_plan, start_actual, and finish_actual. Task means the identifier of the target task. ProductSerial means the serial code of the target product. Job means the identifier of the target job. ProcessSerial means the serial code of the target process. LineModule means the module name of the production line. start_plan indicates the start timing of the task at the time of planning. finish_plan indicates the end timing of the task at the time of planning. start_actual indicates the start timing of the task in actual results. finish_actual indicates the end timing of the task in actual results.

[0071] FIG. 11 is a conceptual diagram showing an example of presentation of remaining items in an embodiment of the present disclosure.

[0072] In step St107 of Fig. 4, the processing device 11 displays the remaining items on the display device 16. Fig. 11 shows an example of the remaining items presented when data is fitted to the planning model database 62 in step St103. Of the four columns, data fitting is successful for Process, order, and ProcessFlow. However, data fitting fails for the subclass column. Therefore, in step St107, the processing device displays on the display device 16 the contents stored in the planning model database 62 at that time and a message saying "There is insufficient data for 'subclass'".

[0073] The above-described embodiments are illustrative examples of the present invention, and are not intended to limit the scope of the present invention to these embodiments. A person skilled in the art can implement the present invention in various other forms without departing from the scope of the present invention. The above-described embodiments also include the following features. However, the features included in the above-described embodiments are not limited to those described below.

[0074] [Item 1] A data management system that manages data acquired from an analysis target has a storage device and a processing device. The storage device stores ontology database format information indicating a predetermined database format, acquired data acquired from the analysis target, and an analysis application that performs analysis using a database in which the data acquired from the analysis target is recorded as input and outputs analysis results. The processing device compares the format of the acquired data with a format indicated in the ontology database format information, generates a provisional database in which the acquired data fits the format indicated in the ontology database format information, executes the analysis application using the provisional database as input, acquires verification results including a status and / or results of execution of the analysis application, and estimates how well the provisional database is suited to the analysis application based on the verification results. This makes it possible to estimate whether the data is suitable for properly executing the application.

[0075] [Matter 2] In the data management system described in item 1, the processing device regards a portion of the acquired data that corresponds to at least one of the data structure of the database, the database name of the database, the column names of the columns that make up the database, and the properties of the values ​​that the columns can take, as indicated in the ontology database format information, as a data feature corresponding portion, and generates a provisional database by fitting the data feature corresponding portion into the format of the ontology database format information. This allows appropriate fitting into ontology database format information using clues such as the data structure, database name, column name, and the properties of the values ​​that the columns can take.

[0076] [Matter 3] In the data management system described in item 2, if the data feature corresponding portion is within a range of values ​​previously expected as an input for an analysis application, the processing device uses the data feature corresponding portion in the provisional database. This makes it possible to generate a hypothetical database based on data within the expected range of the analysis application.

[0077] [Matter 4] In the data management system described in item 1, the processing device generates multiple different provisional databases, obtains verification results for each of the multiple provisional databases, ranks the multiple provisional databases based on the multiple verification results, and displays the rankings of the provisional databases. This makes it possible to know a provisional database into which valid data has been fitted.

[0078] [Matter 5] In the data management system described in item 4, the processing device obtains as verification results the following: operation normality, indicating whether the analytical application operated normally; output value normality, indicating whether the data output from the analytical application is within a predetermined normal range; digit deficiency, indicating whether the cause is an insufficient number of digits in the value of the acquired data if the analytical application did not operate normally or the data output from the analytical application is outside the normal range; data number deficiency, indicating whether the cause is an insufficient number of acquired data items if the analytical application did not operate normally or the data output from the analytical application is outside the normal range; and item deficiency, indicating whether the cause is an insufficient item of the acquired data if the analytical application did not operate normally or the data output from the analytical application is outside the normal range, and ranks the multiple provisional databases based on the verification results. This makes it possible to rank the provisional databases by comprehensively taking into consideration a number of criteria, namely, operational normality, output value normality, insufficient number of digits, insufficient number of data, and insufficient number of items.

[0079] [Matter 6] In the data management system described in item 5, the processing device ranks the analysis application first if the analysis application operates normally and the data output from the analysis application is within the normal range, ranks the analysis application second if the analysis application does not operate normally or the data output from the analysis application is outside the normal range and the cause is an insufficient number of data, and ranks the analysis application third if the analysis application does not operate normally or the data output from the analysis application is outside the normal range and the cause is an insufficient number of items. This makes it possible to perform appropriate ranking according to the suitability of the provisional database for the analysis application.

[0080] [Matter 7] In the data management system described in item 6, the storage device further stores a second analysis application different from the analysis application, which is an application that performs analysis using a database in which data obtained from the analysis target is recorded as input and outputs analysis results, and the processing device further executes the second analysis application using a provisional database as input, obtains second verification results including the status and / or results of the execution of the second analysis application, and further ranks the provisional databases that have the same ranking based on the verification results obtained by the analysis application based on the second verification results. This allows provisional databases that would receive the same ranking when ranked based on the first analysis application to be ranked in more detail based on the second analysis application.

[0081] [Matter 8] In the data management system described in item 6, the storage device further stores analysis results output when the analysis application was normally executed in the past, and the processing device calculates a correlation value between the analysis results obtained by executing the analysis application using the provisional database as input and the analysis results stored in the storage device, and further ranks the provisional databases that have the same ranking based on the verification results obtained by the analysis application based on the correlation value. This allows for more detailed ranking based on the correlation values.

[0082] [Matter 9] In the data management system described in item 4, the processing device identifies blank spaces in the provisional databases that have not been entered with values ​​from the acquired data for provisional databases that are ranked at or above a predetermined rank, and presents the provisional databases and the blank spaces. This makes it possible to present to the user information about those databases that have been ranked at or above a predetermined rank as a result of the prioritization among the multiple temporary databases that have been generated, and to prompt the user to take the next action.

[0083] [Matter 10] In the data management system described in item 9, the processing device compares data features, which are at least one of the data structure of the database, the database name of the database, the column names of the columns that make up the database, and the properties of the values ​​that the columns can take, indicated in the ontology database format information, with the data features of the acquired data and presents the differences. This allows the user to be presented with any inconsistencies in the data characteristics between the analysis target and the ontology database format information, and allows the user to modify the data characteristics of the analysis target to fit it into more suitable ontology database format information.

[0084] [Matter 11] In a data management method for managing data acquired from an analysis target using a data management system having a storage device and a processing device, the storage device stores ontology database format information indicating a predetermined database format, acquired data acquired from the analysis target, and an analysis application that performs analysis using the database in which the data acquired from the analysis target is recorded as input and outputs the analysis results, and the processing device compares the format of the acquired data with the format indicated in the ontology database format information, generates a provisional database in which the acquired data fits the format indicated in the ontology database format information, executes the analysis application using the provisional database as input, obtains verification results including the status and / or results of execution of the analysis application, and estimates how well the provisional database is suited to the analysis application based on the verification results. This makes it possible to estimate whether the data is suitable for properly executing the application. [Explanation of symbols]

[0085] 1...data management system, 11...processing device, 111...data classification unit, 112...application execution unit, 113...judgment processing unit, 114...information presentation unit, 12...storage device, 121...customer database, 122...application, 13...main memory, 14...communication device, 15...input device, 16...display device, 2...analysis target, 3...analysis application, 4...communication network, 5...user terminal

Claims

1. A data management system for managing data acquired from an analysis target, A storage device and a processing device are included. the storage device stores ontology database format information indicating a predetermined database format, acquired data acquired from the analysis target, and an analysis application that performs an analysis using a database in which the data acquired from the analysis target is recorded as input, and outputs an analysis result; The processing device includes: comparing a format of the acquired data with a format indicated in the ontology database format information, and generating a provisional database in which the acquired data is fitted to the format indicated in the ontology database format information; Executing the analytical application using the provisional database as an input to obtain a verification result including a status and / or a result of the execution of the analytical application; estimating how well the provisional database suits the analytical application based on the validation results; Data management system.

2. The processing device includes: a portion of the acquired data that corresponds to at least one of the data structure of the database, the database name of the database, the column names of the columns constituting the database, and the properties of values ​​that the columns can take, which are indicated in the ontology database format information, is determined as a data feature corresponding portion, and the provisional database is generated by fitting the data feature corresponding portion into the format of the ontology database format information. The data management system according to claim 1 .

3. The processing device includes: If the data feature corresponding portion has a value within a range that is previously expected as an input for the analysis application, the data feature corresponding portion is used in the provisional database. The data management system according to claim 2.

4. The processing device includes: Generate several different hypothetical databases, Obtaining a verification result for each of the plurality of provisional databases; Ranking the provisional databases based on the verification results; Displaying the ranking of the provisional database; The data management system according to claim 1 .

5. The processing device includes: the verification results are obtained as follows: operation normality, as to whether the analytical application has operated normally; output value normality, as to whether the data output from the analytical application is within a predetermined normal range; digit deficiency, as to whether the cause of the analytical application not operating normally or the data output from the analytical application being outside the normal range is an insufficient number of digits of the acquired data; data number deficiency, as to whether the cause of the analytical application not operating normally or the data output from the analytical application being outside the normal range is an insufficient number of pieces of the acquired data; and item deficiency, as to whether the cause of the analytical application not operating normally or the data output from the analytical application being outside the normal range is an insufficient item of the acquired data; ranking the plurality of provisional databases based on the verification results; The data management system according to claim 4.

6. The processing device includes: If the analysis application operates normally and the data output from the analysis application is within the normal range, the analysis application is ranked first; If the analysis application did not operate normally or the data output from the analysis application was outside the normal range and the cause was an insufficient amount of data, the ranking was set to second; If the analysis application does not operate normally or the data output from the analysis application is outside the normal range and the cause is a lack of items, the analysis application is ranked third. The data management system according to claim 5.

7. the storage device further stores a second analysis application that is different from the analysis application and that performs analysis using a database in which data acquired from the analysis target is recorded as input and outputs an analysis result; The processing device includes: further executing the second analysis application using the provisional database as an input, and obtaining a second verification result including a status and / or a result of the execution of the second analysis application; further ranking the provisional databases that have the same ranking based on the verification result obtained by the analysis application based on the second verification result; The data management system according to claim 6.

8. The storage device further stores an analysis result output when the analysis application was successfully executed in the past; The processing device includes: Calculating a correlation value between an analysis result obtained by executing the analysis application using the provisional database as an input and an analysis result stored in the storage device; further ranking the provisional databases that have the same ranking based on the verification result obtained by the analysis application based on the correlation value; The data management system according to claim 6.

9. The processing device includes: Identifying blank spaces in the provisional databases that are not filled with values ​​from the acquired data, for the provisional databases that are ranked at or above a predetermined rank; Presenting the provisional database and the blank space; The data management system according to claim 4.

10. The processing device includes: comparing data features, which are at least one of the data structure of the database, the database name of the database, the column names of the columns constituting the database, and the properties of values ​​that the columns can take, indicated in the ontology database format information, with data features of the acquired data, and presenting differences; The data management system according to claim 9.

11. A data management method for managing data acquired from an analysis target by a data management system having a storage device and a processing device, comprising: the storage device stores ontology database format information indicating a predetermined database format, acquired data acquired from the analysis target, and an analysis application that performs an analysis using a database in which the data acquired from the analysis target is recorded as input, and outputs an analysis result; The processing device comprises: comparing a format of the acquired data with a format indicated in the ontology database format information, and generating a provisional database in which the acquired data is fitted to the format indicated in the ontology database format information; Executing the analytical application using the provisional database as an input to obtain a verification result including a status and / or a result of the execution of the analytical application; estimating how well the provisional database suits the analytical application based on the validation results; Data management methods.

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