Information gathering system and information evaluation method

Abstract

To provide an information collection system and an information evaluation method that can efficiently collect data on a plurality of themes.SOLUTION: An estimated cost calculation unit 63 calculates, on the basis of log information of storage processing for storing data, for each data theme, a cost evaluation value obtained by evaluating cost of data storage. An estimated effect calculation unit 62 calculates, on the basis of log information of reference processing that refers to data, for each theme, an effect evaluation value obtained by evaluating a value of data. A cost-effectiveness visualization unit 64 calculates, for each theme, a cost-effectiveness value, which is a comparison value, by comparing the cost evaluation value and the effect evaluation value.SELECTED DRAWING: Figure 2

Description

Technical Field

[0006] , , , , ,

[0001] The present disclosure relates to an information collection system and an information evaluation method.

Background Art

[0002] In industrial fields and the like, in recent years, for the improvement of operations in factories and departments, etc., the use of an information collection system that collects data from various places in the value chain and stores it in a data lake has attracted attention. By using this type of information collection system, it becomes possible to analyze the data stored in the data lake and plan for the early development and sharing of countermeasure means for various themes, etc., enabling further improvement of operations.

[0003] However, simply collecting various data will increase the cost of collecting and accumulating data, such as the amount of data stored in the data lake becoming huge, making it difficult to efficiently improve operations.

[0004] In contrast, Patent Document 1 discloses a technique for adjusting the accuracy of data to be collected hereafter (such as the resolution of an image and the sampling rate of audio) based on the analysis result of data that has already been acquired and the status of a storage unit that stores the data. By using this technique, it becomes possible to collect necessary data while reducing the size of the data to be collected.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] In factories and other industrial settings, various themes requiring improvement exist, and data lakes store data related to these diverse themes. However, typically, those responsible for each theme are unaware of the cost of collecting data related to other themes. As a result, data related to specific themes with low cost-effectiveness for business improvement may accumulate in large quantities compared to data related to other themes, leading to a lower overall cost-effectiveness for the business.

[0007] The technology described in Patent Document 1 does not take into account the existence of multiple themes, making it difficult to efficiently collect data related to multiple themes and thus hindering the reduction in cost-effectiveness for the overall operation.

[0008] The purpose of this disclosure is to provide an information collection system and an information evaluation method that can efficiently collect data on multiple themes. [Means for solving the problem]

[0009] An information collection system according to one aspect of the present disclosure is an information collection system that stores and references data relating to a plurality of themes, comprising: a cost calculation unit that calculates a cost evaluation value for each theme based on log information of a data storage process that stores the data, an effect calculation unit that calculates an effect evaluation value for each theme based on log information of a data reference process that references the data, and a cost-benefit calculation unit that calculates a comparison value for each theme by comparing the cost evaluation value and the effect evaluation value. [Effects of the Invention]

[0010] According to the present invention, it becomes possible to efficiently collect data on multiple themes. [Brief explanation of the drawing]

[0011] [Figure 1] This figure shows an example of the hardware configuration of an information gathering system. [Figure 2] It is a diagram showing an example of the functional configuration of an information collection system. [Figure 3] It is a diagram showing an example of an application account table. [Figure 4] It is a diagram showing an example of a DB reference account table. [Figure 5] It is a diagram showing an example of a collection account table. [Figure 6] It is a diagram showing an example of an ETL table. [Figure 7] It is a diagram showing an example of an assumed effect table. [Figure 8] It is a diagram showing an example of an assumed cost table. [Figure 9] It is a diagram showing an example of a cost - effectiveness table. [Figure 10] It is a flowchart for explaining an example of an account registration process. [Figure 11] It is a flowchart for explaining an example of a collection process. [Figure 12] It is a flowchart for explaining an example of a processing process. [Figure 13] It is a flowchart for explaining an example of a data reference process. [Figure 14] It is a flowchart for explaining an example of an effect calculation process. [Figure 15] It is a flowchart for explaining an example of a cost calculation process. [Figure 16] It is a flowchart for explaining an example of a visualization process.

Embodiments for Carrying Out the Invention

[0012] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0013] FIG. 1 is a diagram showing an example of the hardware configuration of an information collection system according to an embodiment of the present disclosure. The information collection system 1 shown in FIG. 1 includes a data collection device 10, a data processing device 20, data storage devices 30 and 40, a data reference device 50, and a data analysis device 60. Each of the devices 10 to 60 is communicably connected to each other via a network 70.

[0014] The data collection device 10 performs a collection process of collecting and storing data related to a plurality of themes. For example, the data collection device 10 collects data from target devices such as sensors, inspection devices, and processing devices arranged in the collection target area. The collection target area is, for example, a facility such as a factory and a store. In the present embodiment, the collection target area is a factory. The data is, for example, time-series data and includes voice data, video data, sensor data, and the like. Further, the data may have a table structure or the like. A theme is, for example, a problem to be solved, and more specifically, an improvement in production efficiency or a reduction in errors in each line of a factory. In this case, the data related to the theme is data that is considered beneficial or useful for solving the problem, and the relationship between the theme and the data is preset by a system operator or the like who operates the information collection system 1.

[0015] The data processing device 20 performs a processing process of processing the primary data, which is the data collected by the data collection device 10, to generate and store secondary data. The specific content of the processing process for processing the primary data is not particularly limited, but for example, it includes conversion of the data format, integration and elimination of data, extraction of specific elements, and compression of data.

[0016] Data storage devices 30 and 40 store the data collected and generated by the data collection process by the data acquisition device 10 and the data processing process by the data processing device 20. In this embodiment, data storage device 30 is a primary storage device that stores primary data collected by the data acquisition device 10, and data storage device 40 is a secondary storage device that stores secondary data processed by the data processing device 20. Note that the collection process and the processing process are included in the storage process that stores the data.

[0017] The data reference device 50 performs a reference process to access the data stored in the data storage devices 30 and 40. Specifically, the data reference device 50 executes an application program that references the data stored in the data storage devices 30 and 40, and the application program performs the data reference.

[0018] The data analysis device 60 analyzes the data collected by the data collection device 10 based on log information from the storage and reference processes, and evaluates the cost-effectiveness of data collection for each theme.

[0019] Each of the devices 10 to 60 included in the information collection system 1 is implemented, for example, as an information processing device. In the example shown in Figure 1, it includes an input unit 101, a communication interface (communication I / F) 102, a storage device 103, a memory 104, and a CPU (Central Processing Unit) 105.

[0020] The input unit 101 receives various information from users of each device 10 to 60. The communication interface 102 is connected to other devices via the network 70 and transmits and receives various information with other devices.

[0021] The storage device 103 stores a program (computer program) that defines the operation of the CPU 105, and various information used and generated by each of the devices 10 to 60. The memory 104 is a recording medium that functions as a work area for the program stored in the storage device 103. The CPU 105 is a processor that controls each of the devices 10 to 60, and performs various processes by reading the program stored in the storage device 103 and executing the read program using the memory 104 as a work area. The information collection system or information evaluation method included in this disclosure can be stored in a non-temporary storage medium MM such as flash memory or a hard disk drive. This storage medium may be directly connected to the computer or connected to the computer via a communication network. The computer can send and receive computer programs to and from the storage medium MM.

[0022] The hardware configuration of the information gathering system 1 shown in Figure 1 is merely an example and is not limited thereto. For example, each device 10 to 60 may have other configurations in addition to the illustrated configuration, such as a display device for displaying various information. In the example in Figure 1, the data analysis device 60 is equipped with a display device 106. Also, at least some of each device 10 to 60 may be implemented as the same device. For example, by configuring data storage devices 30 and 40 as the same device, primary and secondary data may be stored in the same device. Furthermore, each device 10 to 60 may be composed of multiple devices. In addition, the information gathering system 1 may have other configurations, such as an input device operated by a system operator (see input device 80 in Figure 2).

[0023] Figure 2 shows the functional configuration of the information gathering system 1.

[0024] As shown in Figure 2, the data collection device 10 of the information collection system 1 includes data collection information 11 and a data collection information management unit 12. The data collection information 11 is information related to the collection process of collecting and storing data by the data collection device 10, and includes, for example, a data collection log, which is log information of the collection process. The data collection information 11 is stored in the storage device 103 of the data collection device 10.

[0025] The data acquisition information management unit 12 is a management unit that manages the data acquisition information 11, for example, by transmitting the data acquisition information 11 to the data analysis device 60. The data acquisition information management unit 12 is implemented, for example, by the CPU 105 of the data acquisition device 10 executing a program.

[0026] Furthermore, the data processing device 20 includes data processing information 21 and a data processing information management unit 22. The data processing information 21 is information relating to processing that is performed by the data processing device 20 to process primary data and store it as secondary data, and includes, for example, a data processing log which is log information of the processing. The data processing information 21 is stored in the storage device 103 of the data processing device 20.

[0027] The data processing information management unit 22 is a management unit that manages the data processing information 21, and for example, transmits the data processing information 21 to the data analysis device 60. The data processing information management unit 22 is implemented, for example, by the CPU 105 of the data processing device 20 executing a program.

[0028] The data storage device 30 includes query execution information 31 and a query execution information management unit 32. The query execution information 31 is information related to query execution processing, which is processing in response to queries that request data referencing from the data reference device 50, and includes, for example, a query log, which is log information of query execution processing. The query execution information 31 is stored in the storage device 103 of the data storage device 30.

[0029] The query execution information management unit 32 is a management unit that manages the query execution information 31, and for example, transmits the query execution information 31 to the data analysis device 60. The query execution information management unit 32 is implemented, for example, by the CPU 105 of the data storage device 30 executing a program.

[0030] The data storage device 40 includes query execution information 41 and a query execution information management unit 42. The query execution information 41 is equivalent to the query execution information 31, and the query execution information management unit 42 is a management unit with functions equivalent to the query execution information management unit 32.

[0031] The data reference device 50 includes data reference information 51 and a data reference information management unit 52. The data reference information 51 is information about reference processing, which is processing performed by an application program that references data, and includes, for example, an application log, which is log information of the reference processing. The data reference information 51 is stored in the storage device 103 of the data reference device 50.

[0032] The data reference information management unit 52 is a management unit that manages the data reference information 51, and for example, transmits the data reference information 51 to the data analysis device 60. The data reference information management unit 52 is implemented, for example, by the CPU 105 of the data reference device 50 executing a program.

[0033] The data analysis device 60 includes an account information registration unit 61, an estimated effect calculation unit 62, an estimated cost calculation unit 63, and a cost-benefit visualization unit 64. Each of the units 61-64 is implemented by the CPU 105 of the data analysis device 60 executing a program.

[0034] The account information registration unit 61 is a registration unit that registers account information relating to accounts using the information collection system 1, and includes an account information receiving unit 611 and an account information storage unit 612.

[0035] The account information receiving unit 611 receives account information from the input device 80. The input device 80 is a terminal used by the system operator who operates the information collection system 1. In Figure 2, the input device 80 is shown separately from the data analysis device 60, but it may also be the input unit 101 of the data analysis device 60 shown in Figure 1.

[0036] The account information storage unit 612 is a storage unit that stores account information received by the account information receiving unit 611 in the storage device 103 of the data analysis device 60. In this embodiment, the account information includes the application account table 201, the DB reference account table 202, the collection account table 203, and the ETL (Extract / Transform / Load) table 204.

[0037] The assumed effect calculation unit 62 is an effect calculation unit that analyzes the data stored in the data storage devices 30 and 40 and calculates an effect evaluation value for each theme, which evaluates the value of the data related to that theme. The assumed effect calculation unit 62 includes a data acquisition unit 621, a utilization effect calculation unit 622, and a utilization effect storage unit 623.

[0038] The data acquisition unit 621 acquires query execution information 31, 41 and data reference information 51 from the data storage devices 30, 40 and the data reference device 50.

[0039] The utilization effect calculation unit 622 calculates the effectiveness evaluation value of the data stored in the data storage devices 30 and 40 for each theme, based on the log information contained in the query execution information 31, 41 and data reference information 51 acquired by the data acquisition unit 621.

[0040] The utilization effect storage unit 623 generates an assumed effect table 205 showing the effect evaluation values ​​calculated by the utilization effect calculation unit 622 and stores it in the storage device 103 of the data analysis device 60.

[0041] The estimated cost calculation unit 63 is a cost calculation unit that analyzes the data stored in the data storage devices 30 and 40 and calculates a cost evaluation value for each theme, which is an evaluation of the cost incurred in storing data related to that theme. The estimated cost calculation unit 63 includes a data acquisition unit 631, a usage cost calculation unit 632, and a usage cost storage unit 633.

[0042] The data acquisition unit 631 acquires data acquisition information 11 and data processing information 21 from the data acquisition device 10 and the data processing device 20.

[0043] The cost calculation unit 632 calculates a cost evaluation value for the data stored in the data storage devices 30 and 40 for each theme, based on the log information contained in the data collection information 11 and data processing information 21 acquired by the data acquisition unit 631.

[0044] The usage cost storage unit 633 generates an assumed cost table 206 showing the cost evaluation values ​​calculated by the usage cost calculation unit 632 and stores it in the storage device 103 of the data analysis device 60.

[0045] The cost-effectiveness visualization unit 64 is a comparison unit that visualizes the cost-effectiveness of data collection for each theme by calculating a cost-effectiveness value that evaluates the cost-effectiveness of data collection for each theme based on the assumed effect table 205 and the assumed cost table 206. The cost-effectiveness visualization unit 64 includes a cost-effectiveness calculation unit 641, a cost-effectiveness storage unit 642, and a display information generation unit 643.

[0046] The cost-effectiveness calculation unit 641 calculates a cost-effectiveness value for each theme, based on the assumed cost table 206, evaluating the cost-effectiveness of data collection related to that theme. In doing so, the cost-effectiveness calculation unit 641 may also use the application account table 201, the DB reference account table 202, the collection account table 203, the ETL table 204, the assumed effect table 205, and the assumed cost table 206, if necessary.

[0047] The cost-effectiveness storage unit 642 stores the cost-effectiveness table 207, which shows the cost-effectiveness values ​​calculated by the cost-effectiveness calculation unit 641, in the storage device 103 of the data analysis device 60 or the like.

[0048] The display information generation unit 643 generates display information showing cost-effectiveness values ​​based on the cost-effectiveness table 207 and displays it on the display device 106 of the data analysis device 60.

[0049] Figure 3 shows an example of an application account table 201. The application account table 201 shown in Figure 3 has fields 210 to 213.

[0050] Field 210 stores the factory name to identify the factory that is the area to be collected. Field 211 stores the theme name, which is the name of the theme in the factory. There may be multiple themes for the same factory, or one theme may be shared by multiple factories. Field 212 stores the theme ID to identify the theme. Field 213 stores the app account name to identify the management application program used by the user, which is the user account name to identify the user who manages the theme.

[0051] Figure 4 shows an example of a DB reference account table 202. The DB reference account table 202 shown in Figure 4 has fields 220 to 223.

[0052] Field 220 stores the factory name. Field 221 stores the theme name. Field 222 stores the theme ID. Field 223 stores the reference account name to identify the application program that references the data related to the theme with the theme ID.

[0053] Figure 5 shows an example of a collection account table 203. The collection account table 203 shown in Figure 5 has fields 230 to 233.

[0054] Field 230 stores the factory name. Field 231 stores the theme name. Field 232 stores the theme ID. Field 233 stores the collection account name to identify the application program that collects data related to the theme with the theme ID.

[0055] Figure 6 shows an example of an ETL table 204. The ETL table 204 shown in Figure 6 has fields 240 to 240.

[0056] Field 240 stores the factory name. Field 241 stores the theme name. Field 242 stores the theme ID. Field 243 stores the ETL name to identify the application program that processes the data related to the theme with the theme ID.

[0057] Figure 7 shows an example of the assumed effect table 205. The assumed effect table 205 shown in Figure 7 has fields 250 to 269.

[0058] Field 250 stores the factory name. Field 251 stores the theme name. Field 252 stores the theme ID. Field 253 stores the aggregation period for which the effectiveness evaluation values ​​of the data related to the theme with the theme ID were aggregated. In the example in Figure 7, the effectiveness evaluation values ​​are aggregated on a monthly basis, but the aggregation period is not limited to one month and can be set as appropriate by the system operator or others.

[0059] Field 254 stores the total primary DL reference frequency [times / month], which is the number of times a reference process was performed to refer to the target primary data, which is the primary data related to the theme ID, during the aggregation period. Field 255 stores the average primary DL reference range [number of data points / time], which is the average number of references, which is the number of primary data points referenced in each reference process. Field 256 stores the primary expected effect, which is the effect evaluation value of the target primary data.

[0060] The primary assumed effect is a value calculated based on the number of references and the average primary DL reference range. In this embodiment, it is the value obtained by multiplying the total primary DL reference frequency, which is the average number of references, by the average primary DL reference range. However, the primary assumed effect is not limited to this example, and may be calculated using other statistical values ​​such as the median, maximum, minimum, or sum of references, instead of the total primary DL reference frequency, or in addition to the total primary DL reference frequency.

[0061] Field 257 stores the total secondary DL reference frequency [times / month], which is the number of times a reference process was performed that referenced the target secondary data, which is secondary data related to the theme ID, during the aggregation period. Field 258 stores the average secondary DL reference range [number of data points / time], which is the average number of references, which is the number of secondary data points referenced in each reference process. Field 259 stores the secondary assumed effect, which is the effect evaluation value of the target secondary data. The secondary assumed effect is a value calculated based on the number of references and the secondary DL average reference range, and in this embodiment, it is the value obtained by multiplying the total secondary DL reference frequency, which is the average number of references, by the secondary DL average reference range. However, the secondary assumed effect is not limited to this example, and for example, statistical values ​​such as the median, maximum, minimum, or sum of the number of references may be used instead of, or in addition to, the total secondary DL reference frequency to calculate it.

[0062] Figure 8 shows an example of the assumed cost table 206. The assumed cost table 206 shown in Figure 8 has fields 260 to 269.

[0063] Field 260 stores the factory name. Field 261 stores the theme name. Field 262 stores the theme ID. Field 263 stores the aggregation period for which the cost evaluation values ​​of the data related to the theme with the theme ID were aggregated. Note that the aggregation period for the cost evaluation values ​​is the same as the aggregation period for the effectiveness evaluation values.

[0064] Field 264 stores the total collection frequency [times / month], which is the number of times the target primary data was collected during the aggregation period. Field 265 stores the average collection time [s], which is the average collection time taken for each collection process during the aggregation period. Field 266 stores the estimated collection cost, which is the cost evaluation value of the target primary data during the aggregation period.

[0065] The estimated collection cost is a value calculated based on the total collection frequency and collection time. In this embodiment, it is the total collection frequency multiplied by the average collection time, which is the average value of the collection time. However, the estimated collection cost is not limited to this example; for example, it may be calculated using other statistical values ​​such as the median, maximum, minimum, or sum of the collection times, instead of, or in addition to, the average collection time.

[0066] Field 267 stores the total processing frequency [times / month], which is the number of processing steps performed to generate the target secondary data during the aggregation period. Field 268 stores the average processing time [s], which is the average processing time taken for each processing step during the aggregation period. Field 269 stores the estimated processing cost, which is the cost evaluation value of the target secondary data during the aggregation period.

[0067] The estimated processing cost is a value calculated based on the total processing frequency and processing time. In this embodiment, it is the value obtained by multiplying the total collection frequency by the average processing time, which is the average processing time. However, the estimated processing cost is not limited to this example, and may be calculated using other statistical values ​​such as the median, maximum, minimum, or sum of processing times, instead of or in addition to the average processing time.

[0068] Figure 9 shows an example of a cost-benefit table 207. The cost-benefit table 207 shown in Figure 9 has fields 270 to 275.

[0069] Field 270 stores the factory name. Field 271 stores the theme name. Field 272 stores the theme ID. Field 273 stores the aggregation period.

[0070] Field 274 stores the primary download cost-effectiveness value, which is the cost-effectiveness value for the target primary data related to the theme ID. The primary download cost-effectiveness is a comparison value obtained by comparing the assumed primary effect with the assumed collection cost, and in this embodiment, it is the ratio of the assumed primary effect to the assumed collection cost. In other words, the primary download cost-effectiveness is (total collection frequency - average collection time) / (total primary download reference frequency - average primary download reference range).

[0071] Field 275 stores the cost-effectiveness value of secondary DL for the target secondary data related to the theme ID. The cost-effectiveness value of secondary DL is a comparison value obtained by comparing the assumed secondary effect with the assumed processing cost, and in this embodiment, it is the ratio of the assumed secondary effect to the assumed processing cost. In other words, the cost-effectiveness value of secondary DL is (total processing frequency - average processing time) / (total secondary DL reference frequency - average secondary DL reference range).

[0072] Figure 10 is a flowchart illustrating an example of the account registration process for registering account information.

[0073] In the account registration process, first, the input device 80 receives account information from the system operator of the information collection system 1 and transmits that account information to the data analysis device 60 (step S101).

[0074] The account information receiving unit 611 of the account information registration unit 61 of the data analysis device 60 receives account information. The account information storage unit 612 updates the application account table 201, DB reference account table 202, collection account table 203, and ETL table 204 based on that account information (step S102), and then terminates the process.

[0075] Figure 11 is a flowchart illustrating an example of the data collection process performed by the data acquisition device 10.

[0076] In the data acquisition process, first, the data acquisition information management unit 12 of the data acquisition device 10 acquires data from the target device at predetermined timings (for example, periodically) (step S201). The data acquisition information management unit 12 stores the acquired data as primary data in the data storage device 30 (step S202). Then, the data acquisition information management unit 12 generates a data acquisition log related to the acquired data and adds it to the data acquisition information 11 (step S203), and terminates the process.

[0077] Figure 12 is a flowchart illustrating an example of processing performed by the data processing device 20. The processing is, for example, batch processing and is executed at predetermined timings.

[0078] In the processing step, first, the data processing information management unit 22 of the data processing device 20 extracts the primary data to be processed from the data storage device 30 (step S301). The data processing information management unit 22 processes the extracted primary data to generate secondary data (step S302). The data processing information management unit 22 stores the secondary data in the data storage device 40 (step S303). The data processing information management unit 22 generates a data processing log related to the processing step and adds it to the data processing information 21 (step S304), and then terminates the process.

[0079] Figure 13 is a flowchart illustrating an example of the reference process performed by the data reference device 50.

[0080] In the reference process, first, the data reference information management unit 52 of the data reference device 50 generates a query for the data to be referenced and sends it to at least one of the data storage devices 30 and 40 when it is time to reference the data (step S401). The destination of the query is determined according to whether the data to be referenced is primary data or secondary data. Here, we assume that the query was sent to the data storage device 30.

[0081] The query execution information management unit 32 of the data storage device 30 receives a query from the data reference device 50 and reads the data corresponding to that query (step S402). Then, the query execution information management unit 32 sends the read data to the data reference device 50 and generates a query log corresponding to that query and adds it to the query execution information 31 (step S403). Even if the query is sent to the data storage device 40, the query execution information management unit 42 of the data storage device 40 performs the same processing.

[0082] When the data reference information management unit 52 of the data reference device 50 receives data, it performs a predetermined process that references that data (step S404). Then, when the predetermined process is completed (step S405), the data reference information management unit 52 adds the application log of the reference process to the data reference information 51 (step S406) and terminates the process.

[0083] Figure 14 is a flowchart illustrating an example of the effect calculation process performed by the assumed effect calculation unit 62 of the data analysis device 60. The effect calculation process is, for example, a batch process and is executed at predetermined timings.

[0084] In the effect calculation process, first, the data acquisition unit 621 of the assumed effect calculation unit 62 acquires query execution information 31, 41 and data reference information 51 from the data storage devices 30, 40 and the data reference device 50 (step S501).

[0085] The utilization effect calculation unit 622 analyzes the accumulated data for each theme based on the query execution information 31, 41 and data reference information 51, and calculates the reference frequency (total reference frequency for primary downloads and total reference frequency for secondary downloads) and the reference range (average reference range for primary downloads and average reference range for secondary downloads) for each theme (step S502). Then, the utilization effect calculation unit 622 calculates the expected effect (primary expected effect and secondary expected effect) for each theme based on the calculated reference frequency and reference range (step S503).

[0086] The utilization effect storage unit 623 generates and saves an expected effect table 205 showing the expected effects for each theme (step S504), and then terminates the process.

[0087] Figure 15 is a flowchart illustrating an example of the cost calculation process performed by the estimated cost calculation unit 63. The cost calculation process is, for example, a batch process and is executed at predetermined timings.

[0088] In the effect calculation process, the data acquisition unit 631 of the estimated cost calculation unit 63 acquires data acquisition information 11 and data processing information 21 from the data acquisition device 10 and the data processing device 20 (step S601).

[0089] The cost calculation unit 632 analyzes the accumulated data by theme based on the data collection information 11 and data processing information 21 acquired by the data acquisition unit 631, and calculates the total collection frequency, average collection time, total processing frequency, and average processing time for each theme (step S602). The cost calculation unit 632 then calculates the estimated collection cost and estimated processing cost for each theme based on the total collection frequency, average collection time, total processing frequency, and average processing time (step S603).

[0090] The cost storage unit 633 generates and saves an estimated cost table 206 showing the estimated cost for each theme (step S604), and then terminates the process.

[0091] Figure 16 is a flowchart illustrating an example of the visualization process performed by the cost-effectiveness visualization unit 64. The visualization process is, for example, a batch process and is executed at predetermined intervals.

[0092] In the visualization process, first, the cost-benefit calculation unit 641 of the cost-benefit visualization unit 64 obtains the application account table 201, the DB reference account table 202, the collection account table 203, the ETL table 204, the expected effect table 205, and the expected cost table 206 (step S701).

[0093] The cost-effectiveness calculation unit 641 calculates a cost-effectiveness value for each theme based on the information obtained in step S701, evaluating the cost-effectiveness of data collection related to that theme (step S702). In this embodiment, the cost-effectiveness value includes the cost-effectiveness of primary DL for primary data and the cost-effectiveness of secondary DL for secondary data. The cost-effectiveness of primary DL is a comparison value obtained by comparing the assumed primary effect with the assumed collection cost (more specifically, the ratio of the assumed primary effect to the assumed collection cost), and the cost-effectiveness of secondary DL is a comparison value obtained by comparing the assumed secondary effect with the assumed processing cost (more specifically, the ratio of the assumed secondary effect to the assumed processing cost).

[0094] The cost-effectiveness storage unit 642 generates and stores a cost-effectiveness table 207 showing the cost-effectiveness calculated by the cost-effectiveness calculation unit 641 (step S703).

[0095] Subsequently, the display information generation unit 643 generates display information based on the cost-benefit table 207 (step S704). At this time, the display information generation unit 643 may use the application account table 201, the DB reference account table 202, the collection account table 203, and the ETL table 204 as needed.

[0096] The display information generation unit 643 displays the generated display information (step S705) and then terminates the process.

[0097] In the embodiment described above, the cost-effectiveness value included both the primary DL cost-effectiveness and the secondary DL cost-effectiveness, but it may also include only one of them. Alternatively, the cost-effectiveness value may be a value calculated based on the primary DL cost-effectiveness and the secondary DL cost-effectiveness, such as the sum of the primary DL cost-effectiveness and the secondary DL cost-effectiveness.

[0098] As described above, according to this embodiment, the estimated cost calculation unit 63 calculates a cost evaluation value for each data item, based on log information from the data storage process, which evaluates the cost incurred in storing the data. The estimated effect calculation unit 62 calculates an effect evaluation value for each theme, based on log information from the data reference process, which evaluates the value of the data. The cost-benefit visualization unit 64 calculates a cost-benefit value for each theme, which is a comparison value obtained by comparing the cost evaluation value with the effect evaluation value. Therefore, since the cost incurred in storing the data and the value of the data are compared for each theme, it becomes possible to identify themes with low cost-benefit ratios for business improvement, and to efficiently collect data related to multiple themes.

[0099] Furthermore, in this embodiment, the storage process includes a collection process that collects and stores primary data, and a processing process that processes the primary data and stores it as secondary data. Therefore, it becomes possible to consider not only the cost of collecting the data but also the cost of processing that data, making it possible to more appropriately grasp the cost-effectiveness for each theme.

[0100] Furthermore, in this embodiment, the cost-effectiveness visualization unit 64 calculates cost-effectiveness values ​​for both primary and secondary data. Therefore, it becomes possible to more appropriately grasp the cost-effectiveness of each theme.

[0101] Furthermore, in this embodiment, the estimated cost calculation unit 63 calculates the total collection frequency and the collection time taken to collect primary data based on the log information of the storage process, and calculates the estimated collection cost, which is the cost evaluation value of primary data, based on the collection frequency and the collection time. In this case, it becomes possible to evaluate the cost related to primary data more appropriately.

[0102] Furthermore, in this embodiment, the estimated cost calculation unit 63 calculates the total processing frequency for generating secondary data and the processing time required for generating secondary data based on the log information of the storage process, and calculates the estimated processing cost, which is the cost evaluation value for secondary data, based on the total processing frequency and processing time. In this case, it becomes possible to evaluate the costs related to the two data more appropriately.

[0103] Furthermore, in this embodiment, the assumed effect calculation unit 62 calculates the number of times a reference process was executed and the number of references, which is the number of data referenced in each reference process, based on the log information of the reference process, and calculates an effect evaluation value based on the reference frequency and the number of references. In this case, it becomes possible to appropriately evaluate the value of the data.

[0104] Furthermore, in this embodiment, the cost-effectiveness visualization unit 64 calculates the cost-effectiveness value as the ratio of the effectiveness evaluation value to the cost evaluation value. This makes it possible to evaluate the cost-effectiveness of the data more appropriately.

[0105] The embodiments of the Disclosure described above are illustrative for illustrative purposes and are not intended to limit the scope of the Disclosure to those embodiments only. Those skilled in the art can implement the Disclosure in various other forms without departing from the scope of the Disclosure. [Explanation of symbols]

[0106] 1: Information gathering system 10: Data collection device 11: Data collection information 12: Data collection information management unit 20: Data processing device 21: Data processing information 22: Data processing information management unit 30,40: Data storage device 31,41: Query execution information 32,42: Query execution information management unit 50: Data reference device 51: Data reference information 52: Data reference information management unit 60: Data analysis device 61: Account information registration unit 62: Estimated effect calculation unit 63: Estimated cost calculation unit 64: Cost-effectiveness visualization unit 70: Network 80 Input device 611: Account information reception unit 612: Account information storage unit 621: Data acquisition unit 622: Usage effect calculation unit 623: Usage effect storage unit 631: Data acquisition unit 632: Usage cost calculation unit 633: Usage cost storage unit 641: Cost-effectiveness calculation unit 642: Cost-effectiveness storage unit 643: Display information generation unit

Claims

1. An information gathering system that stores and references data on multiple themes, A cost calculation unit calculates a cost evaluation value for each theme, based on log information of the data storage process that stores the aforementioned data, which evaluates the cost incurred in storing the aforementioned data. Based on log information of the reference process that references the aforementioned data, an effect calculation unit calculates an effect evaluation value for each theme that evaluates the value of the aforementioned data, An information gathering system having a comparison unit that calculates a comparison value by comparing the cost evaluation value and the effect evaluation value for each of the aforementioned themes.

2. The information collection system according to claim 1, wherein the storage process includes a collection process for collecting and storing primary data as data, and a processing process for processing the primary data and storing it as secondary data.

3. The cost calculation unit calculates the cost evaluation value for each of the primary data and the secondary data, The effect calculation unit calculates the effect evaluation value for each of the primary data and the secondary data, The information gathering system according to claim 2, wherein the comparison unit calculates the comparison value for each of the primary data and the secondary data.

4. The information collection system according to claim 2, wherein the cost calculation unit calculates the number of times the primary data was collected and the collection time taken to collect the primary data based on the log information of the storage process, and calculates the cost evaluation value of the primary data based on the number of times the data was collected and the collection time.

5. The information collection system according to claim 2, wherein the cost calculation unit calculates the number of processing steps that generated the secondary data and the processing time taken to generate the secondary data based on the log information of the storage process, and calculates the cost evaluation value of the secondary data based on the number of processing steps and the processing time.

6. The information collection system according to claim 1, wherein the effect calculation unit calculates the number of times the reference process was executed and the number of references, which is the number of data referenced in each reference process, based on the log information of the reference process, and calculates the effect evaluation value based on the number of references and the number of references.

7. The information gathering system according to claim 1, wherein the comparison unit calculates the ratio of the effect evaluation value to the cost evaluation value as the comparison value.

8. An information evaluation method performed by an information gathering system that accumulates and references data on multiple themes, The information collection system calculates a cost evaluation value for each theme, based on the log information of the data storage process, which evaluates the cost incurred in storing the data. The information collection system calculates an effect evaluation value for each theme, based on the log information of the reference process that references the data, An information evaluation method comprising the information gathering system calculating a comparative value for each theme by comparing the cost evaluation value and the effectiveness evaluation value.

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