JP2019219952A - Device, method, and program for processing information - Google Patents

Abstract

To allow for appropriately computing value of data owned by a business operator.SOLUTION: An information processing device 100 comprises an acquisition unit 131 and a computation unit 132. The acquisition unit 131 acquires various types of information from external information processing devices. The computation unit 132 computes a value score representing value of data from feature information of the data acquired by the acquisition unit 131 on the basis of comparison between elements of the data and other elements of other data.SELECTED DRAWING: Figure 3

Description

  The present invention relates to an information processing device, an information processing method, and an information processing program.

  2. Description of the Related Art Conventionally, techniques related to value evaluation of information (data) have been provided. For example, a technique has been proposed in which the degree of contribution to advertising effectiveness of information acquired from outside while maintaining the operation of targeted distribution is proposed.

JP 2013-242657 A JP 2014-81961 A JP 2014-81962 A

  However, with the above-described conventional technology, it may be difficult to calculate the value of data owned by a business operator. For example, for data used in an advertisement or the like, the contribution (value) of the data can be calculated based on the reaction of the user to the advertisement or the like. May not be able to do it properly. As described above, there is a problem that it is difficult to appropriately calculate the value of data owned by a business operator.

  The present application has been made in view of the above, and an object of the present invention is to provide an information processing apparatus, an information processing method, and an information processing program for appropriately calculating the value of data owned by a business operator.

  An information processing apparatus according to the present application includes an acquisition unit that acquires characteristic information indicating characteristics of data owned by a business, and indicates a value of the data based on the characteristic information of the data acquired by the acquisition unit. And a calculating unit for calculating a value score.

  According to an aspect of the embodiment, it is possible to appropriately calculate the value of data owned by a business operator.

FIG. 1 is a diagram illustrating an example of information processing according to the embodiment. FIG. 2 is a diagram illustrating a configuration example of the information processing system according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the information processing apparatus according to the embodiment. FIG. 4 is a diagram illustrating an example of a company information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of a feature information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of a value score information storage unit according to the embodiment. FIG. 7 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 8 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device.

  Hereinafter, an information processing apparatus, an information processing method, and an embodiment (hereinafter, referred to as an “embodiment”) for executing an information processing program according to the present application will be described in detail with reference to the drawings. The information processing apparatus, the information processing method, and the information processing program according to the present application are not limited by the embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description will be omitted.

(Embodiment)
[1. Information processing)
First, an example of information processing according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of information processing according to the embodiment. FIG. 1 illustrates an example in which the information processing apparatus 100 (see FIG. 3) calculates a value score of data of a business based on characteristic information of data owned by the business.

  In the example of FIG. 1, the information processing apparatus 100 includes data DT1 of the business operator EA (see FIG. 4) and data of the business operator EB (see FIG. 4) that use a predetermined platform (hereinafter, referred to as “platform PF”). Calculate the value score of DT2. Hereinafter, the value score may be simply described as “value”. The platform referred to here is an environment for providing a predetermined service (hereinafter, referred to as “platform service”). The platform PF illustrated in the example of FIG. 1 may be an environment provided by a service provider that performs various services using the information processing device 100. Further, the platform PF may be provided across a plurality of cloud services (hereinafter, also referred to as “cloud”).

  As described above, the description “business * (* is an arbitrary character string)” indicates that the business is a business identified by the business ID “*”. For example, when the description is “business EA”, the business is a business identified by a business ID “EA”. Further, when “data DT * (* is an arbitrary numerical value)” is described, it indicates that the data is data identified by the data ID “DT *”. For example, when "data DT1" is described, the data is data identified by data ID "DT1".

  Although not shown, the platform PF is used in such a manner that each owner (operator) who owns the data makes his / her data available to another operator or uses his / her own data of another operator. Platform service. It is assumed that each company can use data in the platform service via a predetermined interface (Interface: IF). For example, data registered in the platform PF shown in the example of FIG. 1 is based on a conventional technology related to various network computing such as a predetermined application programming interface (API) and a virtual machine (VM). By using it appropriately, it is possible to use it for acquisition and the like. For example, an operator may be able to use a predetermined application programming interface, a virtual machine, or the like to obtain data of another operator via the platform PF.

  In the example of FIG. 1, the operator EA has registered the data DT1 in the platform PF. For example, data DT1 is registered in the platform PF from the business terminal 20-1 (see FIG. 2) used by the business EA. For example, the data DT1 of the business EA is stored in the business server 50c used by the business EA. For example, the data DT1 of the provider EA is stored in the provider server 50-2 which is a cloud server outside the platform PF. Further, the data DT1 of the business operator EA is provided to another business operator who uses the platform PF via the platform PF by an acquisition request or the like via the platform PF. The data DT1 registered by the business operator EA may be any data (information). For example, the data DT1 registered by the business operator EA may be any data as long as the data is owned by the business operator EA. For example, when the business EA is a business that sells insurance, the data DT1 registered by the business EA may be insurance sales data or the like. For example, the data DT1 registered by the business operator EA may be information on various demographic attributes and psychographic attributes such as gender, age (age), occupation, and the like of the user (individual) who has subscribed to the insurance. The data DT1 registered by the business operator EA may include data (for example, assets and medical history) specific to insurance business. In the example of FIG. 1, it is assumed that the company EA is a company that uses the platform PF and is a company that is permitted to use data (such as the data DT1) owned by the company EA.

  Further, in the example of FIG. 1, the operator EB has registered the data DT2 in the platform PF. For example, data DT2 is registered in the platform PF from the business terminal 20-2 (see FIG. 2) used by the business EB. For example, the data DT2 of the company EB is stored in the company server 50-2 used by the company EB. For example, the data DT2 of the provider EB is stored in the provider server 50-2 which is a cloud server outside the platform PF. The data DT2 of the operator EB is provided to another operator using the platform PF via the platform PF by an acquisition request or the like via the platform PF. The data DT2 registered by the company EB may be any data (information). For example, the data DT2 registered by the company EB may be any data as long as the data is owned by the company EB. For example, the data DT2 registered by the company EB may be car sales data or the like when the company EB is a company that sells cars. For example, the data DT2 registered by the company EB may be information on various demographic attributes and psychographic attributes such as gender, age (age), and occupation of the user (individual) who purchased the car. The data DT2 registered by the business operator EB may include data unique to the automobile sales business (for example, whether or not the automobile is owned). Further, in the example of FIG. 1, it is assumed that the company EA is a company that uses the platform PF, and is a company that is permitted to use data (such as the data DT2) owned by the company EB.

  First, in the example of FIG. 1, the information processing device 100 acquires the data DT1 (step S11). The information processing device 100 acquires the data DT1 based on the registered data information stored in the business information storage unit 121 (see FIG. 4). The information processing apparatus 100 transmits the data DT1 stored in the provider information storage unit 121 (see FIG. 4) to the provider server 50-1 based on the information indicating that the data DT1 is stored in the provider server 50-1. Obtain the data DT1. For example, the information processing apparatus 100 acquires the data DT1 from the enterprise server 50-1 by transmitting an acquisition request for the data DT1 to the enterprise server 50-1. Note that the information processing apparatus 100 may acquire the data DT1 from the storage unit 120 when the data registered by the business entity is stored in the storage unit 120 (see FIG. 3).

  Then, the information processing device 100 calculates the value of the data DT1 (Step S12). The information processing device 100 calculates the value of the data DT1 based on the characteristic information of the data DT1. For example, the information processing apparatus 100 calculates the value of the data DT1 based on the feature information of the data DT1 as shown in the feature information storage unit 122 (see FIG. 5). The information processing apparatus 100 uses information such as “number of data columns”, “frequency of use of each column”, “number of data rows”, “frequency of use of each row”, and “update frequency” indicating characteristics of the data DT1 to generate data. Calculate the value of DT1.

  The “number of data columns” indicates the number of columns of the corresponding data. The “number of data columns” may be the number of columns of the corresponding data from which duplicate data has been eliminated. The “number of data columns” may be the number of columns of the corresponding data excluding the duplicate columns. For example, “the number of data columns” indicates the number of items (columns) of the corresponding data. “Use frequency of each column” indicates the use frequency of each column of the corresponding data. Note that “use of a column” here may mean that a company other than the company that owns the data uses (requests) data corresponding to an item in the data. For example, “use frequency of each column” indicates the use frequency of each item (column) of the corresponding data. For example, the “frequency of use of each column” may be an average of the number of times of use of each column of the corresponding data. For example, the “use frequency of each column” may be an average of the number of uses of each column of the corresponding data in a predetermined period (for example, one week or the like). For example, the “use frequency of each column” may be the total number of times of use of each column of the corresponding data. For example, the “frequency of use of each column” may be the total number of times each column of the corresponding data is used in a predetermined period (for example, one day).

  “Number of data rows” indicates the number of rows of corresponding data. For example, “the number of data rows” indicates the number of records of the corresponding data. The “number of data rows” may be the number of rows from which the duplicate data is eliminated among the corresponding data. The “number of data rows” may be the number of rows in the corresponding data excluding duplicate rows. “Use frequency of each row” indicates the use frequency of each row of the corresponding data. Note that “use of a row” here may mean that a business other than the business that owns the data uses (requests) a record (row) in the data. For example, “use frequency of each row” indicates the use frequency of each record of the corresponding data. For example, the “frequency of use of each row” may be an average of the number of times of use of each row of the corresponding data. For example, the “frequency of use of each row” may be the average of the number of times each row of the corresponding data is used in a predetermined period (for example, three days). For example, the “frequency of use of each row” may be the total number of times of use of each row of the corresponding data. For example, the “frequency of use of each row” may be the sum of the number of uses of each row of the corresponding data in a predetermined period (for example, two days).

  “Update frequency” indicates the frequency at which the corresponding data is updated. For example, the “update frequency” may be a frequency at which a new record is added to the corresponding data. For example, the “update frequency” may be a frequency at which a new record is added to the corresponding data during a predetermined period (for example, five days). Further, for example, the “update frequency” may be a frequency at which the content of a record included in the corresponding data is updated. For example, the “update frequency” may be a frequency at which the content of the record included in the corresponding data is updated in a predetermined period (for example, one day).

  In the example illustrated in FIG. 1, the data DT1 indicates that the number of data columns is “DC1” and the use frequency of each column is “CF1”. The data DT1 indicates that the number of data rows is “DL1” and the usage frequency of each row is “LF1”. The data DT1 indicates that the update frequency is “RF1”.

  The information processing apparatus 100 determines the number of data columns “DC1”, the usage frequency of each column “CF1”, the number of data rows “DL1”, the usage frequency of each row “LF1”, the update frequency “RF1”, etc., which indicate the characteristics of the data DT1. To calculate the value of the data DT1. Note that the number of data columns “DC1”, the use frequency “CF1” of each column, the number of data rows “DL1”, the use frequency “LF1” of each row, and the update frequency “RF1” are specific numerical values.

  Note that the information processing device 100 may generate (extract) feature information of the data DT1 from the data DT1, or may acquire feature information of the data DT1 from a predetermined external device. For example, the information processing apparatus 100 may generate characteristic information of the data DT1 by analyzing a row (record) or a column (column) of the data DT1. Further, for example, the information processing apparatus 100 may acquire the characteristic information of the data DT1 from the business server 50-1 or the business terminal 20-1.

  Then, in the example of FIG. 1, the information processing apparatus 100 calculates the value of the data DT1 using the characteristic information of the data DT1 and the following equation (1). Note that the following equation (1) corresponds to the calculation equation FC in FIG.

  Data value score = (number of data columns * frequency of use of each column + number of rows of data * frequency of use of each row) * (coefficient * update frequency) + constant ... (1)

  In the above equation (1), predetermined values are assigned to “coefficient” and “constant”. For example, the information processing apparatus 100 sets the “constant” to a fixed value so that the price does not fall below a predetermined value. In addition, for example, the information processing apparatus 100 expresses the update frequency as “percentage” or the like for “coefficient * update frequency” in the above equation (1), and multiplies the update frequency by a coefficient to obtain a value when the update frequency is high. Is higher (larger).

  Then, the information processing apparatus 100 calculates the value of the data DT1 by assigning the characteristic information of the data DT1 to each term of the above equation (1). Note that the above equation (1) is an example, and the information processing apparatus 100 may calculate the value of the data by appropriately using various information.

  In FIG. 1, the information processing apparatus 100 calculates the value of the data DT1 as in the following equation (2) by assigning the characteristic information of the data DT1 to each term of the above equation (1).

  Value score of data DT1 = (DC1 * CF1 + DL1 * LF1) * (coefficient * RF1) + constant (2)

  The information processing apparatus 100 calculates the value score of the data DT1 as “VS1” as shown in the score information SL1 in FIG. The value score “VS1” is a specific numerical value.

  Further, in the example of FIG. 1, the information processing apparatus 100 acquires the data DT2 (step S21). The information processing apparatus 100 acquires the data DT2 based on the registration data information stored in the business information storage unit 121 (see FIG. 4). The information processing apparatus 100 transmits the data DT2 stored in the provider information storage unit 121 (see FIG. 4) to the provider server 50-2 based on the information indicating that the data DT2 is stored in the provider server 50-2. Obtain the data DT2. For example, the information processing device 100 acquires the data DT2 from the enterprise server 50-2 by transmitting an acquisition request for the data DT2 to the enterprise server 50-2. Note that the information processing apparatus 100 may acquire the data DT2 from the storage unit 120 when the data registered by the business entity is stored in the storage unit 120 (see FIG. 3).

  Then, the information processing device 100 calculates the value of the data DT2 (Step S22). The information processing device 100 calculates the value of the data DT2 based on the characteristic information of the data DT2. For example, the information processing apparatus 100 calculates the value of the data DT2 based on the feature information of the data DT2 as shown in the feature information storage unit 122 (see FIG. 5). The information processing apparatus 100 uses information such as “number of data columns”, “frequency of use of each column”, “number of data rows”, “frequency of use of each row”, and “update frequency”, which indicate the characteristics of the data DT2. Calculate the value of DT2.

  In the example shown in FIG. 1, the data DT2 indicates that the number of data columns is “DC2” and the use frequency of each column is “CF2”. The data DT2 indicates that the number of data rows is “DL2” and the usage frequency of each row is “LF2”. The data DT2 indicates that the update frequency is “RF2”.

  The information processing apparatus 100 determines the number of data columns “DC2”, the usage frequency of each column “CF2”, the number of data rows “DL2”, the usage frequency of each row “LF2”, the update frequency “RF2”, etc., which indicate the characteristics of the data DT2. To calculate the value of the data DT2. The number of data columns “DC2”, the use frequency “CF2” of each column, the number of data rows “DL2”, the use frequency “LF2” of each row, and the update frequency “RF2” are specific numerical values.

  Note that the information processing device 100 may generate (extract) feature information of the data DT2 from the data DT2, or may acquire feature information of the data DT2 from a predetermined external device. For example, the information processing apparatus 100 may generate characteristic information of the data DT2 by analyzing a row (record) or a column (column) of the data DT2. Further, for example, the information processing apparatus 100 may acquire the characteristic information of the data DT2 from the business server 50-2, the business terminal 20-2, or the like.

  Then, in the example of FIG. 1, the information processing apparatus 100 calculates the value of the data DT2 using the characteristic information of the data DT2 and the above equation (1). The information processing apparatus 100 calculates the value of the data DT2 by assigning the characteristic information of the data DT2 to each term of the above equation (1). In FIG. 1, the information processing apparatus 100 calculates the value of the data DT2 as in the following equation (3) by assigning the characteristic information of the data DT2 to each term of the above equation (1).

  Value score of data DT2 = (DC2 * CF2 + DL2 * LF2) * (coefficient * RF2) + constant (3)

  The information processing apparatus 100 calculates the value score of the data DT2 as “VS2” as shown in the score information SL2 in FIG. 1 by the above equation (3). The value score “VS2” is a specific numerical value.

  As described above, the information processing apparatus 100 calculates the value score of the data DT1 as “VS1” based on the characteristic information of the data DT1. Further, the information processing device 100 calculates the value score of the data DT2 as “VS2” based on the characteristic information of the data DT2. As described above, the information processing apparatus 100 can appropriately calculate the value of data owned by the business operator.

  For example, in the mutual use of data in the platform PF as described above, in order to preferentially provide data providers (providers) that provide good data and to discount data providers (providers) that provide bad data, Good data definitions are needed. Also, information (data) is different from normal products (tangibles). Even if provided, inventory does not decrease. On the other hand, as the value of information spreads, it becomes information that everyone knows, and its value decreases. In other words, good data is likely to be used while its value is reduced. Therefore, in the information processing system 1, a value based on data is defined. Specifically, in the information processing system 1, the value of data is defined based on quantity and quality.

  For example, the amount of data is defined based on a unique data amount. For example, the amount of data is defined based on the amount of data deduplicated except for redundant data. For example, the number of unique data rows in one data table or the like is simply defined as the data amount, and the larger the amount, the larger the amount of data. Therefore, in the information processing system 1, the value of data is higher as the data amount is larger. Similarly, in the information processing system 1, the value is higher as the number of columns is larger.

  For example, the quality of data is defined based on the freshness of information (data). The information (data) having a higher update frequency has higher data quality. Therefore, in the information processing system 1, information having a higher update frequency has a higher data quality and has a higher data value. Further, in the information processing system 1, data that is difficult to change keeps its value low. For example, in the information processing system 1, although the master data and the like are excellent, the value is kept low because they are not expensive.

  Data quality is defined based on the frequency of use of information (data). For example, in the information processing system 1, the value is reduced when the number of rows and columns used is small even if the data amount is large. As a result, it is possible to prevent the business operator from providing a useless row or column to show a high value. Further, in the information processing system 1, data that cannot be provided to other businesses or the like has high value for each service (business), but is data that cannot be used by many businesses as the platform PF. make low. As a result, the information processing system 1 calculates the value of data that cannot be provided to other businesses or the like as having a low value in an environment where data such as the platform PF is used mutually.

[1-1. Value comparison)
In addition, the information processing apparatus 100 can compare the values of the respective data by comparing the calculated value scores. For example, the information processing device 100 can compare the value of the data DT1 with the value of the data DT2 by comparing the value score “VS1” of the data DT1 with the value score “VS2” of the data DT2. For example, when the value score “VS1” is larger than the value score “VS2”, the information processing device 100 can determine that the data DT1 is higher in value than the data DT2. Further, for example, when the value score “VS2” is larger than the value score “VS1”, the information processing apparatus 100 can determine that the data DT2 is higher in value than the data DT1. For example, when the value score “VS1” is equal to the value score “VS2”, the information processing apparatus 100 can determine that the data DT1 and the data DT2 are equivalent.

[1-2. Value calculation based on rarity (rareness)]
Note that the information processing apparatus 100 may calculate the value of the data by appropriately using various information. For example, the information processing apparatus 100 may calculate the value of the data using the rarity (rareness) of the data. For example, the information processing apparatus 100 may calculate the value such that the higher the rarity of the data, the higher the value of the data.

  For example, when the data items (columns) are less than other data, the information processing apparatus 100 may determine that the rarity of the data is high and increase the value indicating the rarity of the data. For example, when there are many data items (columns) in other data, the information processing apparatus 100 may determine that the rarity of the data is low, and reduce the value indicating the rarity of the data.

  For example, the information processing apparatus 100 may calculate the value of the data using the following equation (4).

  Data value score = (number of data columns * frequency of use of each column + number of rows of data * frequency of use of each row) * (coefficient * update frequency) * rareness + constant (4)

  Note that the above equation (4) is an example, and the information processing apparatus 100 may calculate the value of the data in consideration of the rarity level by appropriately using various information.

[1-3. Rarity (rareness)
For example, the information processing apparatus 100 may calculate the rarity of each data using various types of information stored in the company information storage unit 121 (see FIG. 4). The information processing apparatus 100 may calculate the value score of the data based on a comparison between the data of one provider and other data of another provider. For example, the information processing apparatus 100 may calculate the value score of data based on a comparison between a column that is an element of data and a column that is another element of other data.

  The information processing apparatus 100 calculates the degree of rarity of each data using information for converting the data format (metadata format) of the data of the business to the data format (metadata format) of another business. You may. The information processing apparatus 100 may calculate the degree of rarity of each data using the correspondence information indicating the correspondence between the data of the respective operators. The information processing apparatus 100 may calculate the rarity of each data using the correspondence information indicating the correspondence between the metadata formats of the data of each business. The information processing apparatus 100 may calculate the rarity of each data using the correspondence information indicating the correspondence between the items (columns) of the data included in the metadata of each business entity. The information processing apparatus 100 may calculate the rarity of each data using the conversion information indicating the conversion content in the metadata conversion. The information processing apparatus 100 may calculate the degree of rarity of each data based on conversion information such as a conversion table used for conversion of data between the operators.

  The information processing apparatus 100 calculates the data value score based on the number of other businesses that own other data including other elements (columns and the like) corresponding to the data elements (columns and the like). Is also good. The information processing apparatus 100 may calculate the value score of the data such that the value increases as the number of other businesses that own other data including other elements corresponding to the elements of the data decreases. Good. The information processing apparatus 100 may calculate the degree of rarity of each data based on the number of conversion destination businesses that can convert the data columns. For example, the information processing apparatus 100 may calculate a higher degree of rarity of each data as the number of conversion destination businesses to which data columns can be converted is smaller. For example, the information processing apparatus 100 may calculate the rarity of each data to be lower as the number of conversion destinations to which the data column can be converted is larger.

  The information processing apparatus 100 may calculate the partial rarity for each column of data, and calculate the rarity of the data based on the partial rarity of each column. For example, the information processing apparatus 100 may calculate the total of the partial rarity of each column of the data as the rarity of the data. For example, the information processing apparatus 100 may calculate the average of the partial rarity of each column of the data as the rarity of the data.

  For example, when the column “gender” in the data DT1 of the business EA can be converted to a column of data of many businesses, the information processing apparatus 100 calculates the partial rarity of the column “gender” in the data DT1 to be low. You may. If the column “sex” in the data DT1 of the company EA can be converted to the column of the data of the company 100, the information processing apparatus 100 calculates the partial rarity of the column “sex” in the data DT1 to be low. Is also good. For example, the information processing apparatus 100 may calculate the partial rarity of the column using the following equation (5).

  Partial rarity = 1 / Number of businesses that can be converted ... (5)

  The information processing apparatus 100 may calculate the partial rare degree at which the maximum value is “1” using the above equation (5). For example, if the column “gender” in the data DT1 of the business EA can be converted into a column of 100 business data, the information processing apparatus 100 sets the partial rarity of the column “gender” in the data DT1 to “ 0.01 (= 1/100) ".

  In addition, when the column “asset” in the data DT1 of the company EA can be converted into a column of data of a small number of companies, the information processing apparatus 100 calculates the partial rare degree of the column “asset” in the data DT1 to be high. You may. If the column “Asset” in the data DT1 of the company EA can be converted into only the column of the data of one company, the information processing apparatus 100 increases the partial rare degree of the column “By Asset” in the data DT1. It may be calculated. For example, the information processing apparatus 100 may calculate the partial rarity of the column using the following equation (5).

  For example, if the column “Asset” in the data DT1 of the company EA can be converted into only the column of the data of one company, the information processing apparatus 100 determines the partial rare degree of the column “Asset” in the data DT1. It may be calculated as “1 (= 1/1)”.

  When the number of businesses that can be converted is “0”, the partial rare degree calculated by the above equation (5) is “∞ (infinity)”. The partial rare degree of the column of “0” may be calculated as a predetermined value (for example, a number greater than 1). For example, when the number of businesses that can be converted is “0”, the information processing apparatus 100 may calculate the partial rare degree of the column as “10”. For example, if the column “medical history” in the data DT1 of the business EA cannot be converted to any of the data columns of the business, the information processing apparatus 100 sets the number of business convertible to “0” and sets the number of business convertible to “0”. The partial rare degree may be calculated. The information processing apparatus 100 may calculate the partial rare degree of the column “medical history” in the data DT1 in which the number of convertible businesses is “0” to be “10”. If the number of convertible businesses is “0”, the information processing device 100 may determine that the value is low because there is no exchangeability, and may calculate the partial rare degree of the column as “0”.

  Then, the information processing apparatus 100 may calculate the rarity of the data DT1 based on the partial rarity of each column in the data DT1 of the business operator EA calculated as described above. For example, the information processing apparatus 100 may calculate, as the rarity of the data DT1, the sum of the partial rarity of each column in the data DT1 of the business operator EA calculated as described above. Further, for example, the information processing apparatus 100 may calculate, as the rarity of the data DT1, the average of the partial rarity of each column in the data DT1 of the business operator EA calculated as described above.

  Note that the above is an example, and the information processing apparatus 100 may calculate the rarity of each data in any manner. For example, the data column is an example of a data element, and the information processing apparatus 100 may calculate the value score of the data based on a comparison of any element as long as the element is a data element. For example, the information processing apparatus 100 may calculate the value score of the data based on the comparison of the records of the data.

[1-4. Provision of services based on the value of data)
Further, the information processing apparatus 100 may provide various services to the business operator according to the value of data owned by the business operator. The information processing apparatus 100 may change the mode of the service provided to the business operator according to the value of the data owned by the business operator. For example, the information processing apparatus 100 may provide a predetermined incentive to a business owner having high-value data. For example, the information processing apparatus 100 may provide a predetermined incentive to a business owner having high-value data. For example, the information processing apparatus 100 may make the platform PF available preferentially to a business owner having high-value data. For example, the information processing apparatus 100 may allow a business owner having high-value data to preferentially acquire data of another business via the platform PF.

  Further, the information processing apparatus 100 does not have to provide a predetermined service to a business owner having low-value data. The information processing apparatus 100 may limit the data of another business via the platform PF to the business that owns the data having the value score less than the predetermined threshold. The information processing apparatus 100 may make data having a value score equal to or greater than the predetermined threshold unavailable to a business owner having data having a value score less than the predetermined threshold. For example, the information processing apparatus 100 may be able to use only data with a value score equal to or less than the value score of the data owned by the information processing apparatus 100.

[2. Configuration of information processing system]
As shown in FIG. 2, the information processing system 1 includes an enterprise terminal 20, an enterprise server 50, and an information processing device 100. The enterprise terminal 20, the enterprise server 50, and the information processing apparatus 100 are communicably connected via a predetermined network N by wire or wirelessly. FIG. 2 is a diagram illustrating a configuration example of the information processing system according to the embodiment. Note that the information processing system 1 may include a plurality of information processing apparatuses 100.

  For example, the information processing system 1 includes not only the business terminal 20-1, the business terminal 20-2, the business terminal 20-3, and the business terminal 20-4, but also five or more business terminals 20. May be included. In the case where the business terminal 20-1, the business terminal 20-2, the business terminal 20-3, and the business terminal 20-4 are described without distinction, the business terminal 20 may be described. . Further, the information processing system 1 includes not only the business server 50-1, the business server 50-2, the business server 50-3, and the business server 50-4, but also five or more business servers 50. May be included. In addition, when the business server 50-1, the business server 50-2, the business server 50-3, the business server 50-4, and the like are described without distinction, the business server 50 may be described. .

  The company terminal 20 is an information processing device used by a company (company). For example, the company terminal 20 is an information processing device (computer) used for storing (registering) data in the company server 50 and using data stored in the company server 50. The business terminal 20 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. Hereinafter, the business operator terminal 20 may be referred to as a business operator. That is, in the following, the business operator can be read as the business operator terminal 20.

  For example, the company terminal 20-1 is an information processing device used in the company EA. For example, the company terminal 20-2 is an information processing device used in the company EB. For example, the company terminal 20-3 is an information processing device used in the company EC. For example, the company terminal 20-4 is an information processing device used in the company ED. The business terminal 20 is realized by, for example, a smartphone, a tablet terminal, a notebook PC, a desktop PC, a mobile phone, a PDA, or the like. FIG. 1 shows a case where the business operator terminal 20 is a notebook PC.

  The information processing apparatus 100 is an information processing apparatus (computer) that calculates a value score indicating the value of data based on feature information indicating characteristics of data owned by the business operator. For example, the information processing apparatus 100 acquires various information from the business operator terminal 20 and the business server 50. For example, the information processing apparatus 100 transmits various information to the business operator terminal 20 and the business operator server 50. Note that the information processing apparatus 100 may acquire various types of information stored in the storage unit 120 (see FIG. 3) from an external information processing apparatus.

  The company server 50 is an information processing device used by each company. The company server 50 is a device that stores data owned by each company. The business server 50 may be a so-called cloud server. For example, the business server 50 has a storage unit that stores various data in the cloud service.

  The information processing system 1 is not limited to a cloud server, and may include servers in various operation modes. For example, the information processing system 1 may include a server in an on-premises operation mode arranged in a facility of a business operator or the like.

[2-1. System configuration example)
Here, an example of the configuration of the information processing system 1 according to the embodiment will be described. For example, the data of the company EA is stored in the company server 50-1. Data DT1 and the like are stored in the enterprise server 50-1. Note that the business server 50-1 may be a plurality of servers.

  For example, the data of the company EB is stored in the company server 50-2. Data DT2 and the like are stored in the enterprise server 50-2. Note that the business server 50-2 may be a plurality of servers. For example, the company server 50-3 stores the data of the company EC. Data DT3 and the like are stored in the enterprise server 50-3. Note that the business server 50-3 may be a plurality of servers. For example, the company server 50-4 stores the data of the company ED. Data DT4 and the like are stored in the enterprise server 50-4. Note that the business server 50-4 may be a plurality of servers.

  For example, the platform PF acquires data of each company by communicating information with the company servers 50-1 to 50-4. For example, the company EA registers the data in the platform PF by storing the data in the company server 50-1. Further, for example, the company EB registers the data in the platform PF by storing the data in the company server 50-2. This allows the platform PF to mutually use data between different businesses.

  For example, in the information processing system 1, each business operator may be able to access data arranged in each business server 50 via the platform PF. For example, in the information processing system 1, each provider may be able to access data arranged in one provider server 50 by using an application programming interface of the platform PF.

[2-2. System configuration mode)
The configuration illustrated in FIG. 2 is an example, and the information processing system 1 may have various configurations. For example, the information processing device 100 in the information processing system 1 may be integrated with the company server 50. In this case, one operator may provide the platform service using the information processing device 100. For example, in the information processing system 1, one company that owns data may be a service provider that provides a platform service.

  For example, in the information processing system 1, the platform PF may be built in the company server 50-1. For example, the platform PF may be built over a plurality of company servers 50, or the platform PF may be built on one company server 50. As described above, the configuration of the information processing system 1 may be any configuration as long as the above-described information processing and data control can be performed. That is, in the information processing system 1, any system configuration may be used as long as each business operator using the platform PF can mutually use data.

[3. Configuration of information processing device]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the information processing apparatus according to the embodiment. As shown in FIG. 3, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

(Communication unit 110)
The communication unit 110 is realized by, for example, an NIC (Network Interface Card) or the like. The communication unit 110 is connected to a predetermined network N (see FIG. 2) by wire or wirelessly, and transmits and receives information to and from the business operator terminal 20.

(Storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. As illustrated in FIG. 3, the storage unit 120 according to the embodiment includes a company information storage unit 121, a feature information storage unit 122, and a value score information storage unit 123. Although illustration is omitted, the storage unit 120 may store history information (log) indicating how each data is used. For example, the storage unit 120 may store information relating to registration of each data in the platform PF and a log indicating each business and how each data is used.

(Business information storage unit 121)
The company information storage unit 121 according to the embodiment stores various information about the company. For example, the company information storage unit 121 stores information about a company using the platform service. FIG. 4 is a diagram illustrating an example of a company information storage unit according to the embodiment. As shown in FIG. 4, the company information storage unit 121 includes items such as “company ID”, “business type”, “registered metadata information”, and “registered data information” as information on the company.

  “Business ID” indicates identification information for identifying a business. “Business type” indicates the business type of the business identified by the corresponding business ID. In the example of FIG. 4, the type of business is illustrated by an abstract code such as “A type of business”, but “type of business” is specific information such as “insurance”, “food”, “cosmetics”, “automobile”, etc. It may be.

  “Registered metadata information” indicates metadata registered by the business entity identified by the corresponding business entity ID. “Registered metadata information” indicates a metadata format (metadata format) of the data of the business entity. For example, “registered metadata information” indicates a metadata format of data registered in the platform PF by the business entity. “Registration data information” indicates data registered by a business identified by a corresponding business ID. For example, “registration data information” indicates data registered in the platform PF by the business operator.

  In the example shown in FIG. 4, the business entity (business EA) identified by the business operator ID “EA” indicates that the business type is “business type A”. This indicates that the metadata format of the data of the business operator EA is the metadata format MDT1. The operator EA indicates that the data DT1 has been registered in the platform PF. For example, the operator EA indicates that the data DT1 has been registered in the operator server 50-1.

  The business information storage unit 121 is not limited to the above, and may store various types of information according to purposes. For example, the company information storage unit 121 may store the name or the like of the company identified by the corresponding company ID. For example, the company information storage unit 121 may store information on the business scale such as the number of employees and sales of the company.

  Further, the company information storage unit 121 may store information for converting a data format (metadata format) of data of a company into a data format (metadata format) of another company. The company information storage unit 121 may store information (correspondence information) indicating the correspondence between the data of each company. The company information storage unit 121 may store correspondence information indicating a correspondence between metadata formats of data of each company. The company information storage unit 121 may store correspondence information indicating a correspondence between items (columns) of data included in metadata of each company. Further, the company information storage unit 121 may store information (conversion information) indicating the content of the conversion in the metadata conversion.

(Feature information storage unit 122)
The feature information storage unit 122 according to the embodiment stores information about data. The characteristic information storage unit 122 stores various information such as characteristic information of data. FIG. 5 is a diagram illustrating an example of a feature information storage unit according to the embodiment. As shown in FIG. 5, the feature information storage unit 122 stores “data ID”, “data column number”, “use frequency of each column”, “data line number”, “use frequency of each row”, “update frequency”. Items.

  “Data ID” indicates identification information for identifying data. For example, the data identified by the data ID “DT1” corresponds to the data DT1 owned by the company EA in FIG.

  The “number of data columns” indicates the number of columns of the corresponding data. For example, “the number of data columns” indicates the number of items (columns) of the corresponding data. In FIG. 5, the “number of data columns” is abstractly illustrated like “DC2”, but a specific value such as “10” or “100” is stored. “Use frequency of each column” indicates the use frequency of each column of the corresponding data. In FIG. 5, the "frequency of use of each column" is illustrated in an abstract manner such as "CF2", but specific values such as "5.4" and "25" are stored.

  Note that “use of a column” here may mean that a company other than the company that owns the data uses (requests) data corresponding to an item in the data. For example, “use frequency of each column” indicates the use frequency of each item (column) of the corresponding data. For example, the “frequency of use of each column” may be an average of the number of times of use of each column of the corresponding data. For example, the “use frequency of each column” may be an average of the number of uses of each column of the corresponding data in a predetermined period (for example, one week or the like).

  “Number of data rows” indicates the number of rows of corresponding data. For example, “the number of data rows” indicates the number of records of the corresponding data. In FIG. 5, the "number of data rows" is illustrated abstractly, such as "DL2", but a specific value such as "1000" or "50000" is stored. “Use frequency of each row” indicates the use frequency of each row of the corresponding data. In FIG. 5, the "frequency of use of each row" is abstractly illustrated as "LF2", but a specific value such as "15" or "35.2" is stored.

  “Note that“ use of a row ”here may mean that a business other than the business that owns the data uses (requests) a record (row) in the data. For example, “use frequency of each row” indicates the use frequency of each record of the corresponding data. For example, the “frequency of use of each row” may be an average of the number of times of use of each row of the corresponding data. For example, the “frequency of use of each row” may be the average of the number of times each row of the corresponding data is used in a predetermined period (for example, three days).

  “Update frequency” indicates the frequency at which the corresponding data is updated. In FIG. 5, the "update frequency" is illustrated abstractly, such as "RF2", but a specific value such as "8.2" or "350" is stored. For example, the “update frequency” may be a frequency at which a new record is added to the corresponding data. For example, the “update frequency” may be a frequency at which a new record is added to the corresponding data during a predetermined period (for example, five days). Further, for example, the “update frequency” may be a frequency at which the content of a record included in the corresponding data is updated. For example, the “update frequency” may be a frequency at which the content of the record included in the corresponding data is updated in a predetermined period (for example, one day).

  In the example illustrated in FIG. 5, the data (data DT1) identified by the data ID “DT1” indicates that the number of data columns is “DC1” and the use frequency of each column is “CF1”. The data DT1 indicates that the number of data rows is “DL1” and the usage frequency of each row is “LF1”. The data DT1 indicates that the update frequency is “RF1”.

  The characteristic information storage unit 122 is not limited to the above, and may store various types of information according to purposes. The characteristic information storage unit 122 may store information for specifying a business owner that owns each data.

  Further, the feature information storage unit 122 may store information indicating the rarity (rareness) of each data. For example, the characteristic information storage unit 122 may store information indicating a rarity (rareness) whose value increases as the number of items (columns) of each data is smaller than that of other data. The characteristic information storage unit 122 may store information indicating the degree of rarity of each data calculated based on the correspondence information and the conversion information.

(Value score information storage unit 123)
The value score information storage unit 123 according to the embodiment stores information on the value of data. The value score information storage unit 123 stores various information such as a value score of data. FIG. 6 is a diagram illustrating an example of a value score information storage unit according to the embodiment. As shown in FIG. 6, the value score information storage unit 123 has items such as “data ID” and “value score”.

  “Data ID” indicates identification information for identifying data. For example, the data identified by the data ID “DT2” corresponds to the data DT2 owned by the operator EB in FIG. “Value score” indicates the value of the corresponding data. In FIG. 5, the "value score" is illustrated abstractly, such as "VS2", but a specific value such as "35" or "105.8" is stored.

  In the example illustrated in FIG. 6, the data (data DT1) identified by the data ID “DT1” indicates that the value score is “VS1”.

  The value score information storage unit 123 is not limited to the above, and may store various types of information according to purposes. The value score information storage unit 123 may store the date and time when the value score was calculated. The value score information storage unit 123 may store information for specifying a business owner that owns each data.

(Control unit 130)
The control unit 130 is a controller, and various programs stored in a storage device such as the storage unit 120 inside the information processing apparatus 100 are executed by the CPU and the MPU using the RAM as a work area. Is realized by: For example, the various programs include various programs such as an installed management application. The control unit 130 is a controller, and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

  As illustrated in FIG. 3, the control unit 130 includes an acquisition unit 131, a calculation unit 132, and a provision unit 133, and implements or executes functions and operations of a management process described below. For example, the control unit 130 performs a management process for managing information used for performing information processing.

(Acquisition unit 131)
The acquisition unit 131 acquires various information. For example, the acquisition unit 131 acquires various information from an external information processing device. For example, the acquisition unit 131 acquires various types of information from the company terminal 20 and the company server 50. For example, the acquisition unit 131 acquires information indicating that a business operator has requested use of data of another business company. For example, the acquisition unit 131 acquires information indicating that the business operator requests use of data of another business operator from the business operator terminal 20 or the business operator server 50.

  The acquiring unit 131 acquires various information from the storage unit 120. The acquisition unit 131 acquires various types of information from the company information storage unit 121, the feature information storage unit 122, the value score information storage unit 123, and the like.

  The acquisition unit 131 acquires characteristic information indicating characteristics of data owned by the business. The acquisition unit 131 acquires quantity information indicating a quantitative characteristic of data. The acquisition unit 131 acquires amount information indicating the data amount excluding the duplicated data in the data. The acquisition unit 131 acquires characteristic information including quality information indicating a qualitative characteristic of data. The acquisition unit 131 acquires quality information indicating the frequency of updating data. The acquisition unit 131 acquires quality information on the data update frequency. The acquisition unit 131 acquires quality information indicating the frequency of use of data. The acquisition unit 131 acquires quality information related to data use frequency. The acquisition unit 131 acquires quality information indicating use of an element in data. The acquisition unit 131 acquires quality information on the use of elements in data. The acquisition unit 131 acquires other data owned by another business different from the business.

  In the example of FIG. 1, the acquisition unit 131 acquires the data DT1. The acquisition unit 131 acquires the data DT1 based on the registered data information stored in the business information storage unit 121. The acquisition unit 131 acquires the data DT1 from the business server 50-1 based on the information indicating that the data DT1 stored in the business information storage unit 121 is stored in the business server 50-1. The acquisition unit 131 acquires the data DT1 from the enterprise server 50-1 by transmitting an acquisition request for the data DT1 to the enterprise server 50-1.

  In the example of FIG. 1, the acquisition unit 131 acquires the data DT2. The acquiring unit 131 acquires the data DT2 based on the registered data information stored in the business information storage unit 121. The acquisition unit 131 acquires the data DT2 from the business server 50-2 based on information indicating that the data DT2 stored in the business information storage unit 121 is stored in the business server 50-2. The acquisition unit 131 acquires the data DT2 from the enterprise server 50-2 by transmitting an acquisition request for the data DT2 to the enterprise server 50-2.

(Calculation unit 132)
The calculation unit 132 calculates various information. The calculation unit 132 generates various information. The calculation unit 132 determines various information. The calculation unit 132 extracts various information. For example, the calculation unit 132 performs various calculations based on information stored in the company information storage unit 121, the feature information storage unit 122, the value score information storage unit 123, and the like. For example, the calculation unit 132 performs various calculation processes based on the information acquired by the acquisition unit 131.

  The calculation unit 132 calculates a value score indicating the value of the data based on the characteristic information of the data acquired by the acquisition unit 131. The calculation unit 132 calculates a value score of the data based on the data amount information. The calculation unit 132 calculates the value score of the data such that the value increases as the data amount increases. The calculation unit 132 calculates a value score of the data based on the quality information of the data.

  The calculation unit 132 calculates the value score of the data such that the value increases as the update frequency increases. The calculation unit 132 calculates the value score of the data such that the value increases as the use frequency increases. The calculating unit 132 calculates the value score of the data such that the value increases as the ratio of the used elements among the elements increases.

  The calculation unit 132 calculates a value score of the data based on a comparison between the data and other data. The calculation unit 132 calculates a data value score based on a comparison between the data element and another data element. The calculation unit 132 calculates a data value score based on the number of other businesses that own other data that includes other elements corresponding to the data elements. The calculation unit 132 calculates the data value score such that the value increases as the number of other businesses that own other data that includes other elements corresponding to the elements of the data decreases.

  In the example of FIG. 1, the calculation unit 132 calculates the value of the data DT1. The calculation unit 132 calculates the value of the data DT1 based on the feature information of the data DT1. For example, the calculation unit 132 calculates the value of the data DT1 based on the feature information of the data DT1 as shown in the feature information storage unit 122. The calculation unit 132 uses the information indicating the characteristics of the data DT1 such as “number of data columns”, “frequency of use of each column”, “number of data rows”, “frequency of use of each row”, and “update frequency”. Calculate the value of

  The calculating unit 132 uses the number of data columns “DC1”, the frequency of use of each column “CF1”, the number of data rows “DL1”, the frequency of use of each row “LF1”, the update frequency “RF1”, etc., which indicate the characteristics of the data DT1. Then, the value of the data DT1 is calculated. The calculating unit 132 calculates the value of the data DT1 by using the characteristic information of the data DT1 and the above equation (1).

  Then, the calculation unit 132 calculates the value of the data DT1 by assigning the characteristic information of the data DT1 to each term of the above equation (1). The calculation unit 132 calculates the value of the data DT1 as in the above equation (2) by assigning the characteristic information of the data DT1 to each term of the above equation (1). The calculating unit 132 calculates the value score of the data DT1 as “VS1” as shown in the score information SL1 in FIG.

  The calculation unit 132 calculates the value of the data DT2. The calculation unit 132 calculates the value of the data DT2 based on the feature information of the data DT2. For example, the calculation unit 132 calculates the value of the data DT2 based on the feature information of the data DT2 as shown in the feature information storage unit 122. The calculation unit 132 uses the information such as “number of data columns”, “frequency of use of each column”, “number of data rows”, “frequency of use of each row”, and “update frequency” indicating characteristics of the data DT2. Calculate the value of

  The calculating unit 132 uses the number of data columns “DC2”, the usage frequency of each column “CF2”, the number of data rows “DL2”, the usage frequency of each row “LF2”, the update frequency “RF2”, etc., which indicate the characteristics of the data DT2. Then, the value of the data DT2 is calculated.

  The calculation unit 132 calculates the value of the data DT2 using the feature information of the data DT2 and the above equation (1). The calculating unit 132 calculates the value of the data DT2 by assigning the characteristic information of the data DT2 to each term of the above equation (1). The calculation unit 132 calculates the value of the data DT2 as in the above equation (3) by assigning the characteristic information of the data DT2 to each term of the above equation (1). The calculation unit 132 calculates the value score of the data DT2 as “VS2”, as shown in the score information SL2 in FIG.

(Provider 133)
The providing unit 133 provides various information. For example, the providing unit 133 provides various types of information to an external information processing device such as the business terminal 20 or the business server 50. For example, the providing unit 133 requests various types of information from an external information processing device such as the business terminal 20 or the business server 50. The providing unit 133 transmits various information to the company terminal 10 and the company server 50. The providing unit 133 distributes various types of information to the company terminal 20, the company server 50, and the like. The providing unit 133 provides various types of information based on the various types of information acquired by the acquiring unit 131. The providing unit 133 provides various information based on the various information calculated by the calculation unit 132. For example, the providing unit 133 provides the value score of the data calculated by the calculating unit 132. The providing unit 133 provides the business with the value score of the data calculated by the calculating unit 132.

[4. Information processing flow)
Next, a procedure of information processing by the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 7 is a flowchart illustrating an example of information processing according to the embodiment.

  As shown in FIG. 7, the information processing apparatus 100 acquires characteristic information indicating characteristics of data owned by the business (step S101). Then, the information processing apparatus 100 calculates a value score indicating the value of the data based on the characteristic information (step S102).

[5. effect〕
As described above, the information processing device 100 according to the embodiment includes the acquisition unit 131 and the calculation unit 132. The acquisition unit 131 acquires characteristic information indicating characteristics of data owned by the business. The calculation unit 132 calculates a value score indicating the value of the data based on the characteristic information of the data acquired by the acquisition unit 131.

  As described above, the information processing apparatus 100 according to the embodiment can appropriately calculate the value of the data owned by the business entity by calculating the value score indicating the value of the data based on the characteristic information of the data. it can.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires quantity information indicating a quantitative feature of data. The calculation unit 132 calculates a value score of the data based on the data amount information.

  As described above, the information processing apparatus 100 according to the embodiment can appropriately calculate the value of the data owned by the business entity by calculating the data value score based on the data amount information.

  In addition, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires amount information indicating a data amount excluding redundant data in data. The calculation unit 132 calculates the value score of the data such that the value increases as the data amount increases.

  As described above, the information processing apparatus 100 according to the embodiment acquires the amount information indicating the data amount excluding the duplicated data in the data, and calculates the value score of the data such that the value increases as the data amount increases. By doing so, the value of the data owned by the business can be appropriately calculated.

  In the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires feature information including quality information indicating a qualitative feature of data. The calculation unit 132 calculates a value score of the data based on the quality information of the data.

  As described above, the information processing apparatus 100 according to the embodiment can appropriately calculate the value of the data owned by the business entity by calculating the value score of the data based on the quality information of the data.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires quality information indicating a data update frequency. The calculation unit 132 calculates the value score of the data such that the value increases as the update frequency increases.

  As described above, the information processing apparatus 100 according to the embodiment acquires quality information indicating the data update frequency, and calculates the value score of the data such that the value increases as the update frequency increases. Can appropriately calculate the value of the data owned by.

  In the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires quality information indicating the frequency of use of data. The calculation unit 132 calculates the value score of the data such that the value increases as the use frequency increases.

  As described above, the information processing apparatus 100 according to the embodiment acquires quality information indicating the frequency of use of data, and calculates the value score of data such that the value increases as the frequency of use increases. Can appropriately calculate the value of the data owned by.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires other data owned by another business different from the business. The calculation unit 132 calculates a value score of the data based on a comparison between the data and other data.

  As described above, the information processing apparatus 100 according to the embodiment acquires another data owned by another business different from the business, and calculates the data value score based on the comparison between the data and the other data. By calculating, the value of the data owned by the business can be appropriately calculated.

  In the information processing apparatus 100 according to the embodiment, the calculation unit 132 calculates a data value score based on a comparison between a data element and another data element.

  As described above, the information processing apparatus 100 according to the embodiment calculates the value score of the data based on the comparison between the element of the data and the other element of the other data, so that the value of the data owned by the company is Can be calculated appropriately.

  Further, in the information processing apparatus 100 according to the embodiment, the calculation unit 132 calculates the value score of the data based on the number of other businesses that own other data including another element corresponding to the element of the data. calculate.

  As described above, the information processing apparatus 100 according to the embodiment calculates the data value score based on the number of other businesses that own other data including other elements corresponding to the data elements. Accordingly, the value of the data owned by the business can be appropriately calculated.

  Further, in the information processing apparatus 100 according to the embodiment, the calculating unit 132 may increase the value as the number of other businesses that own other data including other elements corresponding to the data elements decreases. , Calculate the value score of the data.

  As described above, the information processing apparatus 100 according to the embodiment performs the data processing such that the value increases as the number of other businesses that own other data including other elements corresponding to the data elements increases. By calculating the value score, it is possible to appropriately calculate the value of the data owned by the business.

[6. Hardware configuration)
The information processing apparatus 100 according to the embodiment described above is realized by, for example, a computer 1000 having a configuration as shown in FIG. FIG. 8 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device. The computer 1000 includes a CPU 1100, a RAM 1200, a read only memory (ROM) 1300, a hard disk drive (HDD) 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F). ) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400, and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, a program that depends on the hardware of the computer 1000, and the like.

  The HDD 1400 stores a program executed by the CPU 1100, data used by the program, and the like. The communication interface 1500 receives data from another device via a predetermined network N, sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to the other device via the predetermined network N.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from an input device via the input / output interface 1600. Further, CPU 1100 outputs the generated data to an output device via input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides the program or data to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 to the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc), a PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. And so on.

  For example, when the computer 1000 functions as the information processing apparatus 100 according to the embodiment, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them. As another example, these programs may be obtained from another device via a predetermined network N.

  The embodiments of the present application have been described in detail with reference to the drawings. However, these are merely examples, and various modifications and improvements may be made based on the knowledge of those skilled in the art, including the aspects described in the line of disclosure of the invention. The present invention can be implemented in other forms described above.

[7. Others)
Further, of the processes described in the above embodiments, all or a part of the processes described as being performed automatically can be manually performed, or the processes described as being performed manually can be performed. Can be automatically or entirely performed by a known method. In addition, the processing procedure, specific names, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown.

  Each component of each device illustrated is a functional concept, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to the illustrated one, and all or a part of the distribution / integration may be functionally or physically distributed / arbitrarily in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

  Further, the processes described in the above-described embodiments can be appropriately combined within a range that does not contradict the processing contents.

  Further, the above-mentioned “section (section, module, unit)” can be read as “means”, “circuit”, or the like. For example, the obtaining unit can be replaced with an obtaining unit or an obtaining circuit.

DESCRIPTION OF SYMBOLS 1 Information processing system 100 Information processing apparatus 121 Business information storage part 122 Feature information storage part 123 Value score information storage part 130 Control part 131 Acquisition part 132 Calculation part 133 Provision part 20 Business terminal 50 Business server PF platform N network

Claims (12)

An acquisition unit configured to acquire characteristic information indicating characteristics of data owned by the business;
A calculating unit that calculates a value score indicating a value of the data, based on the feature information of the data acquired by the acquiring unit;
An information processing apparatus comprising: The acquisition unit,
Obtaining quantitative information indicating a quantitative characteristic of the data,
The calculation unit,
The information processing device according to claim 1, wherein the value score of the data is calculated based on the amount information of the data. The acquisition unit,
Acquiring the amount information indicating the data amount excluding the duplicated data in the data,
The calculation unit,
The information processing device according to claim 2, wherein the value score of the data is calculated such that the value increases as the data amount increases. The acquisition unit,
Acquiring the characteristic information including quality information indicating a qualitative characteristic of the data,
The calculation unit,
The information processing apparatus according to claim 1, wherein the value score of the data is calculated based on the quality information of the data. The acquisition unit,
Acquiring the quality information indicating the update frequency of the data,
The calculation unit,
The information processing apparatus according to claim 4, wherein the value score of the data is calculated such that the value increases as the update frequency increases. The acquisition unit,
Acquiring the quality information indicating the frequency of use of the data,
The calculation unit,
The information processing apparatus according to claim 4, wherein the value score of the data is calculated such that the value increases as the use frequency increases. The acquisition unit,
Obtain other data owned by another business different from the business,
The calculation unit,
The information processing device according to claim 1, wherein the value score of the data is calculated based on a comparison between the data and the other data. The calculation unit,
The information processing apparatus according to claim 7, wherein the value score of the data is calculated based on a comparison between an element of the data and another element of the other data. The calculation unit,
The said value score of the said data is calculated based on the number of the said another business which owns the said another data in which the said another element corresponding to the said element of the said data is contained. An information processing apparatus according to claim 1. The calculation unit,
Calculating the value score of the data such that the value increases as the number of the other businesses that owns the other data includes the other data corresponding to the element of the data. The information processing apparatus according to claim 9, wherein: An information processing method executed by a computer,
An acquisition step of acquiring characteristic information indicating characteristics of data owned by the business;
A calculating step of calculating a value score indicating the value of the data, based on the feature information of the data obtained in the obtaining step,
An information processing method comprising: An acquisition procedure for acquiring characteristic information indicating characteristics of data owned by the business;
Based on the feature information of the data acquired by the acquisition procedure, a calculation procedure of calculating a value score indicating the value of the data,
An information processing program for causing a computer to execute the following.

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