Data evaluation system, data evaluation method, and program

Abstract

To provide a data evaluation system, data evaluation method, and program that can evaluate data that does not have an explicit relationship with other data. [Solution] The data evaluation system according to this disclosure comprises a management unit that manages the history information of one or more data to be evaluated; an extraction unit that extracts related information of the history data, including at least one of the source data or derived data of the data to be evaluated, based on the history information; and an evaluation unit that evaluates the data to be evaluated based on the extracted related information.

Description

Technical Field

[0001] The present disclosure relates to a data evaluation system, a data evaluation method, and a program.

Background Art

[0002] In recent years, technological development for evaluating the value of data has been underway. For example, Patent Document 1 discloses a technique for evaluating the value of data based on relationships set between data. The information processing system according to Patent Document 1 determines whether a duplication relationship, a co-occurrence relationship, a similarity relationship, a reference relationship, a diversion relationship, or a revision relationship is recognized between document data based on the content, history information, and file path of the target document data. The information processing system evaluates the document data to be evaluated based on the number of other document data having the above relationships with the document data to be evaluated.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order for the information processing system according to Patent Document 1 to evaluate document data, it is necessary that there is some explicit relationship with other document data. However, depending on the data to be evaluated, there may be no explicit relationship recognized between the target data. With the technique according to Patent Document 1, there is a risk that such data cannot be correctly evaluated.

[0005] The present disclosure has been made to solve such problems, and an object thereof is to provide a data evaluation system, a data evaluation method, and a program capable of evaluating data having no explicit relationship with other data.

Means for Solving the Problems

[0006] The data evaluation system relating to this disclosure comprises a management unit that manages the history information of one or more data to be evaluated; an extraction unit that extracts related information of the history data, including at least one of the source data or derived data of the data to be evaluated, based on the history information; and an evaluation unit that evaluates the data to be evaluated based on the extracted related information.

[0007] The data evaluation method relating to this disclosure involves a computer extracting relevant information about the origin data, including at least one of the source data or derived data of the data to be evaluated, based on the origin information of one or more data to be evaluated that it manages, and then evaluating the data to be evaluated based on the extracted relevant information.

[0008] The program relating to this disclosure causes a computer to perform the following steps: extract relevant information about the origin data, including at least one of the source data or derived data of the data to be evaluated, based on the origin information of one or more data to be evaluated that are under management; and evaluate the data to be evaluated based on the extracted relevant information. [Effects of the Invention]

[0009] This disclosure provides a data evaluation system, a data evaluation method, and a program that can evaluate data that does not have an explicit relationship with other data. [Brief explanation of the drawing]

[0010] [Figure 1] Figure 1 is a block diagram showing the configuration of the data evaluation system 1 related to this disclosure. [Figure 2] Figure 2 is a flowchart showing an example of the processing operation of the data evaluation system 1. [Figure 3] Figure 3 is a block diagram showing the configuration of the data evaluation system 2 related to this disclosure. [Figure 4]Figure 4 is a schematic diagram illustrating an example of evaluating two sets of data. [Figure 5] Figure 5 is a schematic diagram illustrating an example of evaluating two sets of data. [Figure 6] Figure 6 is a schematic diagram illustrating an example of evaluating two sets of data. [Figure 7] Figure 7 is a schematic diagram illustrating an example of evaluating two sets of data. [Figure 8] Figure 8 is a flowchart showing an example of the processing operation of the data evaluation system 2. [Figure 9] Figure 9 is a flowchart showing a typical example of the processing operation of the data evaluation system 2. [Figure 10] Figure 10 is a block diagram showing the configuration of the data evaluation system 3 related to this disclosure. [Figure 11] Figure 11 is a table showing an example of how the evaluation unit 13 weights the relevant information. [Figure 12] Figure 12 is a flowchart showing an example of the processing operation of the data evaluation system 3. [Figure 13] Figure 13 shows an example of the hardware configuration of the data evaluation system 20 related to this disclosure. [Modes for carrying out the invention]

[0011] (Embodiment 1) Embodiment 1 of the present disclosure will be described below with reference to the drawings. Figure 1 is a block diagram showing the configuration of the data evaluation system 1 according to the present disclosure. The data evaluation system 1 comprises a management unit 11, an extraction unit 12, and an evaluation unit 13. The data handled by the data evaluation system 1 is typically data that does not have an explicit relationship with other data, but may also include data that has an explicit relationship. An explicit relationship refers to, but is not limited to, a relationship in which a citation relationship, a copy relationship, a similarity relationship, or a parent-child relationship is recognized between two data.

[0012] The management department 11 manages the history information of one or more pieces of evaluation target data. The evaluation target data is one or more pieces of data selected based on a user or the functions of the data evaluation system 1. The history information of the data is information about the processing history and usage history of the data from the time it was generated until the present. That is, the history information of the data is the history information from the generation of the original data until the data is exchanged, processed, and used. The history information of the data includes not only information about the source data used to generate the data, but also information about the derived data generated by using the data.

[0013] Here, the source data includes not only the direct source data directly used to generate the data, but also the source data used to generate the direct source data. Also, the derived data includes not only the direct derived data directly used to generate the data, but also the derived data generated by using the direct derived data.

[0014] The extraction unit 12 extracts the related information of the history data including at least one of the source data or the derived data of the evaluation target data based on the history information of the evaluation target data. The history data means the data itself including at least one of the source data or the derived data. The related information of the history data is also referred to as information about the history data. The extraction unit 12 extracts the related information of the history data by appropriately referring to the history information managed by the management unit 11. When the related information of the history data is included in the history information managed by the management unit 11, the extraction unit 12 directly extracts the related information of the history data from the history information. On the other hand, when the related information of the history data is not included in the history information, the extraction unit 12 extracts the related information of the history data from the metadata associated with the history data. The extraction unit 12 transmits the extracted related information to the evaluation unit 13.

[0015] The evaluation unit 13 receives the related information of the extracted history data from the extraction unit 12, and evaluates the data to be evaluated based on the related information. The evaluation unit 13 evaluates the data to be evaluated by using the related information of the extracted history data as an evaluation parameter when evaluating the data to be evaluated.

[0016] Next, the processing operation flow of the data evaluation system 1 will be described. FIG. 2 is a flowchart showing an example of the processing operation of the data evaluation system 1. First, the extraction unit 12 extracts the related information of the history data of the data to be evaluated based on the history information of one or more pieces of data to be evaluated managed by the management unit 11 (S101). Next, the evaluation unit 13 evaluates the data to be evaluated based on the extracted related information (S102). Thereby, the data evaluation system 1 can evaluate the data to be evaluated.

[0017] As described above, according to the data evaluation system 1 according to the first embodiment, it is possible to evaluate data that does not have an explicit relationship with other data. That is, the data evaluation system 1 does not evaluate the data based on the data to be evaluated itself, but can evaluate the data to be evaluated based on the related information of the history data of the data to be evaluated. Thereby, the data evaluation system 1 can evaluate the data even when it cannot evaluate the data based on the data to be evaluated itself.

[0018] (Second Embodiment) Next, the second embodiment according to the present disclosure will be described. FIG. 3 is a block diagram showing the configuration of the data evaluation system 2 according to the present disclosure. The data evaluation system 2 includes a management unit 11, an extraction unit 12, an evaluation unit 13, a display unit 14, and an input unit 15. That is, the data evaluation system 2 is obtained by adding a display unit 14 and an input unit 15 to the configuration of the data evaluation system 1. The same components as those in the data evaluation system 1 in the data evaluation system 2 are denoted by the same reference numerals. Also, the description of the same functions will be omitted as appropriate.

[0019] The management unit 11 manages the history information of one or more data to be evaluated, similar to the data evaluation system 1 in Embodiment 1. Here, the data to be evaluated in the data evaluation system 2 is one or more arbitrary data that is available on the data linkage platform. That is, the data to be evaluated is one or more arbitrary data that can be referenced by a user through a data catalog for a predetermined data linkage platform. Furthermore, the data to be evaluated in the data evaluation system 2 is assumed to be data that does not have an explicit relationship with other data. Here, the data linkage platform refers to a mechanism or infrastructure that allows different organizations or countries to share and exchange data using a common platform. The data linkage platform may also be called a data space.

[0020] The data to be evaluated can be determined by the user from any data set. The user may define the data to be evaluated by defining the range of data, or they may define the data to be evaluated specifically. Alternatively, the data to be evaluated may be automatically determined from the user's attribute information. User attribute information refers to personal information such as the industry of the company the user belongs to, the user's occupation, the user's gender, and their place of residence. The control unit (not shown) can extract the data to be evaluated from any data set.

[0021] The extraction unit 12 extracts relevant information of the provenance data based on the provenance information managed by the management unit 11, similar to the data evaluation system 1 in Embodiment 1. Here, the provenance data targeted by the extraction unit 12 includes at least one of the direct source data directly used to generate the data to be evaluated, or the direct derived data generated by directly using the data to be evaluated. In this embodiment, direct source data is simply referred to as source data, and direct derived data is simply referred to as derived data.

[0022] Here, we will explain the related information of the provenance data extracted by the extraction unit 12 of the data evaluation system 2. Related information of provenance data is information that can represent the characteristics of provenance data without analyzing the content of the provenance data itself.

[0023] Related information to provenance data includes, for example, at least one of the processing history information or usage information of the provenance data. Processing history information is information about the process by which certain data is processed into provenance data. In other words, processing history information is information about the process of creating provenance data. Processing history information includes, for example, owner information about the owner of the provenance data that processed it into provenance data. Owner information is information about the owner's attributes and the number of owners. Owner attributes include, but are not limited to, the owner's reliability, the owner's industry, and, if the owner is a company, the size of the company. The number of owners can also be expressed as the number of stakeholders involved in the provenance data.

[0024] Furthermore, processing history information includes the purpose for which the owner created the provenance data. For example, in the case of derived data, the purpose of creation refers to the purpose for which the owner created the derived data, and how they intended to use the data being evaluated. In the case of source data, the purpose of creation refers to the purpose for which the owner created the source data, and how they intended to use the original data used when creating the source data.

[0025] Furthermore, processing history information includes information on the update status of the provenance data. Update status information includes, for example, information on the update history and update date and time. In other words, processing history information includes information on when and what kind of data was updated. If the provenance data is generated from information acquired by some sensor, the update status information of the provenance data also includes the sensor synchronization frequency and synchronization date and time.

[0026] Furthermore, processing history information includes information about the standards under which the provenance data was created. In other words, processing history information includes information about the standard compliance of the provenance data. Specifically, processing history information includes information about the legal compliance of the provenance data and compliance with personal information protection policies and privacy policies. If the provenance data was created in accordance with a certain standard, processing history information includes information about that standard.

[0027] Usage information refers to information about how the provenance data is used. This includes, for example, how third parties use and utilize the provenance data. Usage information also includes information about access to the provenance data. Access information includes, for example, the number of accesses to the provenance data, the attributes of the data users who accessed the data, and statistical information about the data users.

[0028] Furthermore, access information includes the ease of access to the provenance data. In other words, access information includes information on whether or not access to the provenance data is easy when judged according to certain criteria. The ease of access to provenance data can be determined, for example, by whether or not the provenance data provides an API (Application Program Interface).

[0029] Furthermore, access information includes cost information for provenance data. Source data cost information is, for example, the acquisition cost required when the owner of the data being evaluated uses the source data to create the data being evaluated. Derived data cost information is, for example, the processing cost required when processing the data being evaluated to create derived data.

[0030] Related information to provenance data may include quality assessment information for provenance data. Quality assessment information for provenance data is information used when evaluating the quality of provenance data, not the data being evaluated. For example, a user of provenance data can evaluate the quality of that provenance data based on the provenance data owner information. In other words, quality assessment information includes provenance data owner information. Similarly, quality control information includes information about access to provenance data, information about the update status of provenance data, and information about the provenance data's compliance with standards.

[0031] Furthermore, related information on the provenance data may include information on its interrelationship with the data being evaluated. Interrelationship information with the data being evaluated refers to information about the interrelationship between the provenance data and the data being evaluated. In other words, interrelationship information indicates the degree of connection between the provenance data and the data being evaluated. Interrelationship information includes the number of provenance data. Specifically, interrelationship information includes the number of source data used to create the data being evaluated, or the number of derived data created using the data being evaluated. Interrelationship information also includes the number of variations in the ownership of the provenance data.

[0032] The extraction unit 12 extracts any relevant information from the relevant information of the provenance data as described above, taking into account the nature of the data to be evaluated. The relevant information extracted by the extraction unit 12 may be one or more. The extraction unit 12 may directly extract relevant information from the provenance information managed by the management unit 11, extract relevant information from metadata linked to the provenance data, or extract relevant information by other methods. For example, the extraction unit 12 may obtain owner information of the provenance data by linking with an external database and extract relevant information. The external database may be, for example, a database that stores evaluations of the reliability of the owners of the provenance data. The database stores information such as the owner's rating information, the owner's history, past behavioral history, and feedback on this information. The extraction unit 12 may extract this information from the external database.

[0033] The evaluation unit 13 evaluates the data to be evaluated based on the relevant information of the history data extracted by the extraction unit 12, similar to the data evaluation system 1 in Embodiment 1. Typically, the evaluation unit 13 evaluates the data to be evaluated by comparing the relevant information common to each history data related to two or more data to be evaluated, but the evaluation method by the evaluation unit 13 is not limited to this. That is, the evaluation unit 13 may evaluate the data to be evaluated based on the relevant information of the history data related to one data to be evaluated, in accordance with a certain criterion.

[0034] The evaluation unit 13 evaluates the data to be evaluated based on the numerical values ​​in the related information of the history data relating to one or more data to be evaluated. That is, the related information of the history data is numerical information expressed in numerical terms. For two or more data to be evaluated, the evaluation unit 13 evaluates the data to be evaluated by comparing the numerical values ​​in the related information common to each data to be evaluated. If there is only one data to be evaluated, the evaluation unit 13 evaluates the data to be evaluated based on whether the numerical value in the related information of the history data relating to the data to be evaluated meets a predetermined standard value expressed in numerical terms. On the other hand, the related information of the history data does not have to be numerical information. For example, the related information of the history data may be expressed as "possible / not possible," "present / absent," or "large / medium / small."

[0035] Here, using diagrams, we will explain several examples of when the evaluation unit 13 evaluates two data to be evaluated. In these examples, the two data to be evaluated are referred to as Data A and Data B. Figure 4 is a schematic diagram showing an example of when two data to be evaluated are evaluated. In Figure 4, the evaluation unit 13 evaluates the data to be evaluated using the number of derived data as related information. In Figure 4, Data A has three derived data. On the other hand, Data B has no derived data. The existence of derived data means that the data to be evaluated is used in that derived data. Therefore, it can be determined that Data A has higher reliability and usefulness as data than Data B. Thus, the evaluation unit 13 evaluates Data A more highly than Data B.

[0036] Let's consider the following example. Figure 5 is a schematic diagram showing an example of evaluating two sets of data to be evaluated. In Figure 5, the evaluation unit 13 evaluates the data to be evaluated using the number of variations in the owners of the derived data as related information. In Figure 5, Data A has 3 derived data sets. Data B has 2 derived data sets. All of the derived data sets of Data A are owned by the same company a. One of the derived data sets of Data B is owned by company m, and the other by company n. Although Data A has more derived data sets than Data B, when comparing by the number of variations in the owners of the derived data, Data A has 1 owner, while Data B has 2 owners. In other words, Data A is used by only one company, while Data B is used by two companies. Therefore, it can be determined that Data B is more reliable and useful as data than Data A. Thus, the evaluation unit 13 rates Data B higher than Data A.

[0037] Let's consider the following example. Figure 6 is a schematic diagram showing an example of evaluating two data sets. In Figure 6, the evaluation unit 13 evaluates the data sets using the number of accesses to derived data as related information. In Figure 6, data A and data B each have one derived data set. The number of accesses to derived data A1 of data A is large, but the number of accesses to derived data B1 of data B is small. In other words, the number of accesses to derived data of data A is greater than the number of accesses to derived data of data B. Therefore, it can be determined that data A is more reliable and useful as data than data B. Thus, the evaluation unit 13 rates data A higher than data B.

[0038] Let's consider the following example. Figure 7 is a schematic diagram showing an example of evaluating two data sets. In Figure 7, the evaluation unit 13 evaluates the data sets using the organizational reliability of the source data owner as relevant information. In Figure 7, data A and data B each have one source data. The organizational reliability of the owner of source data A0 for data A is high, but the organizational reliability of the owner of source data B0 for data B is low. In other words, the organizational reliability of the owner of the source data for data A is higher than that of the owner of the source data for data B. Therefore, it can be determined that data A is more reliable and useful as data than data B. Thus, the evaluation unit 13 rates data A higher than data B. In this way, the evaluation unit 13 can evaluate the data sets by using various relevant information about the provenance data as evaluation criteria.

[0039] Returning to Figure 3, let's continue the explanation of the evaluation unit 13. If the related information of the history data for the data to be evaluated is numerical information with two or more corresponding values, the evaluation unit 13 can normalize or standardize the related information. This allows the evaluation unit 13 to add the related information based on different scales and calculate one evaluation value for one data to be evaluated. If there are two or more data to be evaluated, the evaluation unit 13 can determine the superiority or inferiority of the data to be evaluated by comparing the data based on the evaluation value.

[0040] The display unit 14 is a display device including a liquid crystal panel and organic electroluminescence. The display unit 14 highlights and displays to the user the evaluation target data that has received a high evaluation from the evaluation unit 13 in a predetermined manner. When the evaluation unit 13 evaluates two or more evaluation target data, the display unit 14 typically displays the evaluation target data in descending order of evaluation. Alternatively, the display unit 14 may display the evaluation target data with a high evaluation larger than the evaluation target data with a low evaluation, or it may display the evaluation target data with a high evaluation in a color that is easily visible.

[0041] The input unit 15 is, for example, an input device such as a keyboard, mouse, or touch panel. If the input unit 15 is a touch panel, the display unit 14 and the input unit 15 may be physically integrated. The input unit 15 accepts input from the user of conditions for determining the range of data to be evaluated. In other words, the input unit 15 is an input device that functions when the user defines the range of data to be evaluated. The input unit 15 may accept input of search conditions, or it may accept input of data that the user specifically wants to evaluate. Typically, the input unit 15 can accept input of keywords that the user wants to search for. The input unit 15 may also accept input of user attribute information. In other words, when the range of data to be evaluated is determined based on the user's attribute information, the input unit 15 can accept input of the user's attribute information.

[0042] When accepting user attribute information, the input unit 15 may be a device having an IC (Integrated Circuit) information reading function. Specifically, the input unit 15 may be an IC card reader. That is, the user may input their attribute information by reading an IC that has the user's attribute information recorded on it.

[0043] Next, the processing flow of the data evaluation system 2 will be explained. Figure 8 is a flowchart showing an example of the processing operation of the data evaluation system 2. First, the input unit 15 receives input from the user for conditions to determine the range of the data to be evaluated. Based on this, the control unit (not shown) of the data evaluation system 2 determines the range of the data to be evaluated (S201). Next, the extraction unit 12 extracts relevant information about the history data of the data to be evaluated based on the history information of the data to be evaluated managed by the management unit 11 (S202). After that, the evaluation unit 13 evaluates the data to be evaluated based on the extracted relevant information (S203). After that, the display unit 14 displays the display results to the user by highlighting the data to be evaluated with a high evaluation in a predetermined way (S204).

[0044] Next, another processing operation by the data evaluation system 2 will be described. Figure 9 is a flowchart showing a typical example of the processing operation of the data evaluation system 2. First, the input unit 15 accepts the input of keywords that the user wants to search for in the data catalog (S201a-1). The input unit 15 obtains the search keywords entered by the user (S201a-2). Based on this, the control unit extracts the data to be evaluated that is related to the search keywords (S201a-3). Next, the extraction unit 12 extracts related information of the history data for the data to be evaluated (S202a). After that, the evaluation unit 13 evaluates the data to be evaluated (S203a). After that, the display unit 14 displays the data to be evaluated in descending order of evaluation (S204a). As a result, the user can recognize the data displayed at the top as the data with the highest evaluation.

[0045] As described above, the data evaluation system 2 according to this second embodiment can appropriately evaluate one or more arbitrary data available in the data linkage platform. In the data evaluation system 1 according to the first embodiment, the provenance data includes source data and derived data that are not direct source data or direct derived data. In the data evaluation system 2, the provenance data is data that includes at least one of direct source data or direct derived data. The correlation between the evaluation of related information of direct source data or direct derived data and the evaluation of the data to be evaluated is higher than in the case of related information of other source data or derived data. Therefore, by using the data evaluation system 2, it is possible to perform a highly accurate evaluation of the data to be evaluated.

[0046] One method for evaluating data to be assessed is to use the number of backlinks (citations) to the data in question. This method evaluates data with a large number of backlinks as high-value data. However, this method may not be able to properly evaluate data where there are no linking relationships between the data. In other words, since much of the data in a data linkage platform does not have explicit linking relationships between the data, there is a risk that the data in the data linkage platform cannot be properly evaluated by related technologies.

[0047] Data Evaluation System 2 evaluates data that does not have explicit relationships, such as links, with other data. Furthermore, Data Evaluation System 2 can evaluate data without using the data itself, by using related information from the data's history. Therefore, Data Evaluation System 2 can appropriately evaluate data that does not have explicit relationships.

[0048] Furthermore, while related technologies allow for the evaluation of data based solely on the number of accesses, this approach is insufficient if the data has only recently been published and access is low. Additionally, if multiple data points have similar access counts, access counts alone cannot adequately compare them. Moreover, another related technology allows for the evaluation of data similar to user searches, but this approach is insufficient if multiple data points have similar levels of similarity. Finally, yet another related technology allows for the evaluation of data based on the update date, but newer data does not necessarily mean higher data value. Thus, these related technologies have biased data evaluation methods, potentially preventing the proper evaluation of the target data.

[0049] Data Evaluation System 2 uses various relevant information about the provenance data as evaluation parameters. Specifically, Data Evaluation System 2 evaluates the data to be evaluated by using at least one of the following: processing history information, usage information, quality evaluation information, and interrelationship information of the provenance data. Furthermore, Data Evaluation System 2 evaluates the data by extracting arbitrary relevant information considering the nature of the data to be evaluated. Therefore, Data Evaluation System 2 can suppress bias in the data evaluation method and thus appropriately evaluate the data to be evaluated.

[0050] Furthermore, the data evaluation system 2 can highlight and display highly-rated data to the user in a predetermined manner. This allows the data evaluation system 2 to intuitively show the user the evaluation results of the data to be evaluated. In addition, the data evaluation system 2 can request the user to input conditions for determining the range of data to be evaluated. This allows the user to determine any range as the data to be evaluated, thereby improving user convenience.

[0051] Typically, the data evaluation system 2 prompts the user to input keywords they wish to search for, and extracts data related to the search keywords entered by the user as data to be evaluated. After evaluating the data, the data evaluation system 2 displays the evaluated data in descending order of evaluation. This allows the user to recognize the data displayed at the top as data related to the search keywords and with a high evaluation. By providing input / display formats similar to those used in related technologies, the data evaluation system 2 can ensure user convenience and visibility of display results even in data search scenarios within a data catalog.

[0052] (Embodiment 3) Next, Embodiment 3 of the present disclosure will be described. Figure 10 is a block diagram showing the configuration of the data evaluation system 3 according to the present disclosure. The data evaluation system 3 comprises a management unit 11, an extraction unit 12, an evaluation unit 13, a display unit 14, an input unit 15, and an acquisition unit 16. In other words, the data evaluation system 3 is the data evaluation system 2 with the acquisition unit 16 added to its configuration. In the data evaluation system 3, the same reference numerals are used for the same components as in the data evaluation system 2. Also, descriptions of the same functions will be omitted as appropriate.

[0053] The acquisition unit 16 acquires the purpose of use of the data to be evaluated by the user. In other words, the acquisition unit 16 acquires information about how the user intends to use the data to be evaluated. The purpose of use may include, but is not limited to, "research," "marketing analysis," "financial market trading," "budget management," "medical diagnosis," "user interface design," and "compliance with data protection regulations."

[0054] The acquisition unit 16 acquires the purpose of use based on the user's attribute information. That is, the acquisition unit 16 acquires the user's purpose of use based on the user's attribute information without the user directly inputting the purpose of use. The acquisition unit 16 may acquire the user's attribute information by requesting the user to input attribute information, or it may acquire the user's purpose of use based on the user's attribute information that has been stored in advance. Alternatively, the acquisition unit 16 may acquire the user's purpose of use by having the user directly input the purpose of use. The acquisition unit 16 transmits the acquired user's purpose of use to the evaluation unit 13.

[0055] The user's purpose of use is predetermined. That is, the acquisition unit 16 acquires the most suitable purpose of use from the predetermined purposes of use based on the user's attribute information. Here, the correspondence between the user's attribute information and the purpose of use is predetermined, for example, by a correspondence table. In this case, the acquisition unit 16 acquires the user's purpose of use from the user's attribute information by referring to the correspondence table.

[0056] The acquisition unit 16 may acquire the purpose of use from the user's attribute information by using a pre-trained model. In this case, the pre-trained model is generated by training the user's attribute information and purpose of use as training data. During the operation phase, the acquisition unit 16 receives the user's attribute information as input. From the input attribute information, the pre-trained model outputs the purpose of use. This allows the acquisition unit 16 to acquire the purpose of use.

[0057] When a user enters the purpose of use, they may enter the purpose of use by selecting the most suitable purpose from a predetermined list of purposes of use, or they may directly enter a new purpose of use. The acquisition unit 16 acquires the purpose of use when the user enters the purpose of use.

[0058] The evaluation unit 13 receives the user's purpose of use from the acquisition unit 16. The evaluation unit 13 evaluates the data to be evaluated according to the user's purpose of use. Specifically, the evaluation unit 13 evaluates the data to be evaluated by weighting the extracted relevant information according to the user's purpose of use. In other words, the evaluation unit 13 identifies the relevant information that the user considers important based on the user's purpose of use, and adjusts the evaluation of the data to be evaluated by weighting the relevant information that the user considers important.

[0059] The evaluation unit 13 first classifies the acquired user's purpose of use into predetermined categories. In other words, the evaluation unit 13 determines what parameters the user prioritizes based on the acquired user's purpose of use. For example, if the user's purpose of use is "research," the evaluation unit 13 determines that the user prioritizes reliability for the data being evaluated. This is because, since the user uses the data being evaluated for research, a certain level of reliability is required for the data being evaluated.

[0060] Furthermore, if the user's purpose of use is "marketing analysis," the evaluation unit 13 determines that the user primarily values ​​the usage history of derived data. This is because, for example, derived data from the data being evaluated may be data optimized for general consumers. Based on the usage history of the derived data, the evaluation unit 13 can evaluate the data being evaluated, which is the source of the derived data.

[0061] Similarly, if the user's purpose of use is "financial market transactions," the evaluation unit 13 determines that the user primarily prioritizes the freshness of derived data. If the user's purpose of use is "budget management," the evaluation unit 13 determines that the user prioritizes the cost-effectiveness of the provenance data. If the user's purpose of use is "medical diagnosis," the evaluation unit 13 determines that the user prioritizes the accuracy of the provenance data. If the user's purpose of use is "user interface design," the evaluation unit 13 determines that the user prioritizes the usability of the provenance data. If the user's purpose of use is "compliance with data protection regulations," the evaluation unit 13 determines that the user prioritizes legal compliance of the provenance data.

[0062] The correspondence between a user's purpose of use and the category of that purpose of use is predetermined, for example, by a correspondence table. In this case, the evaluation unit 13 determines the category of the received user's purpose of use by referring to the correspondence table. The evaluation unit 13 may also determine the category from the user's purpose of use by using a trained model. In this case, the trained model generates the purpose of use and the category as training data. During the operational phase, the evaluation unit 13 inputs the user's purpose of use into the trained model. From the input purpose of use, the trained model outputs a category. From this, the evaluation unit 13 can determine the category. By using the trained model, the evaluation unit 13 can determine the category of a new purpose of use even if the user inputs a new purpose of use.

[0063] The evaluation unit 13 evaluates the data to be evaluated by weighting the relevant information based on the identified category. Here, an example of weighting will be explained using a diagram. Figure 11 is a table showing an example of weighting of relevant information by the evaluation unit 13. Figure 11 shows the weighting for the "reliability-focused" category and the "usage history-focused" category. The relevant information extracted by the extraction unit 12 is "user confidence level," "number of stakeholder variations," and "number of accesses" for the source data. For the derived data, it is "user confidence level," "number of stakeholder variations," "number of accesses," and "update date and time." Figure 11 shows the weights for each piece of relevant information. In Figure 11, the weights are assumed to be on a 5-point scale.

[0064] In the case of "reliability-focused" evaluation, the "user confidence level" in the source data is weighted as 5, the "number of stakeholder variations" as 4, and the "number of accesses" as 2. In the case of derived data, the "user confidence level" is weighted as 5, the "number of stakeholder variations" as 3, the "number of accesses" as 2, and the "update date and time" as 2. In the case of "usage history-focused" evaluation, the "user confidence level" in the source data is weighted as 2, the "number of stakeholder variations" as 2, and the "number of accesses" as 3. In the case of derived data, the "user confidence level" is weighted as 2, the "number of stakeholder variations" as 4, the "number of accesses" as 5, and the "update date and time" as 4. In this way, the evaluation unit 13 weights relevant information according to the category corresponding to the purpose of use.

[0065] When weighting is applied, the relevant information of the history data extracted by the extraction unit 12 is typically numerical information. That is, the evaluation unit 13 can calculate an evaluation value for the data to be evaluated by multiplying each of the numerically represented relevant information by a weight parameter. As shown in the example in Figure 11, if there are two or more relevant information pieces of history data for one data to be evaluated, the evaluation unit 13 multiplies the normalized or standardized relevant information by a weight parameter and adds them together. In this way, the evaluation unit 13 can obtain one evaluation value for one data to be evaluated.

[0066] The evaluation unit 13 may evaluate the data to be evaluated without weighting the relevant information, depending on the user's purpose of use. For example, if the extraction unit 12 extracts two or more pieces of relevant information for one piece of data to be evaluated, the evaluation unit 13 may select one piece of relevant information, depending on the user's purpose of use, and evaluate the data to be evaluated based only on that piece of relevant information. In other words, the evaluation unit 13 may select the relevant information that forms the basis of the evaluation, depending on the user's purpose of use.

[0067] When the acquisition unit 16 acquires the purpose of use based on the user's input, the input unit 15 can accept the user's input of the purpose of use. In this case, the input unit 15 may allow the user to select any purpose of use from a predetermined list of purposes of use, or it may allow the user to input any purpose of use. The other configurations of the data evaluation system 3 will not be explained.

[0068] Next, the processing flow of the data evaluation system 3 will be explained. Figure 12 is a flowchart showing an example of the processing operation of the data evaluation system 3. First, the acquisition unit 16 acquires the user's purpose of use (S301). The input unit 15 receives input from the user for conditions to determine the range of the data to be evaluated. This determines the range of the data to be evaluated (S302). Here, if the acquisition unit 16 acquires the user's purpose of use by receiving attribute information and purpose of use input from the user, steps S301 and S302 may be performed simultaneously. The subsequent steps S303 to S305 are the same as steps S202 to S204 in Figure 8, which is an example of the processing operation of the data evaluation system 2, so the explanation will be omitted. Note that the step in which the acquisition unit 16 acquires the user's purpose of use (S301) may be performed at any timing before the step in which the evaluation unit 13 evaluates the data to be evaluated (S304).

[0069] As described above, the data evaluation system 3 according to this embodiment 3 can evaluate the data to be evaluated according to the user's purpose of use by acquiring the user's purpose of use. It is assumed that there are multiple pieces of related information in the history data of the data to be evaluated. Therefore, by evaluating the data to be evaluated using multiple pieces of related information, the data can be evaluated from multiple perspectives. Here, depending on the user's purpose of use, there may be related information that should be given importance and related information that should not be given importance. In such cases, by differentiating the handling of related information according to the user's purpose of use, the data evaluation system 3 can perform an evaluation that is appropriate to the user's purpose of use. Therefore, the data evaluation system 3 can evaluate the data appropriately.

[0070] Specifically, the data evaluation system 3 can evaluate the data to be evaluated by weighting relevant information according to the user's purpose of use. This allows the data evaluation system 3 to quantify the importance of each piece of relevant information and subdivide its importance, enabling more accurate evaluation. In particular, when the relevant information is numerical, the data to be evaluated can be quantitatively evaluated by multiplying each piece of relevant information by a weight parameter. When there are two or more data to be evaluated, the data evaluation system 3 can quantitatively compare them.

[0071] Furthermore, the data evaluation system 3 can obtain the user's purpose of use from the user's attribute information. Since there may be some correlation between the user's attribute information and the purpose of use, in such cases, the purpose of use can be determined from the user's attribute information. Because the data evaluation system 3 can obtain the user's purpose of use without requiring user input, it can improve user convenience. On the other hand, the data evaluation system 3 may also obtain the purpose of use by having the user input it. This allows the data evaluation system 3 to perform evaluations with high accuracy.

[0072] (Example hardware configuration) Figure 13 shows an example of the hardware configuration of a data evaluation system 20 according to this disclosure. In Figure 13, the data evaluation system 20 has a processor 21 and a memory 22. The processor 21 may be, for example, a microprocessor, an MPU (Micro Processing Unit), or a CPU (Central Processing Unit). The processor 21 may include multiple processors. The memory 22 is composed of a combination of volatile memory and non-volatile memory. The memory 22 may include storage located away from the processor 21. In this case, the processor 21 may access the memory 22 via an I / O (Input / Output) interface, which is not shown.

[0073] In the above example, the program can be stored and provided to the computer using various types of non-transitory computer-readable medium. Non-transitory computer-readable medium includes various types of tangible storage medium. Examples of non-transitory computer-readable medium include magnetic storage media (e.g., magneto-optical disks), CD-ROMs, CD-Rs, CD-R / Ws, and semiconductor memory (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, RAMs). Alternatively, the program may be provided to the computer using various types of transient computer-readable medium. Examples of transient computer-readable medium include electrical signals, optical signals, and electromagnetic waves. Transitory computer-readable medium can supply the program to the computer via wired communication channels such as electric wires and optical fibers, or via wireless communication channels. Computers include various information processing devices such as PCs, servers, CPUs, MPUs, FPGAs (Field Programmable Gate Arrays), and ASICs (Application Specific Integrated Circuits).

[0074] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. Various modifications to the structure and details of the present disclosure can be made as can be understood by those skilled in the art within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0075] Each drawing is merely illustrative to illustrate one or more embodiments. Each drawing may be associated with one or more other embodiments rather than with only one specific embodiment. As those skilled in the art will understand, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings, for example, to create embodiments not explicitly shown or described. Not all features or steps shown in any one drawing to illustrate an exemplary embodiment are necessarily required, and some features or steps may be omitted. The order of steps shown in any of the drawings may be changed as appropriate.

[0076] Some or all of the above embodiments may also be described as follows, but are not limited to the following: (Note 1) A management unit that manages the history information of one or more evaluation target data, An extraction unit extracts related information of the history data, including at least one of the source data or derived data of the data to be evaluated, based on the history information. The system comprises an evaluation unit that evaluates the data to be evaluated based on the extracted related information, Data evaluation system. (Note 2) The aforementioned origin data includes at least one of the direct source data directly used to generate the data to be evaluated, or the direct derived data generated by directly using the data to be evaluated. The data evaluation system described in Appendix 1. (Note 3) The aforementioned related information includes at least one of the processing history information or usage information of the aforementioned provenance data, The data evaluation system described in Appendix 1 or 2. (Note 4) The aforementioned related information includes quality evaluation information of the provenance data, A data evaluation system as described in any one of the items 1 to 3 in the appendix. (Note 5) The aforementioned related information includes interrelationship information of the aforementioned history data, A data evaluation system as described in any one of the items 1 to 4 in the appendix. (Note 6) The system further includes an acquisition unit that acquires the purpose of use of the user who will be using the aforementioned evaluation data, The evaluation unit evaluates the data to be evaluated according to the purpose of use. A data evaluation system as described in any one of the items 1 to 5 in the appendix. (Note 7) The evaluation unit evaluates the data to be evaluated by weighting the relevant information according to the purpose of use. The data evaluation system described in Appendix 6. (Note 8) The acquisition unit acquires the purpose of use based on the user's attribute information. The data evaluation system described in Appendix 6 or 7. (Note 9) The acquisition unit acquires the purpose of use when the user inputs the purpose of use. The data evaluation system described in Appendix 6 or 7. (Note 10) The system further includes a display unit that highlights the highly-rated data in a predetermined manner and displays it to the user. A data evaluation system as described in any one of the items 1 through 9 of the appendix. (Note 11) The system further includes an input unit for the user to input conditions for determining the range of the data to be evaluated. A data evaluation system described in any one of the items 1 through 10 of the appendix. (Note 12) The extraction unit extracts related information of the derived data. A data evaluation system as described in any one of the items 1 through 11 of the appendices. (Note 13) The extraction unit extracts related information of the directly derived data. The data evaluation system described in Appendix 12. (Note 14) The extraction unit extracts relevant information from the source data. The data evaluation system described in Appendix 1 to 11. (Note 15) The extraction unit extracts relevant information from the direct source data. The data evaluation system described in Appendix 14. (Note 16) Computers Based on the history information of one or more evaluation target data being managed, relevant history data information including at least one of the source data or derived data of the evaluation target data is extracted. Based on the extracted related information, the data to be evaluated is evaluated. Data evaluation methods. (Note 17) A step of extracting relevant information about the history data, including at least one of the source data or derived data of the data to be evaluated, based on the history information of one or more data to be evaluated that are being managed, A step of evaluating the data to be evaluated based on the extracted related information, A program that causes a computer to execute something.

[0077] Some or all of the elements (e.g., configuration and function) described in Appendices 2 to 15 that are dependent on Appendice 1 may also be dependent on Appendices 16 and 17 in the same manner as those described in Appendices 2 to 15. Some or all of the elements described in any appendice may be applicable to various hardware, software, recording means, systems, and methods for recording software. [Explanation of symbols]

[0078] 1. Data Evaluation System 2. Data Evaluation System 3. Data Evaluation System 11 Management Department 12 Extraction part 13 Evaluation Department 14 Display section 15 Input section 16 Acquisition Department 20 Data Evaluation Systems 21 processors 22 memory

Claims

1. A management unit that manages the history information of one or more evaluation target data, An extraction unit extracts related information of the history data, including at least one of the source data or derived data of the data to be evaluated, based on the history information. The system includes an evaluation unit that evaluates the data to be evaluated based on the extracted related information, Data evaluation system.

2. The aforementioned origin data includes at least one of the direct source data directly used to generate the evaluation target data, or the direct derived data generated by directly using the evaluation target data. The data evaluation system according to claim 1.

3. The aforementioned related information includes at least one of the processing history information or usage information of the aforementioned provenance data, The data evaluation system according to claim 2.

4. The aforementioned related information includes quality evaluation information of the provenance data, The data evaluation system according to claim 2.

5. The aforementioned related information includes interrelationship information of the aforementioned history data, The data evaluation system according to claim 3 or 4.

6. The system further includes an acquisition unit that acquires the purpose of use of the user who will be using the aforementioned evaluation data, The evaluation unit evaluates the data to be evaluated according to the purpose of use. A data evaluation system according to any one of claims 1 to 4.

7. The evaluation unit evaluates the data to be evaluated by weighting the relevant information according to the purpose of use. The data evaluation system according to claim 6.

8. The acquisition unit acquires the purpose of use based on the user's attribute information. The data evaluation system according to claim 6.

9. Computers Based on the history information of one or more evaluation target data being managed, relevant information of the history data, including at least one of the source data or derived data of the evaluation target data, Based on the extracted related information, the data to be evaluated is evaluated. Data evaluation methods.

10. A step of extracting relevant information about the history data, including at least one of the source data or derived data of the data to be evaluated, based on the history information of one or more data to be evaluated that are being managed, A step of evaluating the data to be evaluated based on the extracted related information, A program that causes a computer to execute something.

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