Pattern updating device, pattern updating method, and pattern updating program

Abstract

To provide a pattern update device in which a processing load required for pattern extraction and update is reduced.SOLUTION: A pattern update device 10 includes: an extraction unit 101 that extracts a frequent pattern from a plurality of pieces of route information; a storage unit 102 that stores a pattern extracted by the extraction unit 101, as a predetermined data structure; a collation unit 103 that collates newly obtained route information with the predetermined data structure that is stored in the storage unit 102; and an update unit 104 that updates the frequent pattern in all the pieces of route information including the newly obtained route information by updating the stored predetermined data structure based on the collation by the collation unit 103.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a pattern updating device, a pattern updating method, and a pattern updating program. [Background technology]

[0002] Non-Patent Documents 1 and 2 disclose a method of extracting frequent patterns by batch processing using a plurality of trips (sequences of road links) as input. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] H. Cao et al., Mining frequent spatio-temporal sequential patterns, IEEE International Conference on Data Mining (ICDM), 2005. [Non-Patent Document 2] F. Giannotti et al., Efficient mining of temporally annotated sequences, SIAM International Conference on Data Mining (SDM), 2006. Summary of the Invention [Problem to be solved by the invention]

[0004] The methods in Non-Patent Documents 1 and 2 only provide a technique for extracting frequent patterns by batch processing using multiple trips (sequences of road links) as input, and it is necessary to extract a pattern every time a new trip (sequence of road links) is input. In addition, although the extracted patterns can often be aggregated using prefixes or the like, simple methods require matching for the number of patterns x the pattern length. Here, a pattern refers to a set of specific travel points in a trip, and the pattern length refers to the number of specific travel points in a pattern.

[0005] The present invention has been made in consideration of the above points, and has an object to provide a pattern updating device, a pattern updating method, and a pattern updating program that reduce the processing load required for pattern extraction and updating compared to the conventional techniques. [Means for solving the problem]

[0006] The pattern updating device described in claim 1 includes an extraction unit that extracts a frequently occurring pattern from a plurality of route information, a storage unit that stores the pattern extracted by the extraction unit as a predetermined data structure, a comparison unit that compares newly acquired route information against the predetermined data structure stored in the storage unit, and an update unit that updates the frequently occurring pattern in all route information including the newly acquired route information by updating the stored predetermined data structure based on the comparison by the comparison unit.

[0007] In the pattern updating device described in claim 1, an extraction unit extracts a frequently occurring pattern from a plurality of pieces of route information, a storage unit stores the extracted pattern as a predetermined data structure, a matching unit matches newly acquired route information against the stored predetermined data structure, and an update unit updates the stored predetermined data structure based on the matching by the matching unit, thereby updating a frequently occurring pattern in all route information including the newly acquired route information. According to the pattern updating device described in claim 1, a processing load when updating a pattern can be reduced by adapting a frequently occurring pattern to a predetermined data structure and storing it, matching route information against the stored pattern, and updating the pattern based on the matching result. According to the pattern updating device described in claim 1, matching and updating of frequency can be simplified by matching new route information based on the data structure.

[0008] In the pattern updating device according to claim 2, the extraction unit extracts, as a frequent pattern, route information that has exceeded a set number of times that is set lower than the appearance frequency of a frequent pattern to be acquired.

[0009] According to the pattern updating device of claim 2, a frequently occurring pattern is extracted based on the minimum occurrence frequency of the pattern and the number of pieces of route information to be matched by the matching unit, so that the pattern can be extracted with plenty of time to spare.

[0010] In the pattern updating device according to claim 3, the updating section causes a pattern that has become a new frequently occurring pattern as a result of the matching by the matching section to fit into the data structure and to be stored in the storage section.

[0011] According to the pattern updating device of claim 3, a pattern that has become a new frequently occurring pattern through matching can be made a matching target.

[0012] In the pattern updating device according to claim 4, the data structure is a data structure in which common prefixes are aggregated in a tree structure and the number of occurrences is held in edges.

[0013] According to the pattern updating device of claim 4, the data structure is a data structure in which common prefixes are aggregated in a tree structure and the number of occurrences is held in the edges, thereby making it possible to simplify the data structure.

[0014] In the pattern updating method described in claim 5, a processor extracts frequently occurring patterns from a plurality of route information, stores the extracted patterns as a predetermined data structure, compares newly acquired route information against the stored predetermined data structure, and updates the stored predetermined data structure based on the comparison, thereby executing a process of updating frequently occurring patterns in all route information including the newly acquired route information.

[0015] The pattern updating program described in claim 6 causes a computer to execute a process of extracting a frequently occurring pattern from a plurality of pieces of route information, storing the extracted pattern as a predetermined data structure, comparing newly acquired route information against the stored predetermined data structure, and updating the stored predetermined data structure based on the comparison, thereby updating the frequently occurring pattern in all route information including the newly acquired route information. Effect of the Invention

[0016] According to the present invention, by extracting frequently occurring patterns of route information from the position information of a vehicle's driving data, and efficiently matching the patterns when new position information is acquired, and updating the frequently occurring patterns, it is possible to provide a pattern update device, a pattern update method, and a pattern update program that reduce the processing load compared to conventional techniques. [Brief description of the drawings]

[0017] [Figure 1] FIG. 1 is a diagram illustrating a configuration of a pattern updating device according to an embodiment of the disclosed technique. [Diagram 2] 10 is a diagram illustrating an example of a data structure of a pattern stored in a storage unit; [Diagram 3] 13 is a diagram illustrating an example of collation by a collation unit and updating by an update unit. FIG. [Figure 4] 13 is a diagram illustrating an example of collation by a collation unit and updating by an update unit. FIG. [Diagram 5] 13 is a diagram illustrating an example of collation by a collation unit and updating by an update unit. FIG. [Figure 6] 13 is a diagram illustrating an example of collation by a collation unit and updating by an update unit. FIG. [Figure 7] 13 is a diagram illustrating an example of collation by a collation unit and updating by an update unit. FIG. [Figure 8] 10 is a flowchart showing a flow of a pattern updating process performed by the pattern updating device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] Hereinafter, an example of an embodiment of the present invention will be described with reference to the drawings. In each drawing, the same or equivalent components and parts are given the same reference numerals. In addition, the dimensional ratios of the drawings are exaggerated for the convenience of explanation and may differ from the actual ratios.

[0019] Fig. 1 is a diagram showing the configuration of a pattern updating device according to this embodiment. The pattern updating device 10 shown in Fig. 1 is a device that extracts frequently occurring trajectory patterns from position information of vehicle travel data, efficiently matches new position information with the patterns, and updates the frequently occurring patterns.

[0020] The pattern update device 10 includes a central processing unit (CPU) 11, a read only memory (ROM) 12, a random access memory (RAM) 13, and a storage 14.

[0021] The CPU 11 is a central processing unit, and executes various programs and controls each part. That is, the CPU 11 reads a program from the ROM 12 or the storage 14, and executes the program using the RAM 13 as a working area. The CPU 11 controls each of the above components and performs various arithmetic processing according to the program recorded in the ROM 12 or the storage 14. In this embodiment, the ROM 12 or the storage 14 stores a pattern update program that executes a pattern update process for updating a pattern.

[0022] The ROM 12 stores various programs and various data. The RAM 13 temporarily stores programs or data as a working area. The storage 14 is configured with a storage device such as a hard disk drive (HDD), a solid state drive (SSD), or a flash memory, and stores various programs including an operating system, and various data.

[0023] When executing the above-mentioned pattern updating program, the pattern updating device 10 realizes various functions using the above-mentioned hardware resources. The functional configuration realized by the pattern updating device 10 will be described.

[0024] As shown in FIG. 1, the pattern updating device 10 functions such that the CPU 11 has an extraction unit 101, a storage unit 102, a comparison unit 103 and an update unit 104 by reading and executing an analysis program stored in the ROM 12 or the storage 14 by the CPU 11.

[0025] The extraction unit 101 extracts a frequent pattern from a plurality of pieces of route information acquired in advance. The route information may be stored in, for example, the storage 14. For example, the extraction unit 101 may extract, as a frequent pattern, route information that has exceeded a set number of times that is set lower than the frequency of occurrence of the frequent pattern to be acquired.

[0026] Specifically, the extraction unit 101 extracts frequent patterns from the position information of multiple vehicle travel data. The position information refers to, for example, a series of GPS latitude and longitude (trip). By associating the latitude and longitude with, for example, predefined discrete road links, an expression of a road link string such as the following is obtained for each trip. In the example of trip 1, this means that the trip started from road A, went over road B, and then passed over road C. Each road link is also called a node. Trip 1: A→B→C Trip 2: A→B→C→D Trip 3: A→B→D→C→D

[0027] The extraction unit 101 extracts frequent trajectory patterns from such trip expressions using, for example, a method of sequential pattern mining proposed in the following documents 1 and 2. In this case, the extraction unit 101 extracts only patterns whose occurrence frequency is (mN) or more, with the user specifying the minimum frequency m at which the pattern appears among the total number of trips M, and the number N of trips to be matched with the extracted pattern in the matching unit 103 described later. In addition, the user may specify the ratio r (=(mN) / M) as a condition.

[0028] (Reference 1) ·J. Pei et al., PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of International Conference on Data Engineering (ICDE), 2001.

[0029] (Reference 2) ·R. Srikant and R. Agrawal, Mining sequential patterns: generalizations and performance improvements. In Proceedings of the 5th International Conference on Extending Database Technology Advances in Database Technology (EDBT), 1996.

[0030] When matching unit 103 matches a pattern with occurrence frequency m, the reason why extraction unit 101 pre-extracts the number of occurrences of a pattern to create a tree structure as (mN) is to allow some leeway in extracting frequent patterns. For example, when it is desired to obtain a frequent pattern with occurrence frequency m=10, and the number N of newly added trips is set to N=4, extraction unit 101 initially creates a tree structure with only trips that have appeared mN=6 or more times. By extracting in this manner by extraction unit 101, when matching a new trip against a pattern with occurrence frequency 6 and all N(=4) trips match, a frequent pattern with occurrence frequency of 10 is obtained.

[0031] The storage unit 102 stores the pattern extracted by the extraction unit 101 as a predetermined data structure. The predetermined data structure may be, for example, one of those described in the above-mentioned documents 1 and 2, or may be a data structure in which common prefixes are aggregated in a tree structure and the number of occurrences is held in the edges. The function of the storage unit 102 may be provided in the storage 14.

[0032] 2 is a diagram showing an example of the data structure of patterns stored in the storage unit 102. FIG. 2 shows an example in which the following patterns are stored. Pattern 1: A → B (frequency = 4) Pattern 2: A → B → C (frequency = 3) Pattern 3: A → C (frequency = 3) Pattern 4: A → D (frequency = 4) Pattern 5: E → D (frequency = 4) Pattern 6: E → F (frequency = 3)

[0033] The collation unit 103 collates the path information newly acquired by the pattern updating device 10 against the predetermined data structure stored in the storage unit 102 .

[0034] The update unit 104 updates the frequent patterns in all route information including the route information newly acquired by the pattern updating device 10 by updating the predetermined data structure stored in the storage unit 102 based on the matching of the route information by the matching unit 103. The update unit 104 may adapt the pattern that has become a new frequent pattern through the matching by the matching unit 103 to the predetermined data structure and store it in the storage unit 102.

[0035] The collation by the collation unit 103 and the update by the update unit 104 are repeated until no more route information is obtained.

[0036] 3 to 7 are diagrams showing examples of collation by the collation unit 103 and updating by the update unit 104. Fig. 3 shows an example of collation when a trip A→B→C is newly acquired.

[0037] First, since the starting point is node A, the matching unit 103 first matches node A. Then, the updating unit 104 increments the occurrence count of node A from 5 to 6, as shown in FIG.

[0038] Because the next node is node B, collation unit 103 collates the route from node A to node B. Then, update unit 104 increments the occurrence count of the route from node A to node B from 4 to 5, as shown in FIG.

[0039] Because the next node is node C, the collation unit 103 collates the route from node B to node C. Then, the update unit 104 increments the occurrence count of the route from node A to node C via node B from 3 to 4, as shown in FIG.

[0040] The trip A→B→C can be considered to start from node B along the way. Since the start point is node B, collation unit 103 first collates node B. Then, update unit 104 increments the number of occurrences of node B from 4 to 5, as shown in FIG.

[0041] Since the next node is node C, the collation unit 103 collates the route from node B to node C. Then, the update unit 104 increments the occurrence count of the route reaching node C from 3 to 4, as shown in FIG.

[0042] As shown in FIGS. 3 to 7, the update unit 104 increments the number of occurrences of the path of the corresponding pattern by one, and adds a new pattern that has a frequency equal to or greater than the minimum frequency m or the minimum rate r designated by the user.

[0043] When a new pattern that has not appeared in the patterns appears, the matching unit 103 stops at an intermediate node during matching. For example, when a trip A → Z → B appears, it stops at node Z, which is a child node of node A, and updates the frequency of A → B when it reaches node B, which is further ahead. In this case, if A → Z exceeds the frequency threshold, it is registered as a new pattern.

[0044] When the collation unit 103 collates a trip A→B→C and the update unit 104 updates the frequency, there are two patterns of collation, A→B→C and B→C, but the frequency of B→C is updated only once. Also, the storage unit 102 may distinguish between the pattern B→C starting from node A and the pattern B→C starting from node B and store them as separate information.

[0045] When a new pattern such as trip A→B→C→A appears, the storage unit 102 may store the pattern by making a new end node A appear.

[0046] 1, the pattern update device 10 extracts frequent trajectory patterns from position information (GPS, etc.) of vehicle travel data, and efficiently matches the patterns when new position information is acquired, and updates the frequent patterns. The pattern update device 10 efficiently matches the patterns and updates the frequent patterns, thereby making it possible to reduce the processing load compared to conventional techniques.

[0047] Next, the operation of the pattern updating device 10 will be described.

[0048] 8 is a flowchart showing the flow of the pattern update process by the pattern update device 10. The CPU 11 reads out the pattern update program from the ROM 12 or the storage 14, loads it into the RAM 13, and executes it, thereby performing the pattern update process.

[0049] The CPU 11 extracts a frequent pattern from a plurality of pieces of route information acquired in advance (step S101). The route information may be stored, for example, in the storage 14. For example, the CPU 11 may extract, as a frequent pattern, route information that has exceeded a set number of times that is set lower than the frequency of occurrence of the frequent pattern to be acquired.

[0050] Following step S101, CPU 11 stores the pattern extracted in step S101 as a predetermined data structure (step S102). The predetermined data structure may be, for example, one of those described in the above-mentioned documents 1 and 2, or may be a data structure in which common prefixes are aggregated in a tree structure and the number of occurrences is stored in the edges.

[0051] Following step S102, the CPU 11 starts a process of reading in a new trajectory. First, the CPU 11 sets the number N of patterns to be newly read, and sets a variable n to 0 (step S103).

[0052] Following step S103, the CPU 11 determines whether the variable n is equal to or smaller than the number N of trips to be matched with the extracted pattern (step S104).

[0053] If it is determined in step S104 that n≦N (step S104; Yes), the CPU 11 then compares a pattern from the path information newly acquired by the pattern update device 10 against the predetermined data structure stored in step S102 (step S105).

[0054] Following step S105, the CPU 11 updates the predetermined data structure stored in step S102 based on the comparison in step S105, thereby updating the patterns that frequently appear in all the route information including the route information newly acquired by the pattern updating device 10 (step S106).

[0055] Following step S106, the CPU 11 determines whether there are any more trips to be read (step S107). If there are any more trips to be read (step S107; Yes), the CPU 11 then increments the variable n by one (step S108) and returns to the determination of step S104. If there are no more trips to be read (step S107; No), the CPU 11 ends the series of processes.

[0056] On the other hand, if the result of the determination in step S104 is n>N (step S104; No), the CPU 11 returns to step S101.

[0057] The pattern update device 10 executes the series of processes shown in Fig. 4 to extract frequently occurring trajectory patterns from position information (GPS, etc.) of vehicle travel data, and efficiently matches the patterns when new position information is acquired, and updates the frequently occurring patterns. The pattern update device 10 efficiently matches the patterns and updates the frequently occurring patterns, thereby making it possible to reduce the processing load compared to conventional techniques.

[0058] Although a storage 14 is provided inside the pattern updating device 10, the present invention is not limited to such an example.

[0059] In addition, the pattern update process executed by the CPU by reading the software (program) in each of the above embodiments may be executed by various processors other than the CPU. In this case, examples of the processor include a PLD (Programmable Logic Device) such as an FPGA (Field-Programmable Gate Array) whose circuit configuration can be changed after manufacture, and a dedicated electric circuit such as an ASIC (Application Specific Integrated Circuit) which is a processor having a circuit configuration designed exclusively for executing a specific process. In addition, the pattern update process may be executed by one of these various processors, or may be executed by a combination of two or more processors of the same or different types (for example, a plurality of FPGAs, and a combination of a CPU and an FPGA). In addition, the hardware structure of these various processors is, more specifically, an electric circuit that combines circuit elements such as semiconductor elements.

[0060] In addition, in each of the above embodiments, the program for the pattern update process is stored (installed) in advance in a ROM or storage, but the present invention is not limited to this. The program may be provided in a form recorded on a non-transitory recording medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory. The program may also be downloaded from an external device via a network. [Explanation of symbols]

[0061] 10 Pattern update device 101 Extraction part 102 Storage area 103 Matching Unit 104 Update Department

Claims

1. An extraction unit that extracts a frequent pattern from a plurality of pieces of route information; a storage unit that stores the pattern extracted by the extraction unit as a predetermined data structure; a comparison unit that compares newly acquired route information with a predetermined data structure stored in the storage unit; an update unit that updates the stored predetermined data structure based on the collation by the collation unit, thereby updating a frequently occurring pattern in all route information including newly acquired route information; Equipped with The update unit conforms a pattern that has become a new frequent pattern as a result of exceeding a threshold value through the comparison by the comparison unit to the data structure and stores the pattern in the storage unit.

2. The pattern updating device according to claim 1 , wherein the extraction unit extracts, as the frequent pattern, route information that has exceeded a set number of times that is set lower than the frequency of occurrence of the frequent pattern to be acquired.

3. 3. The pattern updating device according to claim 1, wherein the data structure is a data structure in which common prefixes are aggregated in a tree structure and the number of occurrences is held in edges.

4. The processor: Extracting frequent patterns from multiple route information, storing the extracted patterns as a predetermined data structure; Matching the newly acquired route information against the stored predetermined data structure; updating the stored predetermined data structure based on the comparison to update frequent patterns in all route information including the newly acquired route information; a pattern that has become a new frequent pattern as a result of the comparison exceeding the threshold is adapted to the data structure and stored; How to perform the process and update the pattern.

5. On the computer, Extracting frequent patterns from multiple route information, storing the extracted patterns as a predetermined data structure; Matching the newly acquired route information against the stored predetermined data structure; updating the stored predetermined data structure based on the comparison to update frequent patterns in all route information including the newly acquired route information; a pattern that has become a new frequent pattern as a result of exceeding a threshold value as a result of the comparison of the route information is adapted to the data structure and stored; The pattern update program that causes the process to run.

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