Judgment device, judgment system, judgment method, and program

Abstract

To determine whether to compensate for missing data.SOLUTION: A determination unit 10 includes: a query unit 101 which acquires data from a database using a query; a missing determination unit 102 which determines whether data acquired by the query unit 101 is missing or not; an analysis unit 103 which analyzes data determined to be missing by the missing determination unit 102; and a compensation determination unit 104 which determines whether to compensate for missing of data determined to be missing, on the basis of a result analyzed by the analysis unit 103.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a determination device, a determination system, a determination method, and a program . [Background Art]

[0002] Data related to devices is collected from devices, and the collected data is utilized. The collected data is stored, for example, in a database server, and the collected data can be utilized by a user acquiring and analyzing the data stored in the database server through a query.

[0003] Here, missing data may occur in the collected data due to factors such as temporary failure of a device or poor communication. For example, phenomena may occur such as temperature-related information being missing only for a certain time period, or all data being missing for another certain time period.

[0004] In order to address such a problem, it is conceivable to interpolate missing data by some method. For example, Patent Literature 1 discloses an information processing device that interpolates missing data by using data from other devices at the same time when data of a certain device is missing at that time. [Prior Art Literature] [Patent Literature]

[0005] [Patent Literature 1] Japanese Unexamined Patent Publication No. 2021-114064 [Summary of Invention] [Problem to be Solved by the Invention]

[0006] The information processing device described in Patent Literature 1 interpolates missing data by using data from other devices regardless of the tendency or meaning of the data, so there is a problem that interpolation may be performed even in cases where interpolation is not desirable.

[0007] For example, regarding log data for air conditioning equipment, the midday hours of midsummer are the time when the impact on air conditioning equipment is greatest. Therefore, if there are gaps in the data during this time, it is not advisable to interpolate and utilize this data. This is because particularly large changes in temperature and room temperature can occur during the aforementioned time period, making it difficult to interpolate the data with minimal error.

[0008] Therefore, there is a need for technology to determine whether or not missing data should be interpolated.

[0009] In view of the above circumstances, the purpose of this disclosure is to provide a determination device, etc., that can determine whether or not data loss should be interpolated. [Means for solving the problem]

[0010] To achieve the above objectives, the determination device relating to this disclosure is A query method for retrieving data from a database using queries, A missing data determination means for determining whether or not there are missing data in the data obtained by the query means, Data that has been determined to be missing by the missing data detection means, For columns with missing data An analysis means that performs analysis based on a condition table that defines the reference column and the interpolation conditions, An interpolation feasibility determination means that determines whether or not to interpolate the missing data determined to be missing based on the analysis results by the analysis means, It is equipped with. [Effects of the Invention]

[0011] According to this disclosure, it is possible to determine whether or not missing data should be interpolated. [Brief explanation of the drawing]

[0012] [Figure 1] This figure shows the overall configuration of the determination system according to Embodiment 1 of this disclosure. [Figure 2]Figure showing an example of data acquired by the determination apparatus according to Embodiment 1 of the present disclosure [Figure 3] Figure showing an example of data interpolation by the determination apparatus according to Embodiment 1 of the present disclosure [Figure 4] Figure showing an example of data acquired by the determination apparatus according to Embodiment 1 of the present disclosure [Figure 5] Figure showing an example of data interpolation by the determination apparatus according to Embodiment 1 of the present disclosure [Figure 6] Figure showing an example of a condition table used in interpolation availability determination by the determination apparatus according to Embodiment 1 of the present disclosure [Figure 7] Figure showing an example of the hardware configuration of the determination apparatus according to Embodiment 1 of the present disclosure [Figure 8] Flowchart showing an example of the operation of interpolation availability determination by the determination apparatus according to Embodiment 1 of the present disclosure [Figure 9] Figure showing the overall configuration of a determination system according to Embodiment 2 of the present disclosure [Figure 10] Figure showing the overall configuration of a determination system according to Embodiment 3 of the present disclosure [Figure 11] Figure showing the overall configuration of a determination system according to a modified example of the present disclosure [Mode for Carrying Out the Invention]

[0013] Hereinafter, a determination system according to an embodiment of the present disclosure will be described with reference to the drawings. In each drawing, the same or equivalent portions are denoted by the same reference signs.

[0014] (Embodiment 1) A determination system 1 according to Embodiment 1 will be described with reference to FIG. 1. The determination system 1 includes a determination apparatus 10 and a database server 20. The determination apparatus 10 and the database server 20 are communicably connected to each other. As will be described in detail later, the determination system 1 is a system in which the determination apparatus 10 acquires data from the database server 20, and determines whether or not the deficiency should be interpolated when the acquired data has a deficiency. The determination system 1 is an example of the determination system according to the present disclosure.

[0015] The database server 20 is a database server that stores, as a database, operation data collected from devices via the Internet, for example. The operation data stored in the database server 20 may be, for example, operation data of an air conditioning device, or may be operation data of an electric water heater.

[0016] The determination device 10 acquires data from the database server 20 by query, and when the acquired data has a missing value, determines whether or not the missing value should be interpolated. Details of this determination will be described later. The determination device 10 is, for example, a personal computer. The determination device 10 may acquire data from the database server 20 at fixed intervals by, for example, preset periodic batch processing, or may acquire data from the database server 20 in response to a user operation. In any case, when the acquired data has a missing value, the determination device 10 determines whether or not the missing value should be interpolated. The determination device 10 is an example of the determination device according to the present disclosure.

[0017] Furthermore, as will be described in detail later, when the determination device 10 determines that the missing value should be interpolated, it interpolates the missing data and then acquires data again by query; when it determines that the missing value should not be interpolated, it excludes all data for the period in which the data missing occurred and acquires data again by query.

[0018] Next, an example of data and an example of data interpolation when the data stored in the database server 20 is operation data of an air conditioning device will be described with reference to FIG. 2 and FIG. 3.

[0019] The data shown in FIG. 2 is room temperature data detected by the air conditioning device over a certain period. However, as also shown in FIG. 2, there are missing data.

[0020] Interpolating the data shown in Figure 2 results in the data shown in Figure 3. Figure 3 shows that the data at 01:20:00 and 01:40:00 can be interpolated from the data before and after the missing data, while the data at 11:20:00 and 11:40:00 should not be interpolated. The former is interpolated because the missing time period is late at night, a time when temperatures generally do not change much. On the other hand, the latter should not be interpolated because the missing time period is near noon, a time when temperatures generally change easily. It is preferable not to interpolate such easily changing data. As a data interpolation method, for example, Lagrangian interpolation can be used. In Figure 3, interpolation is performed based on data in the same column.

[0021] As another example, referring to Figures 4 and 5, we will explain an example of data and an example of data interpolation when the data stored in the database server 20 is operating data for an electric water heater.

[0022] The data shown in Figure 4 concerns the remaining amount of hot water in an electric water heater, the cumulative amount of water used for filling the bathtub, whether or not the bathtub is filling, whether or not the shower is in use, and whether or not hot water is being supplied to the kitchen, over a certain period of time. Figure 5 shows how each missing value is interpolated or not interpolated. The main difference between Figure 5 and Figure 3 is that in Figure 3, the possibility of interpolation is determined based on the data in the same column as the missing data, whereas in Figure 5, the possibility of interpolation is determined not only based on the data in the same column as the missing data, but also on data different from the missing data.

[0023] The interpolation criteria in the case shown in Figure 5 are more complex than those in the case shown in Figure 3. When the criteria become complex, such as when data from multiple columns are involved in determining whether interpolation is possible, the decision on whether interpolation is possible is made based on a condition table, such as the one shown in Figure 6. In Figure 6, for example, no "condition" is set for column A, so interpolation will not be performed for column A. Also, for example, a "condition" is set for column B, and "single interpolation" is set to True, so when this "condition" is met, the missing data will be interpolated based on the other data in column B. Also, for example, columns C and D have "conditions" set, "interpolation from other columns" is set to True, and a "reference column name" is set, so when the "condition" is met, the missing data will be interpolated based on the data in the "reference column name".

[0024] In this way, regardless of the type of data stored in the database server 20, it becomes possible to determine whether or not data loss can be interpolated by appropriately setting judgment conditions, for example, as shown in Figure 6.

[0025] Referring again to Figure 1, the functional configuration of the determination device 10 will be explained. The determination device 10 comprises a communication unit 100, a query unit 101, a missing data determination unit 102, an analysis unit 103, and an interpolation feasibility determination unit 104.

[0026] The communication unit 100 communicates with the database server 20. The communication unit 100 is implemented, for example, by a network interface.

[0027] The query unit 101 communicates with the database server 20 via the communication unit 100, queries the database server 20, and retrieves data from the database server 20. The query unit 101 is an example of a query means related to this disclosure. In addition to the above, the query unit 101 also has functions related to the interpolation feasibility determination unit 104, which will be described later.

[0028] The missing data detection unit 102 determines whether or not there are missing data in the data acquired by the query unit 101. For example, if the data acquired by the query unit 101 is as shown in Figure 2, there are missing data, so the missing data detection unit 102 determines that there are missing data in the data acquired by the query unit 101. On the other hand, for example, if the data acquired by the query unit 101 is only the data for the period from 02:00:00 to 02:40:00 of the data shown in Figure 2, there are no missing data in this range, so the missing data detection unit 102 determines that there are no missing data in the data acquired by the query unit 101. The missing data detection unit 102 is an example of a missing data detection means according to this disclosure.

[0029] The analysis unit 103 analyzes the data that the missing data detection unit 102 has determined to have missing data. In particular, the analysis unit 103 analyzes the relationship between columns with missing data and columns without missing data. The analysis unit 103 analyzes the relationship between columns with missing data and columns without missing data based on, for example, the condition table shown in Figure 6. This analysis result is used by the interpolation feasibility determination unit 104, described later, to determine whether or not interpolation should be performed. The analysis unit 103 is an example of the analysis means according to this disclosure.

[0030] The interpolation feasibility determination unit 104 determines, based on the analysis results from the analysis unit 103, whether or not to interpolate the missing data determined by the missing data determination unit 102. The interpolation feasibility determination unit 104 is an example of the interpolation feasibility determination means according to this disclosure.

[0031] The functions of the query unit 101 related to the interpolation feasibility determination unit 104 described above will now be explained. When the interpolation feasibility determination unit 104, described later, determines that interpolation should be performed, the query unit 101 interpolates the missing data and retrieves the data again by querying. When the interpolation feasibility determination unit 104, described later, determines that interpolation should not be performed, the query unit 101 excludes all data from the period in which data is missing and retrieves the data again by querying. For example, consider the case where the data is as shown in Figure 5. The query unit 101 interpolates the data for 21:05:00 and 21:15:00 and retrieves the data again by querying. For the data at 21:25:00, the query unit 101 excludes all data for that date and time, and all data for the corresponding row for that date and time, and retrieves the data again by querying.

[0032] Next, an example of the hardware configuration of the determination device 10 will be described with reference to Figure 7. The determination device 10 shown in Figure 7 is implemented using a computer such as a personal computer or a microcontroller.

[0033] The determination device 10 comprises a processor 1001, a memory 1002, an interface 1003, and a secondary storage device 1004, all of which are connected to each other via a bus 1000.

[0034] The processor 1001 is, for example, a CPU (Central Processing Unit). The processor 1001 reads the operation program stored in the secondary storage device 1004 into the memory 1002 and executes it, thereby realizing each function of the determination device 10.

[0035] Memory 1002 is a main memory device, for example, composed of RAM (Random Access Memory). Memory 1002 stores the operational program read by the processor 1001 from the secondary memory device 1004. Memory 1002 also functions as work memory when the processor 1001 executes the operational program.

[0036] Interface 1003 is an I / O (Input / Output) interface such as a serial port, USB (Universal Serial Bus) port, or network interface. Interface 1003 enables the functionality of the communication unit 100.

[0037] The secondary storage device 1004 is, for example, flash memory, an HDD (Hard Disk Drive), or an SSD (Solid State Drive). The secondary storage device 1004 stores the operational programs that the processor 1001 executes.

[0038] Next, an example of the operation of the determination device 10 in determining whether interpolation is possible will be explained with reference to Figure 8. The operation shown in Figure 8 is performed, for example, when the batch processing described above is started. Alternatively, it is performed when the determination device 10 attempts to retrieve data from the database server 20 due to user operation.

[0039] The query unit 101 of the determination device 10 retrieves data from the database server 20 based on the query (step S101).

[0040] The data loss determination unit 102 of the determination device 10 determines whether or not there are missing data in the data acquired in step S101 (step S102).

[0041] If there are no missing data (Step S102: No), the determination device 10 terminates its interpolation feasibility determination operation because there are no missing data to be interpolated in the first place.

[0042] If there are missing data (Step S102: Yes), the analysis unit 103 of the determination device 10 analyzes the data that was determined to be missing in Step S102 (Step S103).

[0043] The interpolation feasibility determination unit 104 of the determination device 10 determines whether or not data gaps should be interpolated based on the analysis results in step S103 (step S104).

[0044] When it is determined that missing data should be interpolated (Step S104: Yes), the query unit 101 interpolates the missing data and retrieves the data again by querying (Step S105). The determination device 10 then terminates its interpolation feasibility determination operation.

[0045] If it is determined that missing data should not be interpolated (step S104: No), the query unit 101 excludes all data from the period in which the data is missing and retrieves the data again by querying (step S106). The determination device 10 then terminates its interpolation feasibility determination operation.

[0046] After completing the interpolation feasibility determination operation, the determination device 10 performs operations for data utilization, for example. Examples of operations for data utilization include proposing appropriate energy-saving plans and providing monitoring services.

[0047] The determination system 1 according to Embodiment 1 has been described above. According to the determination system 1 according to Embodiment 1, it is possible to determine whether or not missing data should be interpolated. Therefore, the risk of performing undesirable interpolation can be reduced.

[0048] (Embodiment 2) The determination system 1 according to Embodiment 2 will be described with reference to Figure 9. The determination system 1 according to Embodiment 2 differs from Embodiment 1 in that the determination device 10 further comprises a registration unit 105, a metadata determination unit 106, and a storage unit 110. In addition, the function of the interpolation feasibility determination unit 104 is also slightly different.

[0049] The storage unit 110 stores metadata, which will be described later. The storage unit 110 is an example of a storage means related to this disclosure.

[0050] When the interpolation feasibility determination unit 104 determines that interpolation of missing data should not be performed, the registration unit 105 stores metadata related to the data in the storage unit 110. The metadata may include, for example, the data size of the data and a hash value that uniquely identifies the data. The registration unit 105 is an example of a registration means related to this disclosure.

[0051] The metadata determination unit 106 obtains metadata of the data acquired by the query unit 101 and determines whether the acquired metadata matches the metadata stored in the storage unit 110. Note that there may be multiple sets of metadata stored in the storage unit 110 corresponding to the data targeted for determination by the interpolation feasibility determination unit 104; in this case, it is sufficient to determine whether it matches any one of the multiple sets of metadata. The metadata determination unit 106 is an example of the metadata determination means related to this disclosure.

[0052] The interpolation feasibility determination unit 104 determines that data acquired by the query unit 101 that corresponds to metadata that the metadata determination unit 106 has determined to match metadata stored in the storage unit should not be interpolated. In other words, for data that has been previously determined not to be interpolated, the metadata can be used to determine again that interpolation should not be performed without having the analysis unit 103 analyze the data again.

[0053] The determination system 1 according to Embodiment 2 has been described above. According to the determination system 1 according to Embodiment 2, by utilizing metadata, data that has been determined not to be interpolated once can be determined again not to be interpolated without re-analyzing the data, thereby reducing the processing load on the determination device 10.

[0054] (Embodiment 3) The determination system 1 according to Embodiment 3 will be described with reference to Figure 10. The determination system 1 according to Embodiment 3 differs from Embodiment 1 in that the determination device 10 includes a learning unit 107 and a storage unit 110. In addition, the function of the interpolation feasibility determination unit 104 is also slightly different from that of Embodiment 1.

[0055] The memory unit 110 stores the model generated by the learning unit 107, which will be described later.

[0056] The learning unit 107 learns the relationship between columns with missing data and columns without missing data using machine learning for data that the missing data detection unit 102 has determined to have missing data, generates a model for interpolating missing data, and stores it in the storage unit 110. Therefore, it becomes possible to determine whether interpolation is possible without having to pre-set a condition table such as the one shown in Figure 6. The learning unit 107 is an example of a learning means related to this disclosure.

[0057] The interpolation feasibility determination unit 104 determines whether or not interpolation should be performed based on the model stored in the storage unit 110 and the analysis results of the analysis unit 103.

[0058] The judgment system 1 according to Embodiment 3 has been described above. According to the judgment system 1 according to Embodiment 3, a model is generated by learning the relationship between columns with missing data and columns without missing data, and a determination of whether interpolation is possible is made based on this model. Therefore, the effort required to set up a condition table in advance can be reduced. Furthermore, as learning progresses, an improvement in the accuracy of the determination can be expected.

[0059] Although Embodiment 3 was described above as a modification of Embodiment 1, Embodiment 2 may also be modified in a similar manner.

[0060] (modified version) The determination system 1 according to a modified version of the present disclosure will be explained with reference to Figure 11. The determination system 1 according to a modified version of the present disclosure differs from Embodiment 1 in that it further includes a display device 30 and the determination device 10 includes a display control unit 108. The display control unit 108 is connected to the display device 30.

[0061] The display device 30 is, for example, a display. The display device 30 displays an image under the control of the display control unit 108. The display device 30 is an example of a display means according to this disclosure.

[0062] The display control unit 108 controls the display device 30 and displays the image described below. When the interpolation feasibility determination unit 104 determines that interpolation should be performed, the display control unit 108 displays an image on the display device 30 that allows the user to choose between data with the missing data interpolated and data with all data from the period in which there is missing data excluded. The user can obtain the desired data by, for example, checking this image and operating an input device (not shown) to select the desired data.

[0063] In the embodiments described above, the determination device 10 was described as a single device. However, each functional part of the determination device 10 may be distributed across multiple devices. For example, the query unit 101 may be provided in a dedicated device for querying, and the functions of the missing data determination unit 102, the analysis unit 103, and the interpolation feasibility determination unit 104 may be provided in separate devices, with these devices communicating via a network.

[0064] In the hardware configuration shown in Figure 7, the determination device 10 is equipped with a secondary storage device 1004. However, the configuration is not limited to this; the secondary storage device 1004 may be located outside the determination device 10, and the determination device 10 and the secondary storage device 1004 may be connected via an interface 1003. In this configuration, removable media such as USB flash drives and memory cards can also be used as the secondary storage device 1004.

[0065] Alternatively, instead of the hardware configuration shown in Figure 7, the determination device 10 may be configured using a dedicated circuit such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array). Furthermore, in the hardware configuration shown in Figure 7, some of the functions of the determination device 10 may be implemented, for example, by a dedicated circuit connected to interface 1003.

[0066] The various aspects of this disclosure are summarized below as an appendix.

[0067] (Note 1) A query method for retrieving data from a database using queries, A missing data determination means for determining whether or not there are missing data in the data obtained by the query means, An analysis means for analyzing data that has been determined to be missing by the missing data determination means, An interpolation feasibility determination means that determines whether or not to interpolate the missing data determined to be missing based on the analysis results by the analysis means, A determination device equipped with the following features. (Note 2) The analysis means analyzes the relationship between columns with missing data and columns without missing data for the data that has been determined to have missing data. The determination device described in Appendix 1. (Note 3) The system further includes a learning means for generating a model to interpolate missing data by learning the relationship between columns with missing data and columns without missing data, for the data that has been determined to have missing data. The interpolation feasibility determination means determines whether or not to interpolate the missing data based on the analysis results from the analysis means and the model generated by the learning means. The determination device described in Appendix 2. (Note 4) The query means further determines, when the interpolation feasibility determination means determines that the missing data should be interpolated, to interpolate the missing data and retrieve the data again by querying; and when the interpolation feasibility determination means determines that the missing data should not be interpolated, to exclude all data from the period in which the missing data occurred and retrieve the data again by querying. A determination device as described in any one of the appendices 1 to 3. (Note 5) When the interpolation feasibility determination means determines that the missing data determined to be missing should not be interpolated, a registration means registers metadata relating to the data in the storage means, A metadata determination means for determining whether the metadata of the data obtained by the query means matches the metadata registered in the storage means, Furthermore, When the metadata determination means determines that the metadata of the data obtained by the query means matches the metadata registered in the storage means, the interpolation feasibility determination means determines that the missing data should not be interpolated. A determination device as described in any one of the appendices 1 to 4. (Note 6) The display means further comprises a display control means for displaying an image, When the interpolation feasibility determination means determines that the missing data should be interpolated, the display control means displays an image on the display means allowing the user to select between the interpolated data and the data with all data from the period in which the data is missing. A determination device as described in any one of the appendices 1 to 5. (Note 7) A storage means for storing the database to be queried, A query means for obtaining data from the aforementioned database by query, A missing data determination means for determining whether or not there are missing data in the data obtained by the query means, An analysis means for analyzing data that has been determined to be missing by the missing data determination means, An interpolation feasibility determination means that determines whether or not to interpolate the missing data determined to be missing based on the analysis results by the analysis means, A judgment system equipped with the following features. (Note 8) Computers The query retrieves data from the database. Determine whether or not there are missing data in the data obtained by the query. We analyze the data that was determined to be missing, Based on the analysis results, it is determined whether or not to interpolate the missing data that has been determined to be missing. Judgment method. (Note 9) Computers, A query method for retrieving data from a database using queries. A missing data determination means for determining whether or not there are missing data in the data obtained by the query means. Analysis means for analyzing data that has been determined to be missing by the missing data determination means, Interpolation feasibility determination means that determines whether or not to interpolate the missing data determined to be missing based on the analysis results by the analysis means, A program that makes it function as such. [Explanation of Symbols]

[0068] 1 Judgment system, 10 Judgment device, 20 Database server, 30 Display device, 100 Communication unit, 101 Query unit, 102 Missing data determination unit, 103 Analysis unit, 104 Interpolation feasibility determination unit, 105 Registration unit, 106 Metadata determination unit, 107 Learning unit, 108 Display control unit, 110 Storage unit, 1000 Bus, 1001 Processor, 1002 Memory, 1003 Interface, 1004 Secondary storage device.

Claims

1. A query method for retrieving data from a database using queries, A missing data determination means for determining whether or not there are missing data in the data obtained by the query means, An analysis means that analyzes data determined to be missing by the missing data detection means based on a condition table that defines conditions for interpolating missing columns with reference columns, An interpolation feasibility determination means that determines whether or not to interpolate the missing data determined to be missing based on the analysis results by the analysis means, A determination device equipped with the following features.

2. The analysis means analyzes the relationship between columns with missing data and columns without missing data for the data that has been determined to have missing data. The determination device according to claim 1.

3. The system further includes a learning means for generating a model to interpolate missing data by learning the relationship between columns with missing data and columns without missing data, for the data that has been determined to have missing data. The interpolation feasibility determination means determines whether or not to interpolate the missing data based on the analysis results from the analysis means and the model generated by the learning means. The determination device according to claim 2.

4. The query means further determines, when the interpolation feasibility determination means determines that the missing data should be interpolated, to interpolate the missing data and retrieve the data again by querying; and when the interpolation feasibility determination means determines that the missing data should not be interpolated, to exclude all data from the period in which the missing data occurred and retrieve the data again by querying. The determination device according to claim 1.

5. When the interpolation feasibility determination means determines that the missing data determined to be missing should not be interpolated, a registration means registers metadata relating to the data in the storage means, A metadata determination means for determining whether the metadata of the data obtained by the query means matches the metadata registered in the storage means, Furthermore, When the metadata determination means determines that the metadata of the data obtained by the query means matches the metadata registered in the storage means, the interpolation feasibility determination means determines that the missing data should not be interpolated. The determination device according to claim 1.

6. The display means further comprises a display control means for displaying an image, When the interpolation feasibility determination means determines that the missing data should be interpolated, the display control means displays an image on the display means allowing the user to choose between the interpolated data and the data with all data from the period in which the missing data is excluded. The determination device according to claim 1.

7. A storage means for storing the database to be queried, A query means for obtaining data from the aforementioned database by query, A missing data determination means for determining whether or not there are missing data in the data obtained by the query means, An analysis means that analyzes data determined to be missing by the missing data detection means based on a condition table that defines conditions for interpolating missing columns with reference columns, An interpolation feasibility determination means that determines whether or not to interpolate the missing data determined to be missing based on the analysis results by the analysis means, A judgment system equipped with the following features.

8. Computers The query retrieves data from the database. Determine whether or not there are missing data in the data obtained by the query. Data that has been determined to have missing data is analyzed based on a condition table that defines the reference column and the conditions for interpolation for the missing column. Based on the analysis results, it is determined whether or not to interpolate the missing data that has been determined to be missing. Judgment method.

9. Computers, A query method for retrieving data from a database using queries. A missing data determination means for determining whether or not there are missing data in the data obtained by the query means. Analysis means for analyzing data determined to be missing by the missing data detection means based on a condition table that defines conditions for interpolating missing columns with reference columns, Interpolation feasibility determination means that determines whether or not to interpolate the missing data determined to be missing based on the analysis results by the analysis means, A program that makes it function as such.

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