Device and method

Abstract

A device (1) comprises: an acquisition unit (11) that acquires data comprising records including the number of people as an item; and a grouping unit (12) that integrates, among the data acquired by the acquisition unit (11), low-count records, which are records having the number of people less than a threshold value, on the basis of the distance between the records. The distance may relate to closeness of values of the same item of the records. The grouping unit (12) may perform integration such that the total number of people in the low-count records is equal to or greater than the threshold value. The grouping unit (12) may preferentially integrate low-count records which are closer in distance.

Description

Device and Method

[0001] One aspect of the present disclosure relates to an apparatus for integrating small-number records among data consisting of records including the number of people as an item.

[0002] Patent Document 1 below discloses a technique related to the transformation processing of data for protecting privacy.

[0003] Japanese Unexamined Patent Application Publication No. 2016-18379

[0004] As one of the data transformation processes for protecting privacy, there is a process of deleting records with a small number of relevant people. However, by deleting records with a small number of relevant people, the total number of data may decrease or the trend may deviate before and after the process. Therefore, it is desired to appropriately process records with a small number of relevant people.

[0005] The apparatus according to one aspect of the present disclosure includes an acquisition unit that acquires data consisting of records including the number of people as an item, and an integration unit that performs integration on small-number records, which are records with a number of people less than a threshold, among the data acquired by the acquisition unit, based on the distance between the records.

[0006] In such an aspect, integration is performed based on the distance between records for small-number records among records including the number of people as an item. That is, records with a small number of relevant people can be appropriately processed.

[0007] According to one aspect of the present disclosure, records with a small number of relevant people can be appropriately processed.

[0008] This figure shows an example of the functional configuration of the device according to the embodiment. This figure shows an example of a table of aggregated raw data results. This figure shows an example of a table obtained by deleting records with a small number of relevant people from the example table in Figure 2. This figure shows an example of a table obtained by applying differential privacy to the example table in Figure 2. This figure shows an example of a table of nominal scale / ratio scale converted data. This flowchart shows an example of a process performed by the device according to the embodiment. This figure shows an example of a table generated by step S4 in Figure 6. This figure shows an example of a table generated by step S8 in Figure 6. This figure shows an example of a table generated by step S9 in Figure 6. This flowchart shows another example of a process performed by the device according to the embodiment. This figure shows an example of the hardware configuration of the computer used in the device according to the embodiment.

[0009] The embodiments of this disclosure will be described in detail below with reference to the drawings. In the description of the drawings, the same elements will be denoted by the same reference numerals, and redundant descriptions will be omitted. Furthermore, the embodiments of this disclosure described below are specific examples of the present invention and are not limited to these embodiments unless otherwise stated to limit the present invention.

[0010] Figure 1 shows an example of the functional configuration of the apparatus 1 according to the embodiment. The apparatus 1 is a computer (server) that groups (integrates) records with a small number of people, which are records with a number of people less than a threshold (a predetermined threshold), based on the distance between records, from a data (table) consisting of records that include the number of people as an item (column). Details will be described later.

[0011] As shown in Figure 1, the device 1 is composed of a storage unit 10, an acquisition unit 11 (acquisition unit), a grouping unit 12 (integration unit), and an output unit 13.

[0012] Each functional block of Device 1 is intended to function within Device 1, but is not limited to this. For example, some of the functional blocks of Device 1 may function within a computer device separate from Device 1, connected to Device 1 via a network, while appropriately sending and receiving information with Device 1. Furthermore, some functional blocks of Device 1 may be omitted, multiple functional blocks may be integrated into a single functional block, or a single functional block may be broken down into multiple functional blocks.

[0013] Here, we will explain the background and challenges while referring to Figures 2 to 4.

[0014] As background, when an organization discloses or provides to a third party personal information collected through its services, etc., it is necessary to aggregate it as statistical data. This excludes cases where individual consent for the provision of personal information has been obtained. In this embodiment, as an example of the personal information described above, we will use the aggregated results (data) of raw data to explain.

[0015] Figure 2 shows an example of a table showing the aggregated results of raw data. Figure 2 shows an example of a table that aggregates the number of people who traveled from one location (departure point) to another location (destination point) at a certain time (departure time). The example table in Figure 2 includes the items departure point, destination, departure time, and number of people, and these items are associated with each other. The example table in Figure 2 contains eight records. For example, the first record shows that 30 people traveled from Higashi-Osaka to Kobe at 9:00 on October 1, 2024. In the example table in Figure 2, the sum of the numbers in each record (30 + 50 + 15 + 5 + 40 + 10 + 18 + 10) is 178 people.

[0016] In order to ensure privacy protection when aggregating raw data results into statistical data, there are cases where records of a small number of people are deleted or differential privacy is applied, but each of these has its own challenges.

[0017] First, let's explain the deletion of records with a small number of people. Deleting records with a small number of people means deleting records with a small number of relevant people. Figure 3 shows an example table from Figure 2 with records with a small number of relevant people deleted. More specifically, Figure 3 shows an example table from Figure 2 with records where the number of people is less than the threshold of 20 (the 3rd, 4th, and 6th to 8th records) deleted. In the example table shown in Figure 3, the total number of people in each record (30 + 50 + 40) is 120 people. In cases where records with a small number of people are deleted, for example, the example table shown in Figure 3 would be data provided to a third party.

[0018] One challenge with deleting records with a small number of people is that deleting records with a small number of relevant individuals can significantly reduce the overall amount of data or cause deviations in trends before and after processing. For example, if a large proportion of records have a small number of relevant individuals, the data trends may deviate. In the example table shown in Figure 3, data for 58 out of 178 people (30%) has been deleted. Trends such as the fact that many of these people travel from Higashi-Osaka to various parts of the Kansai region (Nara or Hiroshima) are lost.

[0019] Next, we will explain the application of differential privacy. Applying differential privacy involves adding noise to the entire data. By applying differential privacy, it is expected that the possibility of identifying individuals will be reduced without changing the data trends. Figure 4 shows an example table in which differential privacy has been applied to the example table in Figure 2. More specifically, Figure 4 shows an example table in which an integer (noise) within the range of "-6" to "+10" has been added to the number of people item in each record of the example table in Figure 2. For example, "+5" is added to the first record, "-5" to the second record, "-5" to the third record, "-3" to the fourth record, "+10" to the fifth record, "-6" to the sixth record, "-5" to the seventh record, and "+5" to the eighth record. In the example table shown in Figure 4, the total number of people in each record (35 + 45 + 10 + 2 + 50 + 4 + 13 + 15) is 174 people. In cases where differential privacy is applied, for example, the table shown in Figure 4 would be the data provided to a third party.

[0020] One challenge in applying differential privacy is that determining noise levels depends on data trends and experience. More specifically, while data trends are captured, the noise level, a parameter in this context, must be determined considering the nature of the data (e.g., compatibility with other data, distribution of individuals). Therefore, it is difficult for those without expertise in security and data analysis to appropriately set the noise level. Furthermore, even experts find it difficult to determine appropriate noise levels when sufficient knowledge about the data has not been accumulated.

[0021] The background and challenges have now been explained. Next, we will return to Figure 1 and explain each function of the device 1 shown in Figure 1.

[0022] The storage unit 10 stores arbitrary information used in calculations in the device 1, as well as the results of calculations in the device 1. The information stored in the storage unit 10 may be referenced as appropriate by each function of the device 1. The information stored in the storage unit 10 may be pre-set information, or it may be information acquired at any time by the acquisition unit 11 described later.

[0023] The acquisition unit 11 acquires arbitrary information. More specifically, the acquisition unit 11 may acquire arbitrary information used in calculations in the device 1 from other functional blocks of the device 1, including the storage unit 10, or it may acquire (receive) information from other devices such as external devices via a network. The acquisition unit 11 may acquire arbitrary information in advance prior to any calculations in the device 1, or it may acquire information when it becomes necessary, in accordance with the requests of each functional block of the device 1. The acquisition unit 11 may store the acquired information in the storage unit 10, or it may output it to other functional blocks of the device 1.

[0024] The acquisition unit 11 acquires data (data to be processed) consisting of records that include the number of people as an item. For example, the acquisition unit 11 may acquire the aggregated results of raw data as shown in Figure 2.

[0025] The acquisition unit 11 may further acquire scale information regarding the scale to be adopted as distance. Distance may also be the proximity of values ​​for the same item in each record (of the data to be processed). Distance may also be the proximity of records for each item (of the data to be processed). The scale may be the scale adopted as the distance for each column (of the data to be processed). For example, if the table example shown in Figure 2 is the data to be processed, the scale may be the physical distance [km] between the centroids of the departure or destination areas. In this case, the closer the physical distance between the centroids of each area, the closer the data is treated as "closer". Alternatively, the scale may be the maximum distance difference [km] between the departure or destination. Alternatively, the scale may be the difference in departure times [minutes]. In this case, the smaller the difference in departure times, the closer the data is treated as "closer". Alternatively, the scale may be the maximum time difference [minutes] between departure times. The scale information may also relate to multiple scales.

[0026] The acquisition unit 11 may further acquire condition information regarding the conditions for grouping. The condition information may also relate to distance conditions when grouping. The condition information may also relate to constraint conditions when grouping. For example, if the table example shown in Figure 2 is the data to be processed, the condition information may indicate that items that are more than 150 km apart in terms of departure point (or destination) will not be grouped (e.g., distance(departure point) ≤ 150 km), or that items whose departure time is greater than 0 (difference in departure time > 0) will not be grouped (i.e., do not group by departure time, aggregate only records with the same time) (e.g., distance(departure time) == 0 min.).

[0027] The acquisition unit 11 may acquire at least one of the data to be processed, scale information, and condition information.

[0028] The acquisition unit 11 may store the acquired information in the storage unit 10, or it may output it to other functional blocks of the device 1.

[0029] The grouping unit 12 groups the data to be processed (data) acquired by the acquisition unit 11, specifically the small-number records, which are records with fewer people than a threshold, based on the distance between records. The threshold may be a predetermined threshold set by the user of the device 1, or it may be one acquired by the acquisition unit 11. The "small-number records" targeted by the device 1 refer to records with fewer people than the threshold. Grouping means classifying based on common properties, etc., but it may also mean integrating after classification. Integration means combining two or more things into one. In this embodiment, the term "grouping" may be appropriately replaced with "integration".

[0030] The grouping unit 12 may perform grouping such that the total number of people in small records is equal to or greater than a threshold. In other words, the grouping unit 12 may perform grouping while appropriately calculating internally so that the total number of people in small records is equal to or greater than a threshold.

[0031] The grouping unit 12 may prioritize grouping records with smaller group sizes that are closer together. In other words, the grouping unit 12 may group records so that items that are "close" to each other are selected preferentially. For example, the grouping unit 12 may arrange other records with smaller group sizes in order of proximity for each record with smaller group sizes, and then group them one by one in order of proximity to other records with smaller group sizes.

[0032] The grouping unit 12 may further group based on the scale information acquired by the acquisition unit 11. For example, if the scale indicated by the scale information is the physical distance between the centroids of the departure or destination area, the distance may be a normalized value of the straight-line distance between the centroids of the area, and grouping may be performed based on that value. Alternatively, for example, if the scale indicated by the scale information is the difference in departure times, the distance may be a normalized value of the time difference, and grouping may be performed based on that value.

[0033] In this embodiment, place names (departure point and destination) and time (departure time) are used as examples of data items to be processed. However, time is a ratio scale (numerical data), while place names are nominal scale (category data), so distance cannot be calculated directly. Therefore, by converting the place name item to the latitude and longitude of the center of the place name (numerical data such as interval scale or ratio scale), it becomes possible to calculate the distance. Figure 5 shows an example of a table of nominal scale / ratio scale conversion data. In the example table shown in Figure 5, a region (nominal scale), a polygon representing the shape of the region on a map, and the centroid latitude and longitude (ratio scale) representing the centroid of the polygon are associated. The grouping unit 12 may calculate the distance by appropriately referring to the example table shown in Figure 5.

[0034] The grouping unit 12 may perform further grouping based on the condition information acquired by the acquisition unit 11. For example, the grouping unit 12 may perform grouping that satisfies the conditions indicated by the condition information.

[0035] The grouping unit 12 may delete records from the data after grouping that have fewer people than the (above) threshold. In other words, the grouping unit 12 may delete records that still have a small number of people even after grouping.

[0036] The grouping unit 12 may perform grouping based on an evaluation function that is based on the sum of the number of records with fewer people than the threshold (as described above) among the data after grouping. The grouping unit 12 may also perform grouping based on an evaluation function that is based on the sum of the distances in the grouping after grouping. More specifically, the grouping unit 12 may search for groups of records with a small number of people in order to minimize the following evaluation function f, and perform grouping based on the search results.

[0037] Here, N ｍａｘ This indicates the total number of people in the record. This indicates the total number of people after minority deletion. The first term of the evaluation function f is based on the sum of the number of people in the data that is less than the threshold (above) when grouping is performed. The first term of the evaluation function f is a term for reducing the number of people that will be deleted. The second term of the evaluation function f is based on the sum of the distances in the grouping when grouping is performed. The second term of the evaluation function f is a term for grouping records that are close to each other. The evaluation function f may search for targets for grouping while satisfying the distance constraints indicated by the condition information obtained by the acquisition unit 11.

[0038] A specific example of grouping by the grouping unit 12 will be explained in Figure 6 below. The grouping unit 12 may store the grouped data (grouped data) in the storage unit 10, or it may output it to other functional blocks of the device 1.

[0039] The output unit 13 may output (transmit) arbitrary information to other devices such as external devices via a network, or it may be displayed on the screen of device 1. For example, the output unit 13 may output grouped data stored by the storage unit 10 to other devices, or it may be displayed on device 1.

[0040] Next, an example of the process performed by device 1 will be explained with reference to Figure 6. Figure 6 is a flowchart showing an example of the process (grouping method, integration method) performed by device 1.

[0041] First, the acquisition unit 11 acquires the aggregated results of the raw data (step S1, acquisition step). An example of the aggregated results acquired in step S1 is the table example shown in Figure 2. Next, the device 1 (or acquisition unit 11) acquires a threshold X (for example, X = 20) of people for minority deletion (step S2). Next, the grouping unit 12 extracts records from the aggregated results acquired in step S1 that are equal to or greater than the threshold X, and saves them as a temporary file (step S3). An example of the extraction results in step S3 is the table example shown in Figure 3. Next, the grouping unit 12 extracts tables from the aggregated results acquired in step S1 where the number of records is less than the threshold X (step S4). An example of the extraction results in step S4 is the table example shown in Figure 7. Figure 7 is a diagram showing an example of a table generated by step S4 in Figure 6.

[0042] Next, the acquisition unit 11 acquires scale information, and based on the acquired scale information, the grouping unit 12 specifies the column names to be grouped (for example, departure point, destination, departure time, etc.) (step S5, acquisition step). Next, the grouping unit 12 acquires data to convert the nominal scale columns to a ratio scale, etc. (step S6). An example of the data acquired in S6 is the table example shown in Figure 5. Next, the acquisition unit 11 acquires the constraints for grouping (step S7, acquisition step).

[0043] Next, the grouping unit 12 performs grouping optimization on the extraction result in step S4 so as to minimize the evaluation function (based on the column name specified in step S5, the data acquired in step S6, and the constraint condition acquired in step S7). As a result of the optimization, records with less than X people are deleted and saved as a temporary file (step S8). As an example of the evaluation function, the above-described evaluation function f can be cited. As an example of the result of the optimization and deletion in step S8, the table example shown in FIG. 8 can be cited. FIG. 8 is a diagram showing a table example generated in step S8 of FIG. 6. The first record of the table example shown in FIG. 8 is obtained by grouping (integrating) the first and second records of the table example shown in FIG. 7. As a result of the grouping, the destination items are combined into "Wakayama and Nara", and the number of people items are added up to "20" (15 + 5). Similarly, the second record of the table example shown in FIG. 8 is obtained by grouping (integrating) the third and fourth records of the table example shown in FIG. 7. As a result of the grouping, the destination items are combined into "Hiroshima and Kurashiki", and the number of people items are added up to "28" (10 + 18). Note that the fifth record of the table example shown in FIG. 7 is deleted because it is a record with less than X people and is not reflected in the table example shown in FIG. 8.

[0044] Next, the grouping unit 12 combines the temporary file of S3 and the temporary file of S8 (step S9, integration step). As an example of the combination result in step S9, the table example shown in FIG. 9 can be cited. FIG. 9 is a diagram showing a table example generated in step S9 of FIG. 6. The first, second, and fourth records of the table example shown in FIG. 9 correspond to the first to third records shown in FIG. 3, respectively. The third and fifth records of the table example shown in FIG. 9 correspond to the first and second records shown in FIG. 8, respectively. In the table example shown in FIG. 9, the total number of people in each record (30 + 50 + 20 + 40 + 28) is 168 people.

[0045] In addition, in the process of optimal search in step S8, since the evaluation function becomes smaller when the distance is closer, items that are closer are likely to be grouped. Also, the granularity of the grouping that finally minimizes the evaluation function is determined.

[0046] The acquisition of various information in Steps S1, S2, S5, S6, and S7 is not limited to this order, and it may be acquired at any timing as long as it is before using the information. After Step S9, the output unit 13 may output the combination result (aggregated data) of Step S9 (as data provided by a third party).

[0047] Next, another example of the process executed by the apparatus 1 will be described while referring to FIG. 10. FIG. 10 is a flowchart showing another example of the process (grouping method, integration method) executed by the apparatus 1.

[0048] First, the acquisition unit 11 acquires data consisting of records including the number of people as an item (Step S10, acquisition step). Next, the grouping unit 12 performs grouping (integration) based on the distance between records for the small-number records, which are records with a number of people less than the threshold value, among the acquired data (Step S11, integration step).

[0049] Next, the operation and effect of the apparatus 1 according to the embodiment will be described.

[0050] According to the apparatus 1, there are provided an acquisition unit 11 that acquires data consisting of records including the number of people as an item, and a grouping unit 12 that performs grouping (integration) based on the distance between records for the small-number records, which are records with a number of people less than the threshold value, among the data acquired by the acquisition unit 11. With this configuration, grouping (integration) is performed based on the distance between records for the small-number records among the records including the number of people as an item. That is, records with a small corresponding number of people can be appropriately processed.

[0051] Also, in the apparatus 1, the distance may relate to the proximity of the values of the same item of each record. With this configuration, grouping can be more reliably performed because grouping is performed based on the proximity of the values of the same item of each record.

[0052] Furthermore, the grouping unit 12 of the device 1 may perform grouping (integration) of records with a small number of people, such that the total number of people in each record is equal to or greater than a threshold. This configuration allows for the elimination or reduction of records with a small number of people below the threshold, thereby enabling appropriate processing of records with a small number of people.

[0053] Furthermore, the grouping unit 12 of the device 1 may prioritize grouping (integrating) records with smaller numbers of people that are geographically closer. This configuration allows for the grouping of similar records with small numbers of people, for example, enabling appropriate processing of records with a small number of people.

[0054] Furthermore, the acquisition unit 11 of the device 1 may acquire scale information related to the scale to be used as distance, and the grouping unit 12 may perform grouping (integration) based on the scale information acquired by the acquisition unit 11. This configuration allows for more reliable and intended grouping based on the scale to be used as distance.

[0055] Furthermore, the acquisition unit 11 of the device 1 may acquire conditional information regarding the conditions for grouping (integration), and the grouping unit 12 may perform grouping (integration) based on the conditional information acquired by the acquisition unit 11. This configuration allows for more reliable and intended grouping based on the grouping conditions.

[0056] Furthermore, the grouping unit 12 of the device 1 may delete records with fewer than a threshold number of people from the data after grouping (integration). This configuration allows for appropriate processing of records with a small number of people.

[0057] Furthermore, the grouping unit 12 of the device 1 may perform grouping (integration) based on an evaluation function that is based on the sum of the number of records with fewer than a threshold number among the data after grouping (integration). This configuration can reduce, for example, the number of people who are deleted.

[0058] Furthermore, the grouping unit 12 of the device 1 may perform grouping (integration) based on an evaluation function that is based on the sum of the distances in the grouping (integration) performed. With this configuration, for example, records that are close together can be grouped.

[0059] Device 1 takes "data to be provided to a third party," "a measure of distance between data," and "grouping constraints" as input, groups and aggregates records of small numbers that are close together, and generates data that can be provided to a third party by aggregating them to a threshold above which a small number of records will be deleted. Compared to simple small-scale deletion (see the "Deletion of Records of Small Numbers" issue above), Device 1 deletes fewer people (records), thus suppressing "changes in data trends before and after processing." Furthermore, compared to differential privacy (see the "Application of Differential Privacy" issue above), Device 1 only requires setting information that does not require specialized knowledge, such as information for determining "close data" and constraints for determining "closeness," and has the characteristic of not depending on experience. In other words, Device 1 can generate data that can be provided to a third party using a method that "prevents changes in data trends before and after processing" and "does not depend on experience."

[0060] Device 1 groups records of a small number of people and automatically determines the aggregation granularity so that the number of records exceeds a threshold. Device 1 relates to an algorithm for changing the aggregation granularity of privacy data.

[0061] In this embodiment, time (departure time) and region (origin and destination) were used as examples, but other items can be used without any problems and the same processing can be applied. In that case, ordinal, interval, and ratio scales can be used to calculate distance directly, but nominal scales will be converted to other scales as appropriate, as described above.

[0062] The apparatus 1 of this disclosure may have the following configuration.

[0063] [1] An apparatus comprising: an acquisition unit that acquires data consisting of records that include the number of people as an item; and an integration unit that integrates small-number records, which are records with a number of people less than a threshold, from the data acquired by the acquisition unit, based on the distance between records.

[0064] [2] The apparatus described in [1], wherein the distance is related to the proximity of values ​​for the same item in each record.

[0065] [3] The apparatus according to [1] or [2], wherein the integration unit performs the integration such that the total number of people in the small number of records is equal to or greater than the threshold.

[0066] [4] The integration unit performs the integration preferentially on records of a small number of people that are close in distance, as described in any one of [1] to [3].

[0067] [5] The apparatus according to any one of [1] to [4], wherein the acquisition unit further acquires scale information relating to the scale to be adopted as the distance, and the integration unit performs the integration based on the scale information acquired by the acquisition unit.

[0068] [6] The apparatus according to any one of [1] to [5], wherein the acquisition unit further acquires condition information relating to the conditions for the integration, and the integration unit performs the integration based on the condition information acquired by the acquisition unit.

[0069] [7] The apparatus according to any one of [1] to [6], wherein the integration unit deletes records of fewer than the threshold number of people from the data after the integration.

[0070] [8] The apparatus according to any one of [1] to [7], wherein the integration unit performs the integration based on an evaluation function that is based on the sum of the number of records among the data in which the number of people is less than the threshold when the integration is performed.

[0071] [9] The apparatus according to any one of [1] to [8], wherein the integration unit performs the integration based on an evaluation function based on the sum of the distances in the integration when the integration is performed.

[0072] The block diagrams used in the description of the above embodiments show functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired or wireless connections). A functional block may also be realized by combining software with the one or more of the above devices.

[0073] Functions include, but are not limited to, judgment, decision, determination, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. In all cases, as mentioned above, the method of implementation is not particularly limited.

[0074] For example, Apparatus 1 in one embodiment of the present disclosure may function as a computer that processes the integration method of the present disclosure. Figure 11 is a diagram showing an example of the hardware configuration of Apparatus 1 according to one embodiment of the present disclosure. Physically, Apparatus 1 may be configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0075] In the following explanation, the term "device" can be replaced with "circuit," "device," "unit," etc. The hardware configuration of device 1 may include one or more of the devices shown in the figure, or it may be configured to omit some of the devices.

[0076] Each function in device 1 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which allows the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of data reading and writing in the memory 1002 and storage 1003.

[0077] The processor 1001 controls the entire computer, for example, by running an operating system. The processor 1001 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control devices, arithmetic units, registers, etc. For example, the acquisition unit 11, grouping unit 12, and output unit 13 described above may be implemented by the processor 1001.

[0078] Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the acquisition unit 11, the grouping unit 12, and the output unit 13 may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and other functional blocks may be implemented similarly. The above-described various processes have been explained as being executed by one processor 1001, but they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0079] The memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. The memory 1002 may also be called a register, cache, main memory, etc. The memory 1002 can store executable programs (program code), software modules, etc., for carrying out a wireless communication method according to one embodiment of the present disclosure.

[0080] The storage 1003 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., Compact Disc, Digital Multipurpose Disc, Blu-ray® Disc), a smart card, flash memory (e.g., a card, stick, key drive), a floppy® disk, a magnetic strip, etc. The storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0081] The communication device 1004 is hardware (transceiver / receiver device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include, for example, a high-frequency switch, duplexer, filter, frequency synthesizer, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the acquisition unit 11, grouping unit 12, and output unit 13 described above may be implemented by the communication device 1004.

[0082] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0083] Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0084] Furthermore, the device 1 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0085] The notification of information is not limited to the manner / embodiments described herein and may be carried out by other means.

[0086] Each aspect / embodiment described in this disclosure may be applied to at least one of the following systems: LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (new Radio), W-CDMA®, GSM®, CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi®), IEEE 802.16 (WiMAX®), IEEE 802.20, UWB (Ultra-WideBand), Bluetooth®, and other appropriate systems, as well as next-generation systems extended based thereon. Furthermore, multiple systems may be applied in combination (for example, a combination of at least one of LTE and LTE-A with 5G).

[0087] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be reordered, provided they do not contradict each other. For example, the methods described in this disclosure present various step elements using exemplary order and are not limited to the specific order presented.

[0088] Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be transmitted to other devices.

[0089] The determination may be made by a value represented by one bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value).

[0090] Each aspect / embodiment described in this disclosure may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0091] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Therefore, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0092] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0093] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technology (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technology (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0094] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0095] In addition, terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meaning.

[0096] The terms “system” and “network” as used in this disclosure are interchangeable.

[0097] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or corresponding other information.

[0098] The names used for the parameters described above are not restrictive in any way. Furthermore, the formulas and other expressions using these parameters may differ from those expressly disclosed in this disclosure.

[0099] The terms “determining” and “decision” as used in this disclosure may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., searching in tables, databases or other data structures), and ascertaining. “Determining” may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, and accessing (e.g., accessing data in memory). Furthermore, “determining” may include resolving, selecting, choosing, establishing, and comparing. In other words, "judgment" and "decision" can include considering that some action has been "judged" or "decided." Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0100] The terms “connected,” “coupled,” and any variations thereof mean any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be reinterpreted as “access.” As used in this disclosure, two elements may be considered to be “connected” or “coupled” with each other using at least one of one or more wires, cables, and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain.

[0101] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0102] Any reference to elements using the designations “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to the first and second elements do not imply that only two elements may be employed, or that the first element must precede the second element in any way.

[0103] In the configuration of each of the above devices, "means" may be replaced with "part," "circuit," "device," etc.

[0104] Where the terms “include,” “including,” and variations thereof are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR.

[0105] In this disclosure, if articles are added by translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0106] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different."

[0107] 1...Device, 10...Storage unit, 11...Acquisition unit, 12...Grouping unit, 13...Output unit, 1001...Processor, 1002...Memory, 1003...Storage, 1004...Communication device, 1005...Input device, 1006...Output device, 1007...Bus.

Claims

1. An apparatus comprising: an acquisition unit that acquires data consisting of records that include the number of people as an item; and an integration unit that integrates the small number of people records, which are records with fewer people than a threshold, from the data acquired by the acquisition unit, based on the distance between records.

2. The apparatus according to claim 1, wherein the distance relates to the proximity of values ​​for the same item in each record.

3. The apparatus according to claim 1, wherein the integration unit performs the integration such that the total number of people in the small number of records is equal to or greater than the threshold.

4. The apparatus according to claim 1, wherein the integration unit prioritizes the integration of records with a smaller number of users that are closer in distance.

5. The apparatus according to claim 1, wherein the acquisition unit further acquires scale information relating to the scale to be adopted as the distance, and the integration unit performs the integration based on the scale information acquired by the acquisition unit.

6. The apparatus according to claim 1, wherein the acquisition unit further acquires conditional information relating to the conditions for the integration, and the integration unit performs the integration based on the conditional information acquired by the acquisition unit.

7. The apparatus according to claim 1, wherein the integration unit deletes records of fewer than the threshold number of people from the data after the integration.

8. The apparatus according to claim 1, wherein the integration unit performs the integration based on an evaluation function that is based on the sum of the number of records among the data in which the number of people is less than the threshold after the integration has been performed.

9. The apparatus according to claim 1, wherein the integration unit performs the integration based on an evaluation function that is based on the sum of the distances in the integration when the integration is performed.

10. A method performed by a computer, comprising: an acquisition step of acquiring data consisting of records that include the number of people as an item; and an integration step of integrating the small number records, which are records with fewer people than a threshold, from the data acquired in the acquisition step, based on the distance between records.

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