JP2019087199A - Data management system, data management method, and data management program - Google Patents

Abstract

To properly manage data acquired from a data source.SOLUTION: A server 110 and a data store apparatus 130 for managing data to be acquired from predetermined data sources 102 include: a data store 140 which stores data from the data sources 102; a utilization calculation unit which calculates utilization degree indicating effectiveness on using data analysis of data of the data sources 102, on the basis of statistical information on the contents of the data; and an action execution unit which executes predetermined processing operation corresponding to an action condition with respect to the data when the action condition including a condition on the calculated utilization degree is satisfied. The utilization calculation unit may calculate utilization degree for data in a predetermined period of the data source.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a data management system or the like that manages data obtainable from a data source.

  Data of operation information of various devices and systems, data of video information and audio information acquired from camera, and data collected from sensors mounted on persons and devices etc. are stored and visualized using these data -Attempts to extract important information through analysis and analysis are expanding.

  However, if all data such as sensors and operation logs, which occur frequently and become huge, are accumulated and held, appropriate data can be retrieved from the huge amount of data when utilizing the data. Is also difficult. The cost of maintaining huge amounts of data increases.

  With such data, not all data are necessarily used for analysis etc., so methods should be considered to reduce data that is unlikely to be used.

  To address such issues, for example, when storing data, stream data processing etc. is performed before data storage, and data selection and aggregation are performed to reduce the amount of data stored. The method is known. Also, all data is temporarily stored and accumulated, and data for which a certain period has elapsed since accumulation and data that has not been used for a certain period are periodically moved or deleted to a backup environment etc. Methods are also known. For example, as a technology for moving data with low access frequency to a lower tier, the technology described in Patent Document 1 is known.

JP, 2010-257094, A

  For example, when data is stored, stream data processing etc. is performed prior to data storage, and data selection and aggregation are performed to reduce the amount of data stored. When the selection and consolidation are performed, necessary data may be deleted, and important data may not be obtained at the time of data utilization.

  On the other hand, in consideration of the case where the storage destination is moved or data is deleted for data for which a certain period has elapsed since data accumulation and data that has not been used for a certain period, the later data It may be considered that it is not appropriate to judge the data necessary for utilization simply by the accumulated period and the frequency of use. For example, in the case of analyzing past data, when a long period of time has passed since data were stored, the possibility of suddenly using data that has not been used at all before to analyze the past situation May occur. In such a case, the necessary data may be deleted, or it may be difficult to find the necessary storage location of the data.

  The present invention has been made in view of the above circumstances, and an object thereof is to provide a technology capable of appropriately managing data acquired from a data source.

  In order to achieve the above object, a data management system according to one aspect is a data management system for managing data obtainable from a predetermined data source, and a storage unit for storing data from the data source, and contents of the data The utilization degree calculation unit which calculates the utilization degree indicating the degree of effectiveness regarding the utilization of data analysis of data of the data source based on statistical information on the data, and an action condition including a condition regarding the calculated utilization degree, And an action execution unit that executes a predetermined processing operation corresponding to an action condition on data.

  According to the present invention, data acquired from a data source can be appropriately managed.

FIG. 1 is an entire configuration diagram of a computer system according to a first embodiment. FIG. 2 is a functional block diagram of a data management program according to the first embodiment. FIG. 3 is a configuration diagram of a data source management table according to the first embodiment. FIG. 4 is a block diagram of utilization degree data according to the first embodiment. FIG. 5 is a block diagram of metadata according to the first embodiment. FIG. 6 is a configuration diagram of catalog data according to the first embodiment. FIG. 7 is a configuration diagram of an action definition table according to the first embodiment. FIG. 8 is a flowchart of metadata management processing according to the first embodiment. FIG. 9 is a flowchart of data acquisition processing according to the first embodiment. FIG. 10 is a flowchart of utilization degree calculation processing according to the first embodiment. FIG. 11 is a diagram for explaining utilization calculation according to the first embodiment. FIG. 12 is a flowchart of action management processing according to the first embodiment. FIG. 13 is a flowchart of action execution processing according to the first embodiment. FIG. 14 is a flowchart of catalog management processing according to the first embodiment. FIG. 15 is a diagram illustrating an example of a data source search screen according to the first embodiment. FIG. 16 is a diagram illustrating an example of a catalog evaluation screen according to the first embodiment. FIG. 17 is an entire configuration diagram of a computer system according to a second embodiment. FIG. 18 is a diagram showing the contents stored in the related data according to the second embodiment. FIG. 19 is a diagram for explaining calculation of the degree of association according to the second embodiment. FIG. 20 is an entire configuration diagram of a computer system according to a third embodiment. FIG. 21 is a configuration diagram of a user management table according to the third embodiment. FIG. 22 is a configuration diagram of metadata according to the third embodiment.

  Several embodiments will be described with reference to the drawings. The embodiments described below do not limit the invention according to the claims, and all of the elements described in the embodiments and their combinations are essential for the solution means of the invention. There is no limit.

  In the following description, information may be described by the expression “AAA table”, but the information may be expressed by any data structure. That is, the "AAA table" can be called "AAA information" to indicate that the information does not depend on the data structure.

  FIG. 1 is an entire configuration diagram of a computer system according to a first embodiment.

  The computer system 1 includes one or more devices (also referred to as assets) 101, a gateway 103, a server 110, a data store device 130, and a backup data store 160. These configurations are connected by, for example, a wired or wireless network. The asset 101 may include small to large-sized ones (construction machines) and the like, and the asset 101 may also include, for example, equipment, equipment, and devices. The gateway 103 and the backup data store 160 may not be provided in the computer system 10. Here, the server 110 and the data store device 130 constitute a data management system. Although the server 110 and the data store apparatus 130 are separately configured in FIG. 1, the present invention is not limited to this, and the server 110 and the data store apparatus 130 are configured by one computer. It is also good.

  The asset 101 includes, for example, one or more data sources 102. The data source 102 may be a sensor that sequentially outputs time-series data, or may be a storage device that stores various operation logs for the asset 101.

  The gateway 103 communicably connects the data source 102 of the asset 101 and the server 110. For example, when new data is generated in the data source 102, the gateway 103 responds to the function of notifying the server 110 of that effect, or in response to an inquiry about the generation of new data in the data source 102 from the server 110. It may have a function.

  The server 110 is configured by, for example, a computer, and includes a central processing unit (CPU) 111, a memory 120, a storage device 112, and a network adapter 113.

  The CPU 111 executes various programs by executing a program stored in the memory 120. The network adapter 113 connects the server 110 to the network to enable communication with other devices via the network. The memory 120 is, for example, a RAM (Random Access Memory), and stores a program executed by the CPU 111 and various data used by the CPU 111. In the present embodiment, the memory 120 stores a data management program 121 for managing data, a data source management table 122, and an action definition table 123. The details of the data management program 121, the data source management table 122, and the action definition table 123 will be described later. The storage device 112 is, for example, a non-temporary storage device (nonvolatile storage device) such as a hard disk drive (HDD) or a solid state drive (SSD), and stores programs executed by the CPU 111 and various information.

  The data store apparatus 130 is configured by, for example, a computer, and includes a CPU 131, a memory 132, a storage device 134, and a network adapter 135.

  The CPU 131 executes various processes by executing a program stored in the memory 132. The network adapter 135 connects the data store device 130 to the network to enable communication with other devices via the network. The memory 132 is, for example, a RAM, and stores programs executed by the CPU 131 and various data used by the CPU 131. In the present embodiment, the memory 132 stores a data store control program 133 that controls a data store. The data store control program 133 will be described later.

  The storage device 134 is, for example, a non-temporary storage device (nonvolatile storage device) such as an HDD or an SSD, and stores programs executed by the CPU 131 and various information. In the present embodiment, the storage device 134 holds a data store 140 and management data 150. The data store 140 stores one or more data 141 acquired from one or more data sources 102 and a catalog 142 in which the one or more data 141 is collected. The management data 150 stores utilization degree data 151, metadata 152, and catalog data 153.

  FIG. 2 is a functional block diagram of a data management program according to the first embodiment.

  The data management program 121 is executed by the CPU 111 so that the data acquisition unit 201, the usage calculation unit 202, the metadata management unit 203, the action management unit 204, the action execution unit 205, and the evaluation value reception unit A program is included which includes functional units of a catalog management unit 206 as an example of the data processing unit and a data search unit 207 as an example of the input receiving unit and the display control unit. The processing by each functional unit will be described later.

  Next, the data source management table 122 stored in the memory 120 of the server 110 will be described.

  FIG. 3 is a configuration diagram of a data source management table according to the first embodiment.

  The data source management table 122 is a table for managing the data sources 102 in one or more assets 101, and stores entries corresponding to each data source 102. The entries of the data source management table 122 include a data source id column 301, a data division column 302, a data type / unit column 303, a corresponding object column 304, a target section column 305, and a main component analysis target data id column 306. And a calculation execution trigger column 307.

  The data source id column 301 stores the id (Identifier) (data source id) of the data source corresponding to the entry. The data source id may be simply a list of alphanumeric characters, but may be information indicating the type or name of the data source in consideration of the ease of data utilization. In this embodiment, for example, the data source id [Asset1: Sensor1] indicates that the sensor 102 with an id of “Sensor1” mounted on the Asset101 with an id of “Asset1” is a data source. .

  The data division column 302 stores the data source division corresponding to the entry. The classification of data source is “measurement value” indicating the measurement value of sensor etc., “set value” indicating a fixed value which does not change with time and is set manually to asset 101, and character string There are "character string" indicating that, "label value" indicating that it is a label value, and "binary" indicating that it is a binary value.

  The data type / unit column 303 stores the data type and unit of data of the data source corresponding to the entry. For example, if the data is temperature information and the unit is degree Celsius, “‘ Temperature ′: ‘° C ′ ′ is stored in the data type / unit column 303. In the corresponding object column 304, an identifier of an object in the data store 130 for data of a data source corresponding to the entry is stored.

  The target section column 305 stores a section (target section) to be a target of utilization degree calculation processing described later that calculates the utilization degree of data of the data source corresponding to the entry. The unit of the target section may be, for example, a unit of msec (milliseconds). For example, if the target section is 3,600,000, utilization degree calculation processing is executed on data for each hour. Based on the contents of the target section column 305, it is possible to appropriately set a target section for which the degree of utilization is calculated for each data resource.

  In the main component analysis target data id column 306, id (reference data id) of data of a data source serving as a reference for calculating the factor loading amount in the main component analysis is stored. The principal component analysis target data id column 306 is not necessary when the utilization calculation by the principal component analysis is not performed. The computer execution opportunity column 307 stores an opportunity (calculation execution opportunity) for executing the calculation of the utilization degree. For example, “Continuous execution” is stored in the calculation execution opportunity column 307 when performing the calculation of utilization for each target section, and when the utilization calculation processing is performed at 0 o'clock every day, “00 “Execute at 00:00” is stored. Note that the calculation execution trigger column 307 may take into account the load on the server 110 and the data store apparatus 130, and may be specified based on the case where there are enough resources in the system as a trigger. You may store the content to be. If the data source corresponding to the entry does not require the utilization calculation process, the calculation execution trigger column 307 stores “do not calculate” indicating that the data is not to be calculated. Good.

  Next, utilization degree data 151 stored in the storage device 134 of the data storage device 130 will be described.

  FIG. 4 is a block diagram of utilization degree data according to the first embodiment.

  The storage device 134 holds utilization degree data 151 corresponding to each data source 102. FIG. 4 shows an example of utilization data of a single data source “Asset1: Sensor1”.

  The utilization degree data 151 includes an entry indicating the utilization degree for each target section. The entries of the utilization data 151 include an ID column 401, a target section column 402, an acquisition frequency column 403, a defect rate column 404, a fluctuation rate column 405, a standard deviation column 406, and a factor loading amount column 407, Other index column 408 and utilization degree column 409 are included.

  The ID column 401 stores a serial ID corresponding to the entry. The target section column 402 stores information on the start position and the end position at which data corresponding to the target section is sampled. In the present embodiment, the start position and the end position are time information (for example, date, time, hour, minute and second).

  In columns 403 to 408, indexes (statistical information and the like) calculated based on the contents (values) of data sampled in the target section stored in the target section column 402 are stored. The acquisition frequency column 403 stores the data acquisition frequency (for example, times / sec) in the data source corresponding to the entry. The missing rate column 404 stores the occurrence rate (missing rate) of incorrect values in the data source corresponding to the entry. Here, the incorrect value may include, for example, no data, a value indicating no value (value indicating that data could not be obtained), and a value in a range that can not be obtained by the sensor. The fluctuation rate column 405 stores the fluctuation rate of the value of data in the data source corresponding to the entry. The method of calculating the rate of change of the data values will be described later. The standard deviation column 460 stores the standard deviation of data in the data source corresponding to the entry. The factor loading amount column 407 stores a factor loading amount for data corresponding to the reference data id of the entry of the data source management table 122 corresponding to the data source corresponding to the entry. The other index column 408 stores other indexes. Other indexes include, for example, an average value, maximum value, minimum value, and an interval (missing interval) at which a missing value appears. The utilization degree column 409 stores the utilization degree of the data in the target section corresponding to the entry. The method of calculating the degree of utilization to be stored will be described later.

  Next, the metadata 152 stored in the storage device 134 of the data store apparatus 130 will be described.

  FIG. 5 is a block diagram of metadata according to the first embodiment.

  The storage device 134 holds metadata 152 corresponding to each data source 102. FIG. 5 shows an example of metadata of a single data source “Asset1: Sensor1”. The metadata shown in FIG. 5 is an example described in JSON (JavaScript Object Notation (JavaScript is a registered trademark)) format.

  The metadata 152 includes “data source id”, “owner”, “creation date”, “update date”, and “last access date” for data of the data source corresponding to the metadata 152, The items “data type”, “data unit”, “utilization degree”, and “corrected” are described. Note that these items are not all essential. In addition to these items, various items useful for data utilization may be added to the metadata 152. Furthermore, new attributes may be added to the metadata 152 as needed.

  The “data source id” indicates the data source id of the data source corresponding to the metadata 152. Specifically, “asset id” indicating an identifier of Asset, and “sensor id” indicating an identifier of a sensor In the example of FIG. 5, “asset id” is described as “Asset 1”, and “Sensor id” is described as “Sensor 1”.

  “Owner” indicates the owner of data of the data source corresponding to the metadata 152, and in the example of FIG. 5, indicates that the user corresponding to the owner id “44123” owns There is. The “creation date” indicates the creation date of the data object of the data source corresponding to the metadata 152. The “update date” indicates an update date of an object of data of the data source corresponding to the metadata 152. The “last access date” indicates the last date when the object of the data of the data source corresponding to the metadata 152 is accessed. “Data type” indicates the type of data of the data source corresponding to the metadata 152. “Data unit” indicates a unit of data of the data source corresponding to the metadata 152. The values acquired from the data source management table 122 are set as the “data type” and the “data unit”. The “utilization degree” indicates the utilization degree of data of the data source corresponding to the metadata 152. "Modified" indicates whether or not data complementation processing has been performed. When data complementation processing or the like is performed, "Yes" is set to "Modified", and "No" is set if complementation processing or the like is not performed.

  Next, catalog data 153 stored in the storage device 134 of the data storage device 130 will be described.

  FIG. 6 is a configuration diagram of catalog data according to the first embodiment.

  The storage device 134 holds catalog data 153 corresponding to each catalog 142. FIG. 6 shows an example of catalog data of a single catalog "catalog 1". The catalog data shown in FIG. 6 is an example described in JSON format.

  The catalog data 153 includes “catalog id”, “creator”, “creation date”, “update date”, “last access date”, and “evaluation” for the catalog corresponding to the catalog data 153. , “Data list”, “creator role”, and “description” are described. Note that these items are not all essential. In addition to these items, various items useful for data utilization may be added to the catalog data 153. Furthermore, new attributes may be added to the catalog data 153 as needed.

  “Catalog id” indicates the id of the catalog 142, and in the example of FIG. 5, “catalog id” is described as “Catalog 1”.

  “Creator” indicates the creator of the catalog 142 corresponding to the catalog data 153, and the example in FIG. 6 indicates that the user corresponding to the id “3323” has created. The “creation date” indicates a creation date of the catalog 142 corresponding to the catalog data 153. The "update date" indicates the update date of the catalog 142 corresponding to the catalog data 153. The “last access date” indicates the last date when the catalog 142 corresponding to the catalog data 153 was accessed. “Evaluation” indicates the result of the user evaluating whether the catalog 142 corresponding to the catalog data 153 is effective for data utilization. “Data list” indicates a list of data sources belonging to the catalog 142 corresponding to the catalog data 153. Also, "data list" can include references to data such as files and objects other than data sources. In FIG. 6, a file name such as “filename1.aaa” is specified as the “file”. “Creator role” indicates the role (role) of the creator of the catalog 142 corresponding to the catalog data 153. Based on this role information, the catalog management unit 206 can automatically add "evaluation" to a catalog created by an expert. The “description” indicates the description of the catalog 142 corresponding to the catalog data 153. The user can refer to this explanation when using the catalog 142.

  Next, the action definition table 123 stored in the memory 120 of the server 110 will be described.

  FIG. 7 is a configuration diagram of an action definition table according to the first embodiment.

  The action definition table 123 is a table for managing an action (processing operation) to be executed based on a condition (action condition) including a condition regarding utilization, and stores entries corresponding to each of the condition and the set of actions. An entry of the action definition table 123 includes an ID column 701, a name column 702, a condition column 703, an action content column 704, and a determination timing column 705.

  The ID column 701 stores an ID corresponding to the action definition. The name column 702 stores the name of the action definition corresponding to the entry. The condition column 703 stores conditions (action conditions) for executing the action of the action definition corresponding to the entry. The action condition may include a condition regarding statistical information of data in addition to the condition regarding utilization.

  The action content column 704 stores the content of the action to be executed when the action condition of the condition column 703 corresponding to the entry is met.

  The determination timing column 705 stores the timing at which the determination of the condition of the condition column 703 corresponding to the entry is performed. The determination timing may be, for example, every other day (eg, every day at 0 o'clock), every other month, at the time of data update or the like. Note that the load on the server 110 and the data store apparatus 130 may be taken into consideration, and a case where there is a margin in the system may be designated as the determination timing, and there is an instruction of the administrator of the system (server 110 and data store apparatus 130). The specified time may be designated as the determination timing.

  In the first entry (row) of the action definition table 123, the action condition is that the last update date is one year or more ago and the degree of utilization is 10 or less for the entire data (all target sections) The action content is to execute processing of moving one year's worth of data of the target data source to the archive data store. According to this action definition, data can be moved to the archive in consideration of utilization as well as the last update date and time.

  In the second entry (row) of the action definition table 123, the action condition is that the utilization degree is less than or equal to a predetermined value (for example, 30) with respect to data of the latest target section, and the update frequency is predetermined Time (1 sec) or less is to be performed, and the action content is to execute processing of moving data of the target section to the archive, leaving an average value of 1 sec of data of the target section. There is. According to this action definition, data can be thinned out and stored for data of low utilization, and the amount of data stored in the data store 140 can be reduced.

  Also, in the third entry (row) of the action definition table 123, the action condition is 50% or more in utilization level, the defect rate is a predetermined value (5%) or less, and the value of the predetermined data source is a predetermined value The action content is to execute a process of calculating a missing value and calculating an average value of previous and subsequent values to complement the action. According to this action definition, missing values of highly utilized data can be appropriately compensated. The action condition may not include the condition that the value of the predetermined data source is equal to or less than the predetermined value.

  Next, the processing operation of the computer system 10 according to the first embodiment will be described.

  FIG. 8 is a flowchart of metadata management processing according to the first embodiment.

  Metadata management processing is processing executed by the metadata management unit 203. The metadata management process may be performed periodically, for example.

  The metadata management unit 203 acquires data source management information (step 2031). As a method of obtaining data source management information, for example, import from an asset management system (EAM: Enterprise Asset Management etc.) existing in an external system (not shown) or the like, a definition file or the like, or data source management information It may be obtained from the user via the network. As data source management information, various kinds of information registered in the data source management table 122, for example, data source id, measured value or set value as data division, data type / unit, target section necessary for calculation of utilization degree This is information such as id of reference data used when performing principal component analysis, calculation execution timing of utilization degree calculation, and the like.

  The metadata management unit 203 updates the data source management table 122 based on the acquired data source management information (step 2032).

  Next, the metadata management unit 203 corresponds to the data source for which the data source management information has been acquired in step 2031 (referred to as the corresponding data source in the description of this process) for the data store 140 of the data store apparatus 130 An object is created, and identification information of the created object in the data store 140 is stored in the corresponding object column 304 of the entry corresponding to the corresponding data source in the data source management table 122 (step 2033). However, when an object corresponding to the corresponding data source already exists in the data store 140, it is not necessary to create a new object.

  Further, the metadata management unit 203 generates and stores the metadata 152 for the corresponding data source (step 2034). Metadata 152 to be generated is metadata as described in FIG. Referring to the contents described in FIG. 5, “data source id”, “data type”, “data unit” and the like set the information acquired from the data source management table 122. Furthermore, for "Created Date", set the time when the object was newly created. The "update date" and the "last access date" are updated based on information obtained by the data store 140 detecting an update and access to the corresponding object. For "utilization degree", for example, a predetermined default value such as 0.5 is set. For "corrected", "No" indicating uncorrected immediately after creation is set. If there are other useful attributes, they may be generated at the time of execution of this step.

  FIG. 9 is a flowchart of data acquisition processing according to the first embodiment.

  The data acquisition process is a process executed by the data acquisition unit 201. The data acquisition process is performed, for example, when it is detected that new data has occurred in the data source 102. The occurrence of new data in the data source 102 may be notified from, for example, the Gateway 103, or may be confirmed with the Gateway 103. Also, the data acquisition unit 201 may execute a plurality of data acquisition processes in parallel in order to acquire data from a plurality of data sources 102.

  The data acquisition unit 201 acquires data from the data source 102 of the Asset 101 (Step 2011). Next, the data acquiring unit 201 applies the corresponding data to the acquired data of the data source 102 (referred to as the corresponding data source in the description of this process) based on the id of the corresponding data source and the contents of the data source management table 122. The update to the object of the data store 140 corresponding to the source is instructed (step 2012). Next, the data acquisition unit 201 updates the metadata 152 of the corresponding data source (for example, update “update date”, etc.) (step 2013).

  FIG. 10 is a flowchart of utilization degree calculation processing according to the first embodiment.

  The utilization degree calculation process is a process executed by the utilization degree calculation unit 202. The utilization calculation process is performed, for example, periodically. The utilization calculation process is performed on each data source whose entry is registered in the data source management table 122.

  First, the utilization degree calculation unit 202 confirms the execution condition of the utilization degree calculation (step 2021). Specifically, the utilization degree calculation unit 202 confirms the content of the calculation execution trigger column 307 of the entry corresponding to the processing target data source of the data source management table 122.

  The contents of the calculation execution trigger column 307 are conditions such as “continuous execution” and “execute every day at 00:00:00”, but there is a margin in system resources in consideration of the load of the server 110 and the data storage device 130. It is also possible to specify a method triggered by a case or a method triggered by an instruction from a system administrator. For example, if there is enough system resources, the utilization calculation unit 202 monitors information such as the resource utilization of the system, and if the combination of one or more resources of the system is equal to or less than a certain threshold, the utilization is used. The calculation of the degree (the process after step 2023) may be performed. For example, if the usage rate of the CPU 131 of the hardware (in this example, the data store apparatus 130) operating the data store 140 is equal to or less than a predetermined value (for example, 30% or less) Good. If the corresponding data source is not the target of utilization calculation, that is, if the content of the calculation execution trigger column 307 of the entry corresponding to the processing target data source of the data source management table 122 is not calculated, the step The processing after 2023 is not executed.

  Next, the utilization calculation unit 202 determines whether the execution condition of the utilization calculation checked in step 2021 is satisfied (step 2022). As a result, when it is determined that the execution condition is not satisfied ((2) Step 2022: No), the process proceeds to step 2021 to continue the confirmation of the condition.

  On the other hand, when the execution condition is satisfied (step 2022: Yes), the utilization calculation unit 202 uses the data source management table of the identification information of the object corresponding to the target section of the corresponding data source and the corresponding data source of the data store 140. The data of the target section acquired from the data source 122 (hereinafter, target section data) is acquired (step 2023).

  Then, the utilization calculation unit 202 calculates the utilization based on the acquired data, and holds the utilization and the related information obtained by the calculation as the utilization data 151 (step 2024). Here, the related information is, for example, information to be set in columns 401 to 408 of the utilization degree data 151.

  The calculation of the utilization degree by the utilization degree calculation unit 202 will be specifically described below. In the following description, it is assumed that the target section data is time series data (sequence of combinations of time and value), and the utilization data 151 has the content shown in FIG.

  The utilization calculation unit 202 calculates the acquisition frequency, the loss rate, and the fluctuation rate according to the following formulas (1), (2), and (3).

Acquisition frequency [times / sec] = 1 / average time of difference between certain time of target section data and next time [s] (1)
Loss rate = number of incorrect values in target section data / number of all data strings (2)
Rate of change = difference between values of data strings of target section data for each time / number of all data strings (3)

  Further, the utilization degree calculation unit 202 calculates standard deviation, average value, maximum value, minimum value, loss interval, and factor loading amount with respect to the value of the data string of the target section data.

  The missing interval is the average value of the difference between the time with the incorrect value and the time with the next incorrect value. In addition, the factor load amount is the same as Asset (data source id is Asset 1 for data whose id is stored in the principal component analysis target data id column 306 of the data source management table 122 of the corresponding data source). The factor loading amount of the corresponding data source when performing principal component analysis with the data source of the device as a variable.

  The utilization calculation unit 202 calculates the utilization using at least one of the related information described above. The utilization level may be selected from among a plurality of utilization level calculation methods by the user, or may be defined by the user.

  For example, with regard to data sources that are not set values among data sources (in which the data division column 302 of the data source management table 122 is not set values), the degree of utilization of data sources with small change in value is lowered and the values are rich in change. When the degree of utilization of the data source is to be increased, for example, the degree of utilization may be determined by equation (4).

Utilization degree = (α × change rate / β × acquisition frequency + γ) × standard deviation ... (4)
Here, α, β and γ are preset constants.

  In addition, when it is easy to select a data source with a low defect rate (that is, to increase the application rate), the right side of equation (4) is multiplied by (1-deletion rate) to increase the application rate. It may be calculated.

  After calculating the related information and the use degree, the use degree calculation unit 202 adds / updates information on the corresponding target section, the related information, and the use degree to the use degree data 151 of the corresponding data source.

  Next, the utilization calculation unit 202 updates the utilization of the metadata 152 of the corresponding data source based on the utilization stored in the utilization data 151 of the corresponding data source (Step 2025). Here, in the present embodiment, it is assumed that the utilization of the metadata 152 is, for example, the average value of the utilizations of all sections of the corresponding data source. The utilization degree of the metadata 152 may be the utilization degree of the latest section.

  Next, a specific example of calculation of the utilization degree by the utilization degree calculation unit 202 will be described.

  FIG. 11 is a diagram for explaining utilization calculation according to the first embodiment.

  The example of FIG. 11 is an example in which the variation rate is calculated by the above-described equation (3), and the utilization degree is calculated by the equation (4). The constants α, β and γ in the equation (4) are each set to 1.

  FIG. 11 is an example in which the degree of utilization is calculated with respect to time-series data of the data source 1102 (data source Asset1: Sensor2) and the data source 1104 (data source Asset3: Sensor5). In this example, as time series data, data of a row of time and a value at that time (for example, a measurement value of a sensor) is used.

  Calculating related information and utilization for data source 1102, as shown in calculation result 1103, the standard deviation is 0.35, the acquisition frequency is 0.5 / sec, the fluctuation rate is 0.2, and utilization The degree is 0.75.

  On the other hand, calculating related information and utilization for data source 1104, as shown in calculation result 1105, the standard deviation is 31, the acquisition frequency is 0.5 / sec, the fluctuation rate is 20.7, and utilization The degree is 72.3.

  When the degree of utilization of the data source 1102 and the data source 1104 is compared, the data source 1104 has a higher degree of utilization. That is, it is shown that the data source 1104 is more effective in data analysis when it is used for data analysis.

  Although the data source data is described as time series data in the example of FIG. 11, the present invention is not limited to this, and for example, it may not be time series data as long as it is combination data of a key and a value. . In this case, although the acquisition frequency can not be calculated, equivalent data can be acquired by calculating the difference between the key values as long as the data has a matching key value. In addition, the target section may be designated in the range of key values. For example, if the key value is a sequential number, it may be designated by a range for this number.

  FIG. 12 is a flowchart of action management processing according to the first embodiment.

  The action management process is a process executed by the action management unit 204. The action management process may be performed periodically, for example.

  The action management unit 204 acquires action definition information (step 2041). The information acquired as action definition information is, for example, information (name, condition, action content, determination timing, etc.) held in the action definition table 123 shown in FIG. The action definition information may be acquired, for example, when the action management unit 204 reads and acquires a predetermined definition file, or when acquiring an input from a user by providing a UI (User Interface). When the action definition table 123 is registered in advance, the action definition table 123 may not be acquired.

  Next, the action management unit 204 updates the action definition table 123 based on the contents of the action definition information acquired in step 2041 (step 2042).

  FIG. 13 is a flowchart of action execution processing according to the first embodiment.

  The action execution process is a process executed by the action execution unit 205. The action execution process may be performed periodically, for example.

  The action execution unit 205 confirms the determination timing of the action definition (step 2051). Here, the action execution unit 205 acquires information on the determination timing of each action stored in the determination timing column 705 of the action definition table 123.

  Next, the action execution unit 205 determines whether it corresponds to the determination timing acquired in step 2051 (step 2052). Here, when entries corresponding to a plurality of actions are registered in the action definition table 123, the determination in step S2052 is performed on each of the actions. When the execution of the corresponding action is in an invalid state (for example, when the judgment timing column 705 of the entry is set to invalid), the action execution unit 205 executes the step 2051 and the step for this action. The process of 2052 is not performed.

  As a result of the determination in step 2052, when the determination timing does not apply (step 2052: No), the action execution unit 205 advances the process to step 2051.

  On the other hand, when it corresponds to the determination timing (step 2052: Yes), the action execution unit 205 confirms the execution condition of the action definition corresponding to the determination timing (step 2053). Here, the action execution unit 205 acquires the setting contents of the condition column 703 of the action definition table 123.

  Next, the action execution unit 205 determines whether the condition acquired at step 2053 is met (step 2054). Here, in this determination, the action execution unit 205 refers to the contents of the metadata 152, utilization data 151, and catalog data 153 of each data source, and the internal status of the server 110 and the data store apparatus 130 (system Refer to resource usage etc.) and use it. Further, for example, when a condition such as “the latest value of Asset1: Sensor3 is 30 or more” is recorded as the condition of a certain action, the action execution unit 205 corresponds to the acquired data source data itself. Determine by referring to the latest value of the data source. When the acquired condition includes a condition such as “latest target section”, the action execution unit 205 determines the information in the predetermined target section of the utilization level data 151 of the corresponding data source as the condition. Use for

  As a result of the determination in step 2054, when the execution condition is not satisfied (step 2054: No), the action execution unit 205 ends the process without executing the action. On the other hand, if it is determined in step 2054 that the execution condition is satisfied (step 2054: YES), the action execution unit 205 executes the action of the content set in the action content column 704 of the corresponding action definition ( Step 2055). Note that, when executing an action, the action execution unit 205 may execute an API call of an external system or the like.

  As described above, according to the action execution process, when the action condition including the condition of utilization is satisfied, the action corresponding to the condition is executed. Therefore, data sources can be properly managed according to the degree of utilization of the data sources.

  FIG. 14 is a flowchart of catalog management processing according to the first embodiment.

  The catalog management process is a process executed by the catalog management unit 206. The catalog management process may be performed periodically, for example.

  The catalog management unit 206 acquires a catalog definition (step 2061). As a method of acquiring the catalog definition, for example, there is a method of reading from a definition file of a catalog or the like, or a method of acquiring from a user's input through a UI or the like. The information acquired as the catalog definition includes, for example, the contents of “catalog id” included in the catalog data 153 shown in FIG. 6, the data source or file in the “data list”, the contents of “description”, the contents of “creator”, Contents of the "creator role", etc. With regard to the contents of “creator”, the function of the data management program 121 cooperates with a user management function such as a directory service (not shown) to acquire the id of the user who has created the catalog. Also, with regard to the "creator role", information on the role of the user held in the directory service is acquired. For “creation date”, “update date”, and “last access date”, the creation date, update time, and use time of the catalog are acquired, respectively.

  Next, the catalog management unit 206 adds a new catalog to the catalog data 153 or updates an existing catalog based on the information of the catalog definition acquired in step 2061 (step 2062). Note that the catalog management unit 206 sets a default value (for example, an intermediate value 3 in the range of at least 1 to at most 5) when newly adding a catalog to “evaluation” of the catalog data 153.

  Next, the catalog management unit 206 calculates an evaluation correction value (step 2063). Here, the catalog management unit 206. The evaluation correction value is calculated, for example, based on the catalog data 153 of the corresponding catalog. For example, the catalog management unit 206 holds a correspondence table of correction values for “administrator role”, and if the catalog management unit 206 is “data scientist” based on the correspondence table, the correction value is set to +1 or the like.

  Next, the catalog management unit 206 updates the utilization degree held in the metadata 152 of the data source group belonging to the corresponding catalog based on the “evaluation” of the corresponding catalog and the evaluation correction value calculated in step 2063 (step 2064) ). In the present embodiment, the catalog management unit 206 adds the evaluation correction value to the “evaluation” value of the catalog data 153, divides this result by the evaluation default value (for example, 3), and belongs to the corresponding catalog. The product multiplied by the value of “utilization degree” of the data source metadata 152 is updated as a new “utilization degree” in the metadata 152. Thus, the utilization degree of the metadata 152 of the data source can be updated to the utilization degree reflecting the evaluation value of the catalog to which the data source belongs.

  FIG. 15 is a diagram illustrating an example of a data source search screen according to the first embodiment.

  The data source search screen 1501 is a data source search UI for users provided by the data search unit 207. The data source search screen 1501 is displayed by a browser, a client application, a mobile tablet application, or the like. The data search unit 207 operates as a web server or an application server.

  The data source search screen 1501 includes a search key input area 1502, a search button 1503, a detail search option button 1504, a candidate display box 1505, and a close button 1506.

  The search key input area 1502 is an area where keywords for searching for data sources can be input. The search button 1503 is a button for the user to instruct a search, and when the search button 1503 is pressed, the data source is searched based on the keyword input in the search key input area 1502, and the search result ( The candidate data source is displayed in the candidate display box 1505. When the detail search option button 1504 is pressed, an option for selecting a detail condition in the search is displayed.

  The candidate display box 1505 is an area for displaying a search result. In the present embodiment, the candidate display box 1505 displays, for example, a data source id of a candidate data source, information (for example, a tag) related to the data source, the utilization degree, and details. Relevant information can be obtained from the metadata 152 of this data source. In the present embodiment, when displaying a plurality of candidate data sources, for example, the data search unit 207 sorts and displays the data sources in descending order according to the degree of utilization. When the details of the candidate display box 1505 are selected, a screen for acquiring this data source is displayed, which contains more detailed information of the corresponding data source.

  The close button 1506 is a button for closing the data source search screen 1501. When the close button 1506 is pressed, the data search unit 207 closes the data source search screen 1501.

  FIG. 16 is a diagram illustrating an example of a catalog evaluation screen according to the first embodiment.

  The catalog evaluation screen 1601 is a screen displayed by the catalog management unit 206 for requesting the user using the catalog to input an evaluation. The catalog evaluation screen 1601 is displayed by a browser, a client application, an application for mobile tablets, or the like. Similar to the data source search shown in FIG. 15, a catalog search screen (not shown) for searching the keywords in the catalog and displaying information about the catalog is prepared, and the catalog management unit 206 performs the catalog search. If a catalog is selected on the screen, the catalog evaluation screen 1601 may be displayed, or if the used data source belongs to a catalog, the catalog evaluation screen 1601 for the catalog may be displayed. .

  Catalog evaluation screen 1601 includes a catalog information display area 1602, a data content box 1603, an evaluation setting area 1604, and an end button 1605.

  The catalog information display area 1602 displays information of the catalog used by the user. Catalog information is displayed based on the contents of catalog data 153 of this catalog. A data content box 1603 displays a list of data sources belonging to this catalog. The data source list displays, for example, the data source id of each data source, information (for example, a tag) related to the data source, the degree of utilization, and details. Information related to the data source can be obtained from the metadata 152 of this data source.

  The evaluation setting area 1604 is an area for setting the evaluation of this catalog, and for example, five star-shaped buttons selectable by the user are displayed. The user can evaluate the catalog in five levels by changing the number of star buttons to be selected according to the contents of the catalog. The end button 1605 is a button for ending the evaluation of the catalog. When the end button 1605 is pressed, the catalog management unit 206 evaluates the catalog data 153 based on the result of evaluation in the evaluation setting area 1604. Update.

  According to the catalog evaluation screen 1601, the user can easily evaluate the catalog. Also, by evaluating the catalog, it is possible to properly evaluate the utilization of data.

  Next, a computer system according to the second embodiment will be described. In the description of the second embodiment, differences from the computer system according to the first embodiment will be mainly described.

  The computer system 10a according to the second embodiment newly holds relationship data 154 for managing the relationship between data sources in the data storage device 130, and uses this to identify a highly relevant data source, and It is intended to realize data management based on This enables, for example, identification of a plurality of data sources whose data contents match. For this reason, it is possible to selectively accumulate one of the data and move the remaining data to an archive storage etc., thereby reducing the use of unnecessary storage area by duplicate data. it can.

  FIG. 17 is an entire configuration diagram of a computer system according to a second embodiment. The same components as those of the computer system according to the first embodiment are denoted by the same reference numerals.

  In the computer system 10 a according to the second embodiment, the management data 150 of the data storage device 130 further stores relationship data 154.

  FIG. 18 is a diagram showing the contents stored in the related data according to the second embodiment.

  Relational data 154 is generally managed using a graph structure database or the like, and is information of a data format supported by this database, but in the figure, the contents indicated by the information of related data 154 can be easily understood. It is illustrated as a graph structure itself. That is, the content corresponding to the graph structure in the figure is stored in the related data 154.

  The nodes 1801, 1802 and 1803 of the graph respectively hold data source ids. With respect to edges 1811, 1812 and 1813 connecting these nodes, weights 1821, 1822 and 1823 indicating the degree of association between the nodes are associated. The degree of association between nodes indicates that the degree of association between the nodes is high, for example, if it is close to 1.0 in this embodiment. The calculation of the degree of association between nodes will be described later.

  FIG. 19 is a diagram for explaining calculation of the degree of association according to the second embodiment.

  The degree-of-association calculation is executed by the degree-of-use calculation unit 202 as an additional process in step 2024 of the degree-of-use calculation process shown in FIG. FIG. 19 is an example in which the degree of association is calculated for time-series data of the data source 1901 (data source Asset1: Sensor1) and the data source 1903 (data source Asset1: Sensor2). In this example, as time series data, data of a row of time and a value at that time (for example, a measurement value of a sensor) is used.

  The utilization degree calculation unit 202 calculates a standard deviation, a fluctuation rate, and the like as related information in the calculation of the utilization degree, and further calculates the utilization degree and adds it to the utilization degree data 151. In addition to this, in the present embodiment, the utilization calculation unit 202 calculates the degree of association between the two data sources based on the information held in the utilization data 151. Specifically, the utilization degree calculation unit 202 acquires a standard deviation, a fluctuation rate, and the like from the utilization degree data 151 as an “index” for comparison, and calculates the degree of association using Expression (5).

  Degree of association = weight of each index × consistency of each index / total number of indices to be compared (5)

  Here, the “weight of each index” of the equation (5) may be set in advance based on the importance of the index, or may be defined by the user. The method of calculating the degree of association is not limited to this.

  The indices 1902 and 1904 for the data source 1901 and the data source 1903 shown in FIG. 19 have a standard deviation of 31, a variation rate of 20.7, and the respective indices match.

  At this time, when the weight of each index is 1, the degree of association calculated by equation (5) is 1.0. The degree of utilization can also be used as an index for calculating the degree of association. As other indexes that can be used, there is information in columns 403 to 409 of utilization level data 151 shown in FIG. 4, and if specialized for calculation of the degree of association, the hash value is calculated for the specific timing and the target section. It can be used.

  For example, for a combination of two or more data sources having a high degree of association, the action content of the action definition table 123 leaves an object of one data source in the data store 140, and an object of another data source is external By setting to move to the archive or backup storage 160, efficient data management such as data volume reduction can be realized when this action is executed.

  Next, a computer system 10b according to a third embodiment will be described. In the description of the third embodiment, differences from the computer system 10 according to the first embodiment will be mainly described.

  The computer system 10b according to the third embodiment is an embodiment in which the method of calculating the degree of utilization can be changed for each user and the group to which the user belongs. Thus, when performing search of data sources and data management, it is possible to use the degree of utilization of each user or group, and it is possible to search data sources according to the user or group.

  FIG. 20 is an entire configuration diagram of a computer system according to a third embodiment. The same components as those of the computer system according to the first embodiment are denoted by the same reference numerals.

  In the computer system 10b according to the third embodiment, the memory 120 of the server 110 further stores a user management table 124. The memory 120 is an example of a calculation method storage unit. In addition, in the computer system 10b, utilization degree data 151 for each data source is stored for each group. Also, the computer system 10 b stores metadata 152 a (see FIG. 22) instead of the metadata 152.

  FIG. 21 is a configuration diagram of a user management table according to the third embodiment.

  The user management table 124 is a table for managing user information, and stores entries corresponding to each user. The entry of the user management table 124 includes a user id column 2101, a Name column 2102, a roll column 2103, a group id column 2111, a utilization calculation method column 2112, and a description column 2113.

  The user id column 2101 stores a user id for identifying the user corresponding to the entry. The Name column 2102 stores the name and the like of the user corresponding to the entry. The role column 2103 stores the user role (role) corresponding to the entry. The contents of the roll column 2103 can be used as a description for adding to the “creator roll” of the catalog data 153 shown in FIG. 6 of the first embodiment.

  The group id column 2111 stores the id (group id) of the group to which the user corresponding to the entry belongs. The group id is, for example, a combination of a numeric identifier and a description of the group. For example, when the numeric identifier is "001" and the group description is "data science team A", the group id is "001: Data Science Team A". In addition, as long as the group can be identified, the group id may be only information of either a numerical identifier or a description. The utilization degree calculation method column 2112 stores information (calculation method information) of the utilization degree calculation method in the group to which the user corresponding to the entry belongs, for example, a calculation formula of the utilization degree. The description column 2113 stores a description of a method of calculating the degree of utilization of the group to which the user corresponding to the entry belongs.

  Note that all the columns of the user management table 124 are not necessarily essential. For example, the Name column 2102, the roll column 2103, and the description column 2113 are illustrated as the explanation of the present embodiment for the purpose of making the understanding easy. It is not always necessary.

  The user management table 124 may be created and changed, for example, in accordance with an operation for importing from a definition file or the like, an input operation from a user, or the like in a user-oriented UI provided by the data management program 121. The information set in the user id column 2101, the Name column 2102, the roll column 2103, and the group id column 2111 may be acquired from an external directory service or the like.

  The utilization degree calculation process by the utilization degree calculation unit 202 according to the present embodiment is different from the utilization degree calculation process shown in FIG. 10 by the utilization degree calculation unit 202 according to the first embodiment as follows. The utilization calculation unit 202 according to the third embodiment performs calculation of the utilization based on the calculation method of the utilization described in the utilization calculation method column 2112 of the user management table 124. The utilization calculation unit 202 uses the utilization calculation method in part or all of the utilization calculation method of the utilization calculation method column 2112 described in the user management table 124 in step 2024 of the utilization calculation process shown in FIG. Calculate The utilization degrees of all the calculation methods calculated are stored in utilization degree data 151 corresponding to the group id of the group corresponding to the entry.

  In addition, in step 2025 of the utilization degree calculation process shown in FIG. 10, the utilization degree calculation unit 202 sets the utilization degree for each group (for example, the average utilization degree in the group) together with the group id to the metadata 152a of the data source. Add / update. As to whether or not the utilization degree is calculated by only a part of the calculation method, for example, data indicating the necessity of calculation of the utilization degree is added to the user management table 124 or the like, and determination is made based on the data. Alternatively, the specification of the id of the data source for which only a part of the calculation method is to be performed may be received in advance, and the determination may be made based on the data source id of the target.

  In the utilization degree calculation method column 2112 of the user management table 124, when related information such as “variation rate”, “acquisition frequency”, and “standard deviation” is described in the utilization degree calculation method, for example, The utilization calculation unit 202 calculates each piece of related information to be used for the utilization calculation method before executing the utilization calculation method in step 2024 of the utilization calculation process shown in FIG.

  FIG. 22 is a configuration diagram of metadata according to the third embodiment.

  The metadata 152a differs from the metadata 152 according to the first embodiment in that one or more pairs of a group id and a utilization degree of the group id are included as the utilization degree.

  In the metadata 152a shown in FIG. 22, the utilization in the group id "001" is "75", the utilization in the group id "002" is "99", and the utilization in the group id "041" is "12" .9 "is described.

  In the third embodiment, the entry of the action definition table 123 is managed in association with the group id of the group executing the entry, and the action execution unit 205 executes step 2054 of the action execution process shown in FIG. In the above, the utilization degree corresponding to the corresponding group id may be acquired from the metadata 152 and used for condition determination.

  Further, the data search unit 207 may change the utilization degree of the data source displayed in the candidate box 1505 of the data source search screen 1501 according to the user who uses the data management program 121. Specifically, the data search unit 207 acquires, from the user management table 124, the group id to which the user using the data management program 121 belongs, and acquires the utilization degree of the acquired group id from the metadata 152a. , And may be displayed corresponding to the data source of the candidate box 1505. Thereby, the utilization degree corresponding to the group to which the user who is using can be displayed appropriately.

  The present invention is not limited to the above-described embodiment, and can be appropriately modified and implemented without departing from the spirit of the present invention.

  For example, although the server 110 and the data store apparatus 130 are separate computers in the above embodiment, the present invention is not limited to this. For example, the server 110 and the data store apparatus 130 are configured by one computer. You may do so.

  In the above embodiment, part or all of the processing performed by the CPU may be performed by a dedicated hardware circuit. For example, at least one of the functional units (201 to 201, etc.) configured by the CPU executing a program may be configured by a dedicated hardware circuit. Also, the program in the above embodiment may be installed from a program source. The program source may be a program distribution server or storage medium (eg, non-volatile portable storage medium).

  10: computer system, 101: device, 102: data source, 110: server, 130: data storage device, 202: utilization degree calculation unit, 205: action execution unit, 207: data search unit

Claims (12)

A data management system for managing data obtainable from a predetermined data source, comprising:
A storage unit for storing data from the data source;
A utilization calculation unit that calculates utilization based on statistical information about the content of the data, indicating a degree of effectiveness regarding utilization of data analysis of the data of the data source;
A data management system, comprising: an action execution unit which executes a predetermined processing operation corresponding to the action condition on the data when an action condition including the calculated condition regarding the utilization degree is satisfied. The data management system according to claim 1, wherein the utilization degree calculation unit calculates the utilization degree for data in a predetermined section of the data source. The action condition includes a condition that the utilization degree is equal to or less than a predetermined value,
The data management system according to claim 1 or 2, wherein the predetermined processing operation corresponding to the condition is an operation of storing the data in an archive. The statistical information is included in the data, such as a defect rate that is a rate at which incorrect values are included in values of multiple time points included in the data, a variation rate related to fluctuations in values of multiple time points included in the data, or the data The data management system according to any one of claims 1 to 3, which is at least one of standard deviations of values at a plurality of time points. The data management system according to claim 4, wherein the utilization degree calculation unit calculates the utilization degree based on an acquisition frequency of values at a plurality of time points included in the data, the loss rate, and the standard deviation. The data management system according to any one of claims 1 to 5, wherein the action condition includes a condition regarding the statistical information. A catalog management unit is further provided which manages a plurality of related data sources as a catalog, and manages evaluation values related to the catalog,
The data management according to any one of claims 1 to 6, wherein the catalog management unit corrects the degree of utilization of data of the data sources belonging to the catalog based on the evaluation value regarding the catalog. system. The data management system according to claim 7, further comprising an evaluation value receiving unit that receives specification of an evaluation value regarding the catalog. The computer further includes a calculation method storage unit storing calculation method information including a calculation method for calculating the utilization for at least one of a user and a plurality of users.
The data management system according to any one of claims 1 to 8, wherein the utilization degree calculation unit calculates the utilization degree by the calculation method corresponding to a user or a group who has requested the calculation of the utilization degree. An input receiving unit that receives an input of a search condition related to a data source for which the utilization degree is to be displayed;
A data search unit for searching a data source corresponding to the search condition;
10. The display control unit according to claim 1, further comprising: a display control unit configured to display the degree of utilization calculated for the data source with respect to the data source detected by the data search unit. Data management system. A data management method by a data management system for managing data obtainable from a predetermined data source, comprising:
Store data from the data source,
Based on the statistical information on the content of the data, a utilization degree indicating the degree of effectiveness regarding utilization of data analysis of the data of the data source is calculated;
The data management method which performs predetermined processing operation corresponding to the said action condition with respect to the said data, when action conditions containing the conditions regarding the calculated said utilization degree are satisfy | filled. A data management program for causing a computer constituting a data management system that manages data obtainable from a predetermined data source to be executed, the data management program comprising:
The computer,
A utilization calculation unit that calculates utilization based on statistical information about the content of the data, indicating a degree of effectiveness regarding utilization of data analysis of the data of the data source;
A data management program that functions as an action execution unit that executes a predetermined processing operation corresponding to the action condition on the data when an action condition including the calculated condition regarding the utilization degree is satisfied.