WO2021210100A1 - Pattern extraction device, pattern extraction method, and program - Google Patents

Pattern extraction device, pattern extraction method, and program Download PDF

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Publication number
WO2021210100A1
WO2021210100A1 PCT/JP2020/016583 JP2020016583W WO2021210100A1 WO 2021210100 A1 WO2021210100 A1 WO 2021210100A1 JP 2020016583 W JP2020016583 W JP 2020016583W WO 2021210100 A1 WO2021210100 A1 WO 2021210100A1
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function
pattern
executions
patterns
unit
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PCT/JP2020/016583
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French (fr)
Japanese (ja)
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忍 斎藤
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日本電信電話株式会社
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Publication of WO2021210100A1 publication Critical patent/WO2021210100A1/en

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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

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  • the present invention relates to a pattern extraction device, a pattern extraction method, and a program.
  • system Inside the information system (hereinafter, simply referred to as "system"), various functions are sequentially executed in order to provide various services. As the services required for systems have become larger and more complex, the number of patterns of system behavior (function execution process) has reached an enormous number.
  • each function inside the system has a mutual dependency relationship, so changing one function may affect the behavior of other functions. Therefore, at the timing of system reconstruction, etc., not only investigate how each function is used alone, but also investigate the flow of multiple functions (function execution process) executed when the system provides services. It is also important.
  • FIG. 1 is a diagram showing an example of distribution of the number of times the pattern of the system execution process is executed.
  • the horizontal axis corresponds to the number of patterns and the vertical axis corresponds to the number of executions.
  • the number (type) of rare patterns with a small number of executions tends to be much larger than the number of high-frequency patterns.
  • the present invention has been made in view of the above points, and an object of the present invention is to efficiently extract a pattern including a function that needs improvement from among the patterns of the execution process of the function of the system.
  • the pattern extraction device includes a calculation unit that calculates the number of executions of the function for each time interval for each function based on the history of the execution process of the function of the system, and a calculation unit for each time interval. Based on the number of executions, the pattern including the function specified by the specific unit is extracted from the specific unit that specifies the function whose execution time is biased and the pattern of the execution process based on the history. It has an extraction unit.
  • FIG. 2 is a diagram showing a hardware configuration example of the pattern extraction device 10 according to the first embodiment.
  • the pattern extraction device 10 of FIG. 2 includes a drive device 100, an auxiliary storage device 102, a memory device 103, a CPU 104, an interface device 105, a display device 106, an input device 107, and the like, which are connected to each other by a bus B, respectively.
  • the program that realizes the processing in the pattern extraction device 10 is provided by a recording medium 101 such as a CD-ROM.
  • a recording medium 101 such as a CD-ROM.
  • the program is installed in the auxiliary storage device 102 from the recording medium 101 via the drive device 100.
  • the program does not necessarily have to be installed from the recording medium 101, and may be downloaded from another computer via the network.
  • the auxiliary storage device 102 stores the installed program and also stores necessary files, data, and the like.
  • the memory device 103 reads and stores the program from the auxiliary storage device 102 when the program is instructed to start.
  • the CPU 104 realizes the function related to the pattern extraction device 10 according to the program stored in the memory device 103.
  • the interface device 105 is used as an interface for connecting to a network.
  • the display device 106 displays a programmatic GUI (Graphical User Interface) or the like.
  • the input device 107 is composed of a keyboard, a mouse, and the like, and is used for inputting various operation instructions.
  • FIG. 3 is a diagram showing a functional configuration example of the pattern extraction device 10 according to the first embodiment.
  • the pattern extraction device 10 includes a pattern generation unit 11, an index calculation unit 12, a target function identification unit 13, a pattern extraction unit 14, an output unit 15, and the like.
  • Each of these parts is realized by a process of causing the CPU 104 to execute one or more programs installed in the pattern extraction device 10.
  • the pattern extraction device 10 also uses the system log storage unit 121, the execution count storage unit 122, and the like.
  • Each of these storage units can be realized by using, for example, a storage device that can be connected to the auxiliary storage device 102 or the pattern extraction device 10 via a network.
  • the pattern (hereinafter, simply referred to as “pattern”) of the function execution process of a computer system (hereinafter, simply referred to as “system”) such as an information system needs to be improved by focusing on the execution time. Pattern extraction (search) is performed.
  • the function execution process means, for example, information indicating the execution order of functions used in a certain business work (hereinafter, referred to as “case”).
  • case information indicating the execution order of functions used in a certain business work
  • the functions used and the order in which the functions are executed differ depending on the case. For example, in a system including functions A, B, C, D, etc., A ⁇ B ⁇ D is executed in a certain work (case 1), and A ⁇ C ⁇ D is executed in another work (case 2).
  • NS a system including functions A, B, C, D, etc.
  • each of "A-> B-> D” and “A-> C-> D” corresponds to the pattern or type of the function execution process (hereinafter, simply referred to as "pattern").
  • pattern the case (execution process) and the pattern do not have a one-to-one correspondence. For example, when the same execution process appears in a plurality of cases in which the cases are different from each other, these execution processes are extracted as one pattern.
  • the improvement-required pattern refers to a pattern in which there is a relatively high possibility that improvement is required among a plurality of patterns.
  • temporal characteristic pattern a pattern including a function having a temporal characteristic (biased) in the execution time
  • search a pattern including a function having a temporal characteristic (biased) in the execution time
  • target function a pattern including a function having a limited execution time (execution time is limited)
  • target function corresponds to a time phase feature pattern and is required. It is extracted (searched) as an improvement pattern.
  • FIG. 4 is a diagram for explaining the outline of extraction of the improvement-requiring pattern in the first embodiment.
  • FIG. 4 shows patterns 1, patterns 70 to 79, ..., Patterns 80 to 89, and the like.
  • a rounded rectangle containing the letters A, B, C, E or F indicates a function.
  • each of patterns 70 to 79 and patterns 80 to 89 corresponds to a rare pattern.
  • the number of times the pattern is executed is the number of times the pattern is executed based on the execution record of each pattern in the past use of the system.
  • the patterns 71 to 78 are executed four times each, and the patterns 81 to 88 are executed four times each.
  • the number of times each function is executed in the certain period is as follows in descending order.
  • the number of times each of the function E and the function F included in the rare pattern is executed is 40 times, but this value is insignificant as compared with the number of times the other functions are executed. Not really.
  • the function E is included only in the patterns 70 to 79 and the function F is included only in the patterns 80 to 89, the number of appearance patterns of the function E and the function F is 10, respectively, which is not necessarily insignificant. ..
  • patterns 70 to 89 do not have any special features that should be judged to require improvement, except that they are rare patterns.
  • FIG. 5 is a diagram showing the number of executions of the function E and the function F by month.
  • FIG. 5 shows the number of executions by month for the six months from October to March for each of the functions E and F in the form of a table and a bar graph.
  • each of the patterns 80 to 89 including the target function corresponds to the time phase feature pattern and is a candidate for the improvement-required pattern.
  • improvement of the improvement-required pattern since the execution time of the improvement-required pattern is limited, it is possible to return to manual operation, delete the function F, or replace the function F with another function. ..
  • FIG. 6 is a flowchart for explaining an example of the processing procedure executed by the pattern extraction device 10 in the first embodiment.
  • step S101 the pattern generation unit 11 extracts a pattern based on the system log stored in the system log storage unit 121.
  • FIG. 7 is a diagram showing a configuration example of the system log.
  • each line constituting the system log is referred to as "log data".
  • the log data is recorded in the system log storage unit 121 (added to the system log) each time one function of the system is executed.
  • each log data includes a case ID, a function name, a date and time, and the like.
  • the case ID is unique identification information for each work using the system.
  • the function name is identification information of the function executed in the case related to the case ID.
  • the date and time is the date and time when the function was executed.
  • the sequence of function names of the log data group with the same case ID corresponds to the function execution process. Therefore, it can be said that the system log is a history of the function execution process.
  • step S101 the type of the function execution process is extracted as a pattern.
  • the execution process (A ⁇ B ⁇ C) of the function indicated by the log data group whose case ID is “W 1 ” is extracted as pattern 1
  • the log data whose case ID is “W m” is extracted.
  • the function execution process (A ⁇ E ⁇ B ⁇ C) indicated by the group is extracted as the pattern 70
  • the function execution process (A ⁇ F ⁇ B ⁇ C) indicated by the log data group whose case ID is “W n”. Is shown as an example in which is extracted as pattern 80.
  • These patterns 1, 70 and 80 correspond to the patterns 1, 70 and 80 shown in FIG.
  • the pattern can be extracted from the system log by using a known technique (for example, the technique disclosed in Patent Document 1 and JP-A-2017-187953).
  • the pattern generation unit 11 records the number of executions of the pattern and the number of executions of each function in the pattern in the execution number storage unit 122 for each extracted pattern (S102).
  • FIG. 8 is a diagram showing a configuration example of the execution number storage unit 122.
  • FIG. 8 shows a table in which patterns are assigned in the row direction and the number of times the pattern is executed and the number of times each function is executed (number of times the function is executed) are assigned in the column direction as an example of the configuration of the execution number storage unit 122. ing. The number of function executions in each line is the number of times each function is executed in the pattern corresponding to the line.
  • the pattern generation unit 11 when the pattern generation unit 11 extracts each pattern from the system log, the pattern generation unit 11 can obtain the number of executions of each pattern by counting the execution process of the function matching each pattern in the system log. can.
  • the pattern generation unit 11 can obtain the number of function executions in the pattern by totaling the number of times each function is executed in the execution sequence classified into the pattern for each pattern.
  • the index calculation unit 12 calculates the number of executions for each continuous time interval (Time Bucket) for each function with reference to the system log (FIG. 7) (S103).
  • FIG. 9 is a diagram showing an example of the calculation result of the number of executions of each function for each time interval.
  • FIG. 9 shows the number of executions for each continuous time interval (unit period) in the time series for each function. In the example of FIG. 9, one time interval is one month. Therefore, FIG. 9 shows the number of times each function is executed by month.
  • the value of the "function name" is the function name of the function and the value of the "date and time" is the month for a certain time interval (a certain month of a certain year) of a certain function. By totaling the number of log data belonging to, the number of executions in the time interval is calculated.
  • the target function specifying unit 13 determines whether or not the execution time of each function is limited based on the calculation result (FIG. 9) by the index calculation unit 12, and the function whose execution time is limited. (Target function) is specified (S104). For example, when the threshold value for the number of time intervals in which the number of executions is 1 or more (hereinafter referred to as "the number of execution periods") is 1, and the number of execution periods is not more than the threshold value, the execution time is limited. It may be a condition.
  • FIG. 10 is a diagram showing an example of a determination result of whether or not the execution time is limited.
  • a column of “time-limited determination” is added to the table shown in FIG.
  • the "time-limited determination” is an item indicating a determination result of whether or not the execution time is limited.
  • the value of the item is 1 or 0. 1 indicates a determination result that the execution time is limited. 0 indicates a determination result that the execution time is not limited.
  • the pattern extraction unit 14 counts (counts) the content of the target function for each pattern (S105).
  • the content number of the target function for each pattern can be specified by referring to the execution number storage unit 122 (FIG. 8).
  • the number of target functions contained in the pattern means the number of types of target functions included in the pattern. Therefore, for example, when the target function is one type of the function F, the maximum value of the content is 1.
  • the pattern extraction unit 14 specifies a pattern in which the number of times the function F is executed is 1 or more in FIG.
  • the pattern extraction unit 14 sets the content number of the target function of the pattern to 1.
  • the content number of the target function for the pattern in which the function execution count of the function F is empty is set to 0.
  • FIG. 11 is a diagram showing an example of the counting result of the content number of the target function in the first embodiment.
  • FIG. 11 shows the counting result of the content when the storage content of the execution number storage unit 122 is as shown in FIG. 8 and the function F is the target function.
  • the pattern extraction unit 14 extracts a pattern related to the pattern number in which the "content number of the target function" is 1 or more as a pattern requiring improvement (S106).
  • the output unit 15 outputs a pattern requiring improvement (S107).
  • the pattern number of the pattern requiring improvement may be output.
  • the value of "content number of target function" may be used for prioritization.
  • the pattern number may be output in descending order of the value of "content number of target function”.
  • the output unit 15 may also output the function name of the target function included in the pattern related to the pattern number in association with the output pattern number.
  • the output form is not limited to the predetermined one. For example, it may be displayed on the display device 106, stored in the auxiliary storage device 102, or transmitted to another device.
  • the user can identify the improvement-requiring pattern and the improvement-requiring function by referring to the output information.
  • the pattern requiring improvement can be automatically extracted from the rare patterns. That is, it is possible to efficiently extract patterns including functions that need improvement from the patterns of the system function execution process.
  • the second embodiment will be described which is different from the first embodiment.
  • the points not particularly mentioned in the second embodiment may be the same as those in the first embodiment.
  • a function (hereinafter referred to as "target function") having a large variation in the number of executions in each time interval (variation in the number of executions in an adjacent time interval) is performed at the execution time.
  • target function a function having a specific feature (biased) will be described.
  • FIG. 12 is a diagram for explaining an outline of extraction of the improvement-required pattern in the second embodiment.
  • patterns 90 to 99 are shown instead of patterns 70 to 89.
  • FIG. 13 is a diagram showing an example of the monthly execution number of the function E and the function G and the fluctuation rate of the execution number.
  • FIG. 13 shows a table and a graph showing the number of executions by month (by time interval) for 6 months from October to March for each of function E and function G, and the fluctuation rate of the number of executions by month (adjacent).
  • the function G is executed every month like the function E. Therefore, in the first embodiment, the function G is not specified as a target function.
  • the fluctuation rate of function G in December (the fluctuation rate between November and December) stands out at 1400%, and the number of executions of function G increases momentarily in December (only in December). It can be seen that the number of executions increases sharply). That is, the function G corresponds to a function (target function) in which the number of executions in chronological order for each time interval fluctuates greatly.
  • the patterns 90 to 9 including the function G, which is the target function correspond to the time phase feature pattern and are extracted as the improvement-required pattern.
  • the speed improvement scale-out is performed only when the improvement-requiring pattern increases momentarily.
  • FIG. 14 is a flowchart for explaining an example of the processing procedure executed by the pattern extraction device 10 in the second embodiment.
  • the same steps as those in FIG. 6 are assigned the same step numbers, and the description thereof will be omitted.
  • step S204 the index calculation unit 12 calculates the fluctuation rate of the number of executions of each time interval of each function with respect to the number of executions of the immediately preceding execution section based on the calculation result (FIG. 9) in step S103 (S204). ..
  • FIG. 15 is a diagram showing an example of the calculation result of the fluctuation rate of the number of executions of each function for each time interval.
  • FIG. 15 shows the calculation result of the volatility of the number of executions for each time interval in chronological order for each function. The method of calculating the volatility is as described above.
  • the target function specifying unit 13 determines the presence or absence of the over-variation function for each function based on the calculation result (FIG. 15) by the index calculation unit 12, and identifies the function (target function) corresponding to the over-variation function. (S205). For example, the condition that the threshold value with respect to the absolute value of the volatility is 1000% and the absolute value of the volatility is equal to or more than the threshold value (that is, the volatility is + 1000% or more or -1000% or less) is the excessive fluctuation function. May be.
  • FIG. 16 is a diagram showing an example of a determination result of whether or not the function is an excessive fluctuation function.
  • a column of “excessive fluctuation determination” is added to the table shown in FIG.
  • the "excessive fluctuation determination” is an item indicating a determination result of whether or not the function is an excessive fluctuation function.
  • the value of the item is 1 or 0. 1 indicates a determination result that the function is an excessive fluctuation function.
  • 0 indicates a determination result that the function is not an excessive fluctuation function.
  • FIG. 16 shows the determination result when having a volatility of + 1000% or more or -1000% or less is a condition for the over-variation function. Therefore, the function G having a volatility of 1400% is determined to be an excessive volatility function. That is, in this case, the function G is specified as the target function.
  • the pattern extraction unit 14 counts (counts) the content of the target function for each pattern (S206).
  • the content number of the target function for each pattern can be specified by referring to the execution number storage unit 122 (FIG. 8) as in step S105 of FIG.
  • the pattern extraction unit 14 specifies a pattern in which the number of times the function G is executed is 1 or more in FIG.
  • the pattern extraction unit 14 sets the content number of the target function of the pattern to 1.
  • the content number of the target function for the pattern in which the function execution count of the function G is empty is set to 0.
  • FIG. 17 is a diagram showing an example of the counting result of the content number of the target function in the second embodiment.
  • FIG. 17 shows the counting result of the content when the storage content of the execution number storage unit 122 is as shown in FIG. 8 and the function G is the target function.
  • the number of times the function G is executed is 1 or more for each of the patterns 90 to 99. Therefore, in FIG. 17, the value of the "content of the target function" for each of the patterns 90 to 99 is set to 1, and the value of the "content of the target function" for the patterns other than these is set to 0. There is.
  • the second embodiment it is possible to extract the improvement-requiring pattern from the rare patterns that are difficult to extract as the improvement-requiring pattern in the first embodiment. Therefore, among the patterns of the execution process of the functions of the system, the patterns including the functions that need improvement can be efficiently extracted.
  • the processing procedure of FIG. 14 may be executed following the processing procedure of FIG.
  • a pattern including either or both of the target function in the first embodiment and the target function in the second embodiment may be extracted as a pattern requiring improvement.
  • Patent Document 1 The present embodiment may be applied to the execution process (pattern) of the function with a swim lane in the above.
  • the index calculation unit 12 is an example of the calculation unit.
  • the target function specifying unit 13 is an example of the specific unit.
  • the pattern extraction unit 14 is an example of an extraction unit.
  • Pattern extraction device 11 Pattern generation unit 12 Index calculation unit 13 Target function identification unit 14 Pattern extraction unit 15 Output unit 100 Drive device 101 Recording medium 102 Auxiliary storage device 103 Memory device 104 CPU 105 Interface device 106 Display device 107 Input device 121 System log storage unit 122 Execution count storage unit B bus

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Abstract

This pattern extraction device efficiently extracts, from among patterns of processes for executing functions of a system, a pattern including a function which requires improvement, such extraction carried out as a result of the pattern extraction device including: a calculation unit that calculates, for each function, the number of execution instances for each time segment of the function, on the basis of a history of processes for executing functions of the system; an identification unit that identifies a function with an execution timing deviation, on the basis of the number of execution instances for each time segment; and an extraction unit that extracts, on the basis of the history, a pattern including the function identified by the identification unit, from among the execution process patterns.

Description

パターン抽出装置、パターン抽出方法及びプログラムPattern extraction device, pattern extraction method and program
 本発明は、パターン抽出装置、パターン抽出方法及びプログラムに関する。 The present invention relates to a pattern extraction device, a pattern extraction method, and a program.
 情報システム(以下、単に「システム」という。)の内部では、各種のサービスを提供するために多様な機能が順次実行される。システムに求められるサービスが大規模化・複雑化するなかで、システムの挙動(機能の実行プロセス)のパターンも膨大な数に至っている。 Inside the information system (hereinafter, simply referred to as "system"), various functions are sequentially executed in order to provide various services. As the services required for systems have become larger and more complex, the number of patterns of system behavior (function execution process) has reached an enormous number.
 従来、システムの改善のために、システムの実行データ(システムログ)から機能の実行プロセスのパターンを可視化し、膨大な数のパターンの中から頻繁に発生するパターン(高頻度パターン)を特定し、高頻度パターンに含まれる(=よく使われる)機能を改善の対象の候補として抽出する技術が開示されている(例えば、特許文献1)。 Conventionally, in order to improve the system, the pattern of the function execution process is visualized from the system execution data (system log), and the pattern that frequently occurs (high frequency pattern) is identified from a huge number of patterns. A technique for extracting a function included in a high-frequency pattern (= often used) as a candidate for improvement is disclosed (for example, Patent Document 1).
 なお、システム内部の各機能は相互に依存関係を持つため、ある機能を変更すると他の機能の挙動に影響を及ぼすことがある。そのため、システムの再構築等のタイミングでは、個々の機能単独の使われ方を調査するだけでなく、システムがサービス提供時に実行される複数機能の一連の流れ(機能の実行プロセス)を調査することも重要である。 Note that each function inside the system has a mutual dependency relationship, so changing one function may affect the behavior of other functions. Therefore, at the timing of system reconstruction, etc., not only investigate how each function is used alone, but also investigate the flow of multiple functions (function execution process) executed when the system provides services. It is also important.
特開2017-228257号公報JP-A-2017-228257
 一方、システムの改善には滅多に発生しないパターン(希少パターン)の分析も重要である。改善の一例として、使われていない機能を捨てること(削除機能の選別)や、他機能との統合が挙げられる。 On the other hand, it is also important to analyze patterns (rare patterns) that rarely occur in order to improve the system. Examples of improvements include discarding unused functions (selecting deleted functions) and integrating with other functions.
 しかしながら、パターンの実行回数は、図1に示されるような傾向に有る。図1は、システムの実行プロセスのパターンの実行回数の分布例を示す図である。 However, the number of times the pattern is executed tends to be as shown in FIG. FIG. 1 is a diagram showing an example of distribution of the number of times the pattern of the system execution process is executed.
 図1において、横軸はパターン数に対応し、縦軸は実行回数に対応する。図1に示されるように、実行回数の少ない希少パターンの数(種類)は、高頻度パターンの数に比べて非常に多い傾向にある。 In FIG. 1, the horizontal axis corresponds to the number of patterns and the vertical axis corresponds to the number of executions. As shown in FIG. 1, the number (type) of rare patterns with a small number of executions tends to be much larger than the number of high-frequency patterns.
 そのため、膨大な希少パターンの中から、改善が必要な機能が含まれるパターン(要改善パターン)を効率的に抽出する技術が必要とされる。 Therefore, a technology is required to efficiently extract patterns (improvement-requiring patterns) that include functions that need improvement from a huge number of rare patterns.
 本発明は、上記の点に鑑みてなされたものであって、システムの機能の実行プロセスのパターンのうち改善が必要な機能を含むパターンを効率的に抽出することを目的とする。 The present invention has been made in view of the above points, and an object of the present invention is to efficiently extract a pattern including a function that needs improvement from among the patterns of the execution process of the function of the system.
 そこで上記課題を解決するため、パターン抽出装置は、システムの機能の実行プロセスの履歴に基づいて、前記機能ごとに当該機能の時間区間ごとの実行回数を計算する計算部と、前記時間区間ごとの実行回数に基づいて、実行時期に偏りが有る前記機能を特定する特定部と、前記実行プロセスのパターンの中から、前記特定部が特定した前記機能を含む前記パターンを前記履歴に基づいて抽出する抽出部と、を有する。 Therefore, in order to solve the above problem, the pattern extraction device includes a calculation unit that calculates the number of executions of the function for each time interval for each function based on the history of the execution process of the function of the system, and a calculation unit for each time interval. Based on the number of executions, the pattern including the function specified by the specific unit is extracted from the specific unit that specifies the function whose execution time is biased and the pattern of the execution process based on the history. It has an extraction unit.
 システムの機能の実行プロセスのパターンのうち改善が必要な機能を含むパターンを効率的に抽出することができる。 It is possible to efficiently extract patterns that include functions that need improvement from the patterns of the system function execution process.
システムの実行プロセスのパターンのイメージ例を示す図である。It is a figure which shows the image example of the pattern of the execution process of a system. 第1の実施の形態におけるパターン抽出装置10のハードウェア構成例を示す図である。It is a figure which shows the hardware configuration example of the pattern extraction apparatus 10 in 1st Embodiment. 第1の実施の形態におけるパターン抽出装置10の機能構成例を示す図である。It is a figure which shows the functional structure example of the pattern extraction apparatus 10 in 1st Embodiment. 第1の実施の形態における要改善パターンの抽出の概要を説明するための図である。It is a figure for demonstrating the outline of the extraction of the improvement necessary pattern in 1st Embodiment. 機能E及び機能Fの月別の実行回数を示す図である。It is a figure which shows the number of executions of function E and function F by month. 第1の実施の形態におけるパターン抽出装置10が実行する処理手順の一例を説明するためのフローチャートである。It is a flowchart for demonstrating an example of the processing procedure executed by the pattern extraction apparatus 10 in 1st Embodiment. システムログの構成例を示す図である。It is a figure which shows the configuration example of a system log. 実行回数記憶部122の構成例を示す図である。It is a figure which shows the structural example of the execution number storage part 122. 各機能の時間区間ごとの実行回数の計算結果の一例を示す図である。It is a figure which shows an example of the calculation result of the execution number of each function for each time interval. 実行時期が限定的であるか否かの判定結果の一例を示す図である。It is a figure which shows an example of the determination result whether or not the execution time is limited. 第1の実施の形態における対象機能の含有数のカウント結果の一例を示す図である。It is a figure which shows an example of the count result of the content number of the target function in 1st Embodiment. 第2の実施の形態における要改善パターンの抽出の概要を説明するための図である。It is a figure for demonstrating the outline of the extraction of the improvement-requiring pattern in the 2nd Embodiment. 機能E及び機能Gの月別の実行回数及び当該実行回数の変動率の一例を示す図である。It is a figure which shows an example of the monthly execution number of function E and function G, and the fluctuation rate of the execution number. 第2の実施の形態におけるパターン抽出装置10が実行する処理手順の一例を説明するためのフローチャートである。It is a flowchart for demonstrating an example of the processing procedure executed by the pattern extraction apparatus 10 in 2nd Embodiment. 各機能の時間区間ごとの実行回数の変動率の計算結果の一例を示す図である。It is a figure which shows an example of the calculation result of the fluctuation rate of the number of times of execution of each function for each time interval. 過大変動機能であるか否かの判定結果の一例を示す図である。It is a figure which shows an example of the determination result whether or not it is an overvariation function. 第2の実施の形態における対象機能の含有数のカウント結果の一例を示す図である。It is a figure which shows an example of the count result of the content number of the target function in 2nd Embodiment.
 以下、図面に基づいて本発明の実施の形態を説明する。図2は、第1の実施の形態におけるパターン抽出装置10のハードウェア構成例を示す図である。図2のパターン抽出装置10は、それぞれバスBで相互に接続されているドライブ装置100、補助記憶装置102、メモリ装置103、CPU104、インタフェース装置105、表示装置106、及び入力装置107等を有する。 Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 2 is a diagram showing a hardware configuration example of the pattern extraction device 10 according to the first embodiment. The pattern extraction device 10 of FIG. 2 includes a drive device 100, an auxiliary storage device 102, a memory device 103, a CPU 104, an interface device 105, a display device 106, an input device 107, and the like, which are connected to each other by a bus B, respectively.
 パターン抽出装置10での処理を実現するプログラムは、CD-ROM等の記録媒体101によって提供される。プログラムを記憶した記録媒体101がドライブ装置100にセットされると、プログラムが記録媒体101からドライブ装置100を介して補助記憶装置102にインストールされる。但し、プログラムのインストールは必ずしも記録媒体101より行う必要はなく、ネットワークを介して他のコンピュータよりダウンロードするようにしてもよい。補助記憶装置102は、インストールされたプログラムを格納すると共に、必要なファイルやデータ等を格納する。 The program that realizes the processing in the pattern extraction device 10 is provided by a recording medium 101 such as a CD-ROM. When the recording medium 101 storing the program is set in the drive device 100, the program is installed in the auxiliary storage device 102 from the recording medium 101 via the drive device 100. However, the program does not necessarily have to be installed from the recording medium 101, and may be downloaded from another computer via the network. The auxiliary storage device 102 stores the installed program and also stores necessary files, data, and the like.
 メモリ装置103は、プログラムの起動指示があった場合に、補助記憶装置102からプログラムを読み出して格納する。CPU104は、メモリ装置103に格納されたプログラムに従ってパターン抽出装置10に係る機能を実現する。インタフェース装置105は、ネットワークに接続するためのインタフェースとして用いられる。表示装置106はプログラムによるGUI(Graphical User Interface)等を表示する。入力装置107はキーボード及びマウス等で構成され、様々な操作指示を入力させるために用いられる。 The memory device 103 reads and stores the program from the auxiliary storage device 102 when the program is instructed to start. The CPU 104 realizes the function related to the pattern extraction device 10 according to the program stored in the memory device 103. The interface device 105 is used as an interface for connecting to a network. The display device 106 displays a programmatic GUI (Graphical User Interface) or the like. The input device 107 is composed of a keyboard, a mouse, and the like, and is used for inputting various operation instructions.
 図3は、第1の実施の形態におけるパターン抽出装置10の機能構成例を示す図である。図3において、パターン抽出装置10は、パターン生成部11、指標計算部12、対象機能特定部13、パターン抽出部14及び出力部15等を有する。 FIG. 3 is a diagram showing a functional configuration example of the pattern extraction device 10 according to the first embodiment. In FIG. 3, the pattern extraction device 10 includes a pattern generation unit 11, an index calculation unit 12, a target function identification unit 13, a pattern extraction unit 14, an output unit 15, and the like.
 これら各部は、パターン抽出装置10にインストールされた1以上のプログラムが、CPU104に実行させる処理により実現される。パターン抽出装置10は、また、システムログ記憶部121及び実行回数記憶部122等を利用する。これら各記憶部は、例えば、補助記憶装置102、又はパターン抽出装置10にネットワークを介して接続可能な記憶装置等を用いて実現可能である。 Each of these parts is realized by a process of causing the CPU 104 to execute one or more programs installed in the pattern extraction device 10. The pattern extraction device 10 also uses the system log storage unit 121, the execution count storage unit 122, and the like. Each of these storage units can be realized by using, for example, a storage device that can be connected to the auxiliary storage device 102 or the pattern extraction device 10 via a network.
 第1の実施の形態では、情報システム等のコンピュータシステム(以下、単に「システム」という。)の機能の実行プロセスのパターン(以下、単に「パターン」という。)について、実行時期に着目した要改善パターンの抽出(探索)が行われる。機能の実行プロセスとは、例えば、業務上の或る作業(以下、「ケース」という。)において利用された機能の実行順を示す情報をいう。ケースごとに、利用される機能や、機能の実行順は異なる。例えば、機能A、B、C及びD等を含むシステムにおいて、或る作業(ケース1)では、A→B→Dが実行され、別の作業(ケース2)ではA→C→Dが実行される。この場合、「A→B→D」及び「A→C→D」のそれぞれが、機能の実行プロセスのパターン又は種別(以下、単に「パターン」という。)に相当する。但し、ケース(実行プロセス)とパターンとは、1対1に対応するものではない。例えば、ケースが相互に異なる複数のケースにおいて同じ実行プロセスが出現した場合、これら実行プロセスは、1つのパターンとして抽出される。 In the first embodiment, the pattern (hereinafter, simply referred to as “pattern”) of the function execution process of a computer system (hereinafter, simply referred to as “system”) such as an information system needs to be improved by focusing on the execution time. Pattern extraction (search) is performed. The function execution process means, for example, information indicating the execution order of functions used in a certain business work (hereinafter, referred to as “case”). The functions used and the order in which the functions are executed differ depending on the case. For example, in a system including functions A, B, C, D, etc., A → B → D is executed in a certain work (case 1), and A → C → D is executed in another work (case 2). NS. In this case, each of "A-> B-> D" and "A-> C-> D" corresponds to the pattern or type of the function execution process (hereinafter, simply referred to as "pattern"). However, the case (execution process) and the pattern do not have a one-to-one correspondence. For example, when the same execution process appears in a plurality of cases in which the cases are different from each other, these execution processes are extracted as one pattern.
 また、要改善パターンとは、複数のパターンのうち、改善が必要である可能性が相対的に高いパターンをいう。 In addition, the improvement-required pattern refers to a pattern in which there is a relatively high possibility that improvement is required among a plurality of patterns.
 本実施の形態では、単なる希少パターンではなく、実行時期に時間的特徴がある(偏りが有る)機能を含むパターン(以下、「時相特徴パターン」という。)が要改善パターンとして抽出(探索)される。特に、第1の実施の形態では、実行時期が限定的である(実行時期が限定されている)機能(以下、「対象機能」という。)を含むパターンが時相特徴パターンに該当し、要改善パターンとして抽出(探索)される。 In the present embodiment, not only a rare pattern but also a pattern including a function having a temporal characteristic (biased) in the execution time (hereinafter referred to as "temporal characteristic pattern") is extracted (search) as a pattern requiring improvement. Will be done. In particular, in the first embodiment, a pattern including a function having a limited execution time (execution time is limited) (hereinafter, referred to as “target function”) corresponds to a time phase feature pattern and is required. It is extracted (searched) as an improvement pattern.
 図4は、第1の実施の形態における要改善パターンの抽出の概要を説明するための図である。図4には、パターン1、パターン70~79、・・・、パターン80~89等が示されている。各パターンにおいて、A、B、C、E又はFの文字を含む角丸矩形は、機能を示す。 FIG. 4 is a diagram for explaining the outline of extraction of the improvement-requiring pattern in the first embodiment. FIG. 4, shows patterns 1, patterns 70 to 79, ..., Patterns 80 to 89, and the like. In each pattern, a rounded rectangle containing the letters A, B, C, E or F indicates a function.
 或る期間におけるこれらのパターンの実行回数が図示されている通り(それぞれ4回)である場合、パターン70~79及びパターン80~89のそれぞれの実行回数は、相対的に非常に少ない。すなわち、パターン70~79及びパターン80~89のそれぞれが希少パターンに相当する。なお、パターンの実行回数とは、システムの過去の利用における各パターンの実行実績に基づく実行回数である。また、図4において図示は省略されているが、パターン71~78の実行回数はそれぞれ4回であり、パターン81~88の実行回数はそれぞれ4回であるとする。 When the number of times these patterns are executed in a certain period is as shown in the figure (4 times each), the number of times each of patterns 70 to 79 and patterns 80 to 89 is executed is relatively very small. That is, each of patterns 70 to 79 and patterns 80 to 89 corresponds to a rare pattern. The number of times the pattern is executed is the number of times the pattern is executed based on the execution record of each pattern in the past use of the system. Although not shown in FIG. 4, it is assumed that the patterns 71 to 78 are executed four times each, and the patterns 81 to 88 are executed four times each.
 また、当該或る期間における各機能の実行回数(各機能の各パターンでの実行回数の総和)が、降順で以下の通りであるとする。
A:200回
B:200回

E:40回
F:40回
 この場合、希少パターンに含まれる機能E及び機能Fのそれぞれの実行回数は共に40回であるが、この値は他の機能の実行回数と比較して微少であるとはいえない。また、機能Eは、パターン70~79にのみ含まれ、機能Fは、パターン80~89にのみ含まれるとしても、機能E及び機能Fの出現パターン数は、それぞれ10であり、必ずしも微少ではない。
Further, it is assumed that the number of times each function is executed in the certain period (the total number of times each function is executed in each pattern) is as follows in descending order.
A: 200 times B: 200 times:
E: 40 times F: 40 times In this case, the number of times each of the function E and the function F included in the rare pattern is executed is 40 times, but this value is insignificant as compared with the number of times the other functions are executed. Not really. Further, even if the function E is included only in the patterns 70 to 79 and the function F is included only in the patterns 80 to 89, the number of appearance patterns of the function E and the function F is 10, respectively, which is not necessarily insignificant. ..
 上記に鑑みれば、希少パターンであることを除き、パターン70~89には、要改善と判定すべき特別な特徴は見られないとも考えられる。 In view of the above, it is considered that patterns 70 to 89 do not have any special features that should be judged to require improvement, except that they are rare patterns.
 ここで、機能Eの月別の実行回数及び機能Fの月別の実行回数が図5に示される通りであるとする。 Here, it is assumed that the number of monthly executions of function E and the number of monthly executions of function F are as shown in FIG.
 図5は、機能E及び機能Fの月別の実行回数を示す図である。図5には、表及び棒グラフの形式で、機能E及びFそれぞれについて、10月~3月の6ヶ月間の月別の実行回数が示されている。 FIG. 5 is a diagram showing the number of executions of the function E and the function F by month. FIG. 5 shows the number of executions by month for the six months from October to March for each of the functions E and F in the form of a table and a bar graph.
 当該表及び当該グラフによれば、機能Eは、毎月ほぼ同じ回数(6回又は7回)実行されているのに対し、機能Fは3月のみに40回も実行されている。そうすると、機能Fの実行時期は限定的であり、対象機能に該当するといえる。したがって、対象機能を含むパターン80~89のそれぞれは、時相特徴パターンに該当し、要改善パターンの候補となる。なお、要改善パターンの改善の一例として、要改善パターンは実行時期が限定的なので手運用に戻したり、機能Fを削除したり、機能Fを他の機能で代替したりするといったことが挙げられる。 According to the table and the graph, the function E is executed almost the same number of times (6 or 7 times) every month, while the function F is executed 40 times only in March. Then, the execution time of the function F is limited, and it can be said that the function F corresponds to the target function. Therefore, each of the patterns 80 to 89 including the target function corresponds to the time phase feature pattern and is a candidate for the improvement-required pattern. As an example of improvement of the improvement-required pattern, since the execution time of the improvement-required pattern is limited, it is possible to return to manual operation, delete the function F, or replace the function F with another function. ..
 以下、第1の実施の形態においてパターン抽出装置10が実行する処理手順について説明する。図6は、第1の実施の形態におけるパターン抽出装置10が実行する処理手順の一例を説明するためのフローチャートである。 Hereinafter, the processing procedure executed by the pattern extraction device 10 in the first embodiment will be described. FIG. 6 is a flowchart for explaining an example of the processing procedure executed by the pattern extraction device 10 in the first embodiment.
 ステップS101において、パターン生成部11は、システムログ記憶部121に記憶されているシステムログに基づいて、パターンを抽出する。 In step S101, the pattern generation unit 11 extracts a pattern based on the system log stored in the system log storage unit 121.
 図7は、システムログの構成例を示す図である。本実施の形態では、システムログを構成する各行を「ログデータ」という。ログデータは、システムの1つの機能が実行されるたびにシステムログ記憶部121に記録される(システムログに追加される)。 FIG. 7 is a diagram showing a configuration example of the system log. In the present embodiment, each line constituting the system log is referred to as "log data". The log data is recorded in the system log storage unit 121 (added to the system log) each time one function of the system is executed.
 図7において、各ログデータは、ケースID、機能名及び日時等を含む。ケースIDは、システムを利用した作業ごとに一意な識別情報である。機能名は、ケースIDに係るケースにおいて実行された機能の識別情報である。日時は、当該機能が実行された日時である。 In FIG. 7, each log data includes a case ID, a function name, a date and time, and the like. The case ID is unique identification information for each work using the system. The function name is identification information of the function executed in the case related to the case ID. The date and time is the date and time when the function was executed.
 ケースIDが共通するログデータ群の機能名の並びが機能の実行プロセスに相当する。したがって、システムログは、機能の実行プロセスの履歴であるともいえる。 The sequence of function names of the log data group with the same case ID corresponds to the function execution process. Therefore, it can be said that the system log is a history of the function execution process.
 ステップS101では、機能の実行プロセスの種別が、パターンとして抽出される。例えば、図7には、ケースIDが「W」であるログデータ群が示す機能の実行プロセス(A→B→C)がパターン1として抽出され、ケースIDが「W」であるログデータ群が示す機能の実行プロセス(A→E→B→C)がパターン70として抽出され、ケースIDが「W」であるログデータ群が示す機能の実行プロセス(A→F→B→C)がパターン80として抽出される例が示されている。これらのパターン1、70及び80は、図4に示したパターン1、70及び80に対応する。 In step S101, the type of the function execution process is extracted as a pattern. For example, in FIG. 7, the execution process (A → B → C) of the function indicated by the log data group whose case ID is “W 1 ” is extracted as pattern 1, and the log data whose case ID is “W m” is extracted. The function execution process (A → E → B → C) indicated by the group is extracted as the pattern 70, and the function execution process (A → F → B → C) indicated by the log data group whose case ID is “W n”. Is shown as an example in which is extracted as pattern 80. These patterns 1, 70 and 80 correspond to the patterns 1, 70 and 80 shown in FIG.
 なお、システムログからのパターンの抽出は、公知技術(例えば、特許文献1や特開2017-187953号公報等において開示された技術)を用いて行われればよい。 The pattern can be extracted from the system log by using a known technique (for example, the technique disclosed in Patent Document 1 and JP-A-2017-187953).
 続いて、パターン生成部11は、抽出したパターンごとに、当該パターンの実行回数と、当該パターンにおける各機能の実行回数とを実行回数記憶部122に記録する(S102)。 Subsequently, the pattern generation unit 11 records the number of executions of the pattern and the number of executions of each function in the pattern in the execution number storage unit 122 for each extracted pattern (S102).
 図8は、実行回数記憶部122の構成例を示す図である。図8には、行方向にパターンが割り当てられ、列方向にパターンの実行回数及び機能ごとの実行回数(機能実行回数)が割り当てられた表が、実行回数記憶部122の構成の一例として示されている。各行における機能実行回数は、当該行に対応するパターンにおいて各機能が実行された回数である。 FIG. 8 is a diagram showing a configuration example of the execution number storage unit 122. FIG. 8 shows a table in which patterns are assigned in the row direction and the number of times the pattern is executed and the number of times each function is executed (number of times the function is executed) are assigned in the column direction as an example of the configuration of the execution number storage unit 122. ing. The number of function executions in each line is the number of times each function is executed in the pattern corresponding to the line.
 例えば、パターン生成部11は、システムログから各パターンを抽出する際に、システムログ内において各パターンに一致する機能の実行プロセスをカウント(計数)することで、各パターンの実行回数を得ることができる。 For example, when the pattern generation unit 11 extracts each pattern from the system log, the pattern generation unit 11 can obtain the number of executions of each pattern by counting the execution process of the function matching each pattern in the system log. can.
 また、パターン生成部11は、パターンごとに、当該パターンに分類された実行シーケンスにおける各機能の実行回数を集計することで、当該パターンにおける機能実行回数を得ることができる。 Further, the pattern generation unit 11 can obtain the number of function executions in the pattern by totaling the number of times each function is executed in the execution sequence classified into the pattern for each pattern.
 続いて、指標計算部12は、システムログ(図7)を参照して、各機能について連続する時間区間(Time Bucket)ごとの実行回数を計算する(S103)。 Subsequently, the index calculation unit 12 calculates the number of executions for each continuous time interval (Time Bucket) for each function with reference to the system log (FIG. 7) (S103).
 図9は、各機能の時間区間ごとの実行回数の計算結果の一例を示す図である。図9には、機能ごとに時系列において連続する時間区間(単位期間)ごとの実行回数が示されている。図9の例において、1つの時間区間は1ヶ月である。したがって、図9には、各機能の月別の実行回数が示されている。指標計算部12は、或る機能の或る時間区間(或る年の或る月)について、「機能名」の値が当該機能の機能名であり、かつ、「日時」の値が当該月に属するログデータの数を集計することで、当該時間区間の実行回数を計算する。 FIG. 9 is a diagram showing an example of the calculation result of the number of executions of each function for each time interval. FIG. 9 shows the number of executions for each continuous time interval (unit period) in the time series for each function. In the example of FIG. 9, one time interval is one month. Therefore, FIG. 9 shows the number of times each function is executed by month. In the index calculation unit 12, the value of the "function name" is the function name of the function and the value of the "date and time" is the month for a certain time interval (a certain month of a certain year) of a certain function. By totaling the number of log data belonging to, the number of executions in the time interval is calculated.
 続いて、対象機能特定部13は、指標計算部12による計算結果(図9)に基づいて、各機能について実行時期が限定的であるか否かを判定し、実行時期が限定的である機能(対象機能)を特定する(S104)。例えば、実行回数が1以上である時間区間の数(以下、「実行期間数」という。)に対する閾値を1とし、実行期間数が閾値以下であることが、実行時期が限定的であることの条件とされてもよい。 Subsequently, the target function specifying unit 13 determines whether or not the execution time of each function is limited based on the calculation result (FIG. 9) by the index calculation unit 12, and the function whose execution time is limited. (Target function) is specified (S104). For example, when the threshold value for the number of time intervals in which the number of executions is 1 or more (hereinafter referred to as "the number of execution periods") is 1, and the number of execution periods is not more than the threshold value, the execution time is limited. It may be a condition.
 図10は、実行時期が限定的であるか否かの判定結果の一例を示す図である。図10には、図9に示した表に対して「時期限定判定」の列が追加されている。「時期限定判定」は、実行時期が限定的であるか否かの判定結果を示す項目である。当該項目の値は、1又は0である。1は、実行時期が限定的であるとの判定結果を示す。0は、実行時期が限定的でないとの判定結果を示す。 FIG. 10 is a diagram showing an example of a determination result of whether or not the execution time is limited. In FIG. 10, a column of “time-limited determination” is added to the table shown in FIG. The "time-limited determination" is an item indicating a determination result of whether or not the execution time is limited. The value of the item is 1 or 0. 1 indicates a determination result that the execution time is limited. 0 indicates a determination result that the execution time is not limited.
 続いて、パターン抽出部14は、各パターンについて、対象機能の含有数をカウント(計数)する(S105)。パターンごとの対象機能の含有数は、実行回数記憶部122(図8)を参照して特定することができる。なお、パターンにおける対象機能の含有数とは、当該パターンが含む対象機能の種類の数をいう。したがって、例えば、対象機能が、機能Fの1種類である場合、当該含有数の最大値は1となる。 Subsequently, the pattern extraction unit 14 counts (counts) the content of the target function for each pattern (S105). The content number of the target function for each pattern can be specified by referring to the execution number storage unit 122 (FIG. 8). The number of target functions contained in the pattern means the number of types of target functions included in the pattern. Therefore, for example, when the target function is one type of the function F, the maximum value of the content is 1.
 具体的には、機能Fが対象機能である場合、パターン抽出部14は、図8において機能Fの機能実行回数が1以上であるパターンを特定する。パターン抽出部14は、当該パターンの対象機能の含有数を1とする。一方、機能Fの機能実行回数が空であるパターンについての対象機能の含有数は0とされる。 Specifically, when the function F is the target function, the pattern extraction unit 14 specifies a pattern in which the number of times the function F is executed is 1 or more in FIG. The pattern extraction unit 14 sets the content number of the target function of the pattern to 1. On the other hand, the content number of the target function for the pattern in which the function execution count of the function F is empty is set to 0.
 図11は、第1の実施の形態における対象機能の含有数のカウント結果の一例を示す図である。図11は、実行回数記憶部122の記憶内容が図8に示される通りであって、かつ、機能Fが対象機能である場合の含有数のカウント結果を示す。 FIG. 11 is a diagram showing an example of the counting result of the content number of the target function in the first embodiment. FIG. 11 shows the counting result of the content when the storage content of the execution number storage unit 122 is as shown in FIG. 8 and the function F is the target function.
 すなわち、図8において、パターン80~89のそれぞれについては、機能Fの機能実行回数が1以上である。したがって、図11において、パターン80~89のそれぞれに対する「対象機能の含有数」の値は、1とされており、これら以外のパターンに対する「対象機能の含有数」の値は、0とされている。 That is, in FIG. 8, for each of the patterns 80 to 89, the number of times the function F is executed is 1 or more. Therefore, in FIG. 11, the value of the "content of the target function" for each of the patterns 80 to 89 is set to 1, and the value of the "content of the target function" for the patterns other than these is set to 0. There is.
 続いて、パターン抽出部14は、「対象機能の含有数」が1以上であるパターン番号に係るパターンを要改善パターンとして抽出する(S106)。 Subsequently, the pattern extraction unit 14 extracts a pattern related to the pattern number in which the "content number of the target function" is 1 or more as a pattern requiring improvement (S106).
 続いて、出力部15は、要改善パターンを出力する(S107)。例えば、要改善パターンのパターン番号が出力されてもよい。この際、「対象機能の含有数」の値が優先度付けに利用されてもよい。例えば、「対象機能の含有数」の値の降順にパターン番号が出力されてもよい。出力部15は、また、出力されるパターン番号に対応付けて、当該パターン番号に係るパターンに含まれる対象機能の機能名を出力してもよい。なお、出力形態は、所定のものに限定されない。例えば、表示装置106への表示であってもよいし、補助記憶装置102への保存であってもよいし、他の装置への送信であってもよい。 Subsequently, the output unit 15 outputs a pattern requiring improvement (S107). For example, the pattern number of the pattern requiring improvement may be output. At this time, the value of "content number of target function" may be used for prioritization. For example, the pattern number may be output in descending order of the value of "content number of target function". The output unit 15 may also output the function name of the target function included in the pattern related to the pattern number in association with the output pattern number. The output form is not limited to the predetermined one. For example, it may be displayed on the display device 106, stored in the auxiliary storage device 102, or transmitted to another device.
 ユーザは、出力された情報を参照して、要改善パターン及び要改善機能を特定することができる。 The user can identify the improvement-requiring pattern and the improvement-requiring function by referring to the output information.
 上述したように、第1の実施の形態によれば、希少パターンの中から、要改善パターンを自動的に抽出することができる。すなわち、システムの機能の実行プロセスのパターンのうち改善が必要な機能を含むパターンを効率的に抽出することができる。 As described above, according to the first embodiment, the pattern requiring improvement can be automatically extracted from the rare patterns. That is, it is possible to efficiently extract patterns including functions that need improvement from the patterns of the system function execution process.
 次に、第2の実施の形態について説明する。第2の実施の形態では第1の実施の形態と異なる点について説明する。第2の実施の形態において特に言及されない点については、第1の実施の形態と同様でもよい。 Next, the second embodiment will be described. The second embodiment will be described which is different from the first embodiment. The points not particularly mentioned in the second embodiment may be the same as those in the first embodiment.
 第2の実施の形態では、時間区間ごとの実行回数の時系列順の変動(隣接する時間区間における実行回数の変動)が大きな機能(以下、「対象機能」という。)が、実行時期に時間的特徴がある(偏りが有る)機能の一例とされる例について説明する。 In the second embodiment, a function (hereinafter referred to as "target function") having a large variation in the number of executions in each time interval (variation in the number of executions in an adjacent time interval) is performed at the execution time. An example of a function having a specific feature (biased) will be described.
 図12は、第2の実施の形態における要改善パターンの抽出の概要を説明するための図である。図12には、パターン70~89の代わりにパターン90~99が示されている。 FIG. 12 is a diagram for explaining an outline of extraction of the improvement-required pattern in the second embodiment. In FIG. 12, patterns 90 to 99 are shown instead of patterns 70 to 89.
 或る期間におけるこれらのパターンの実行回数が図示されている通りであり、かつ、当該期間における各機能の実行回数は、降順で以下の通りであるとする。
A:200回
B:200回

G:40回
 上記によれば、機能Gの実行回数(40回)は微少ではなく、機能Gは、一定数(パターン90~パターン99の10個)のパターンに含まれている。
It is assumed that the number of times these patterns are executed in a certain period is as shown in the figure, and the number of times each function is executed in the period is as follows in descending order.
A: 200 times B: 200 times:
G: 40 times According to the above, the number of times the function G is executed (40 times) is not very small, and the function G is included in a fixed number of patterns (10 patterns 90 to 99).
 ここで、機能E及び機能Gの月別の実行回数及び当該実行回数の変動率が図13に示される通りであるとする。 Here, it is assumed that the number of monthly executions of the functions E and G and the fluctuation rate of the number of executions are as shown in FIG.
 図13は、機能E及び機能Gの月別の実行回数及び当該実行回数の変動率の一例を示す図である。図13には、機能E及び機能Gのそれぞれについて、10月~3月の6ヶ月間の月別(時間区間別)の実行回数を示す表及びグラフと、月別の実行回数の変動率(隣接する時間区間の実行回数の変動率)を時系列順に示す表とが示されている。なお、或る月の当該変動率は、以下の式によって計算される。
変動率=(当月の実行回数/前月の実行回数)-1
 図13によれば、機能Gは、機能Eと同様に毎月実行されている。したがって、第1の実施の形態では、機能Gは、対象機能として特定されない。
FIG. 13 is a diagram showing an example of the monthly execution number of the function E and the function G and the fluctuation rate of the execution number. FIG. 13 shows a table and a graph showing the number of executions by month (by time interval) for 6 months from October to March for each of function E and function G, and the fluctuation rate of the number of executions by month (adjacent). A table showing the volatility of the number of executions in the time interval) in chronological order is shown. The volatility for a given month is calculated by the following formula.
Volatility = (number of executions in the current month / number of executions in the previous month) -1
According to FIG. 13, the function G is executed every month like the function E. Therefore, in the first embodiment, the function G is not specified as a target function.
 一方で、機能Gの12月の変動率(11月と12月との変動率)は1400%と突出しており、12月において瞬間的に機能Gの実行回数が増加している(12月のみ実行回数の上昇が激しい)ことが分かる。すなわち、機能Gは、時間区間ごとの時系列順の実行回数の変動が大きな機能(対象機能)に該当する。 On the other hand, the fluctuation rate of function G in December (the fluctuation rate between November and December) stands out at 1400%, and the number of executions of function G increases momentarily in December (only in December). It can be seen that the number of executions increases sharply). That is, the function G corresponds to a function (target function) in which the number of executions in chronological order for each time interval fluctuates greatly.
 したがって、第2の実施の形態において、対象機能である機能Gを含むパターン90~9は、時相特徴パターンに該当し、要改善パターンとして抽出される。なお、要改善パターンの改善の一例として、要改善パターンについて、瞬間的に増加する時期のみ速度改善(スケールアウト)を実施する等が挙げられる。 Therefore, in the second embodiment, the patterns 90 to 9 including the function G, which is the target function, correspond to the time phase feature pattern and are extracted as the improvement-required pattern. As an example of the improvement of the improvement-requiring pattern, the speed improvement (scale-out) is performed only when the improvement-requiring pattern increases momentarily.
 図14は、第2の実施の形態におけるパターン抽出装置10が実行する処理手順の一例を説明するためのフローチャートである。図14中、図6と同一ステップには、同一ステップ番号を付し、その説明は省略する。 FIG. 14 is a flowchart for explaining an example of the processing procedure executed by the pattern extraction device 10 in the second embodiment. In FIG. 14, the same steps as those in FIG. 6 are assigned the same step numbers, and the description thereof will be omitted.
 ステップS204において、指標計算部12は、ステップS103における計算結果(図9)に基づいて、各機能の各時間区間の実行回数について、直前の実行区間の実行回数に対する変動率を計算する(S204)。 In step S204, the index calculation unit 12 calculates the fluctuation rate of the number of executions of each time interval of each function with respect to the number of executions of the immediately preceding execution section based on the calculation result (FIG. 9) in step S103 (S204). ..
 図15は、各機能の時間区間ごとの実行回数の変動率の計算結果の一例を示す図である。図15には、機能ごとに、時間区間ごとの実行回数の時系列順の変動率の計算結果が示されている。なお、変動率の計算方法は、上記した通りである。 FIG. 15 is a diagram showing an example of the calculation result of the fluctuation rate of the number of executions of each function for each time interval. FIG. 15 shows the calculation result of the volatility of the number of executions for each time interval in chronological order for each function. The method of calculating the volatility is as described above.
 続いて、対象機能特定部13は、指標計算部12による計算結果(図15)に基づいて、各機能について過大変動機能の有無を判定し、過大変動機能に該当する機能(対象機能)を特定する(S205)。例えば、変動率の絶対値に対する閾値を1000%とし、変動率の絶対値が閾値以上(すなわち、変動率が+1000%以上又は-1000%以下)である機能が、過大変動機能であることの条件とされてもよい。 Subsequently, the target function specifying unit 13 determines the presence or absence of the over-variation function for each function based on the calculation result (FIG. 15) by the index calculation unit 12, and identifies the function (target function) corresponding to the over-variation function. (S205). For example, the condition that the threshold value with respect to the absolute value of the volatility is 1000% and the absolute value of the volatility is equal to or more than the threshold value (that is, the volatility is + 1000% or more or -1000% or less) is the excessive fluctuation function. May be.
 図16は、過大変動機能であるか否かの判定結果の一例を示す図である。図16には、図15に示した表に対して「過大変動判定」の列が追加されている。「過大変動判定」は、過大変動機能であるか否かの判定結果を示す項目である。当該項目の値は、1又は0である。1は、過大変動機能であるとの判定結果を示す。0は、過大変動機能でないとの判定結果を示す。 FIG. 16 is a diagram showing an example of a determination result of whether or not the function is an excessive fluctuation function. In FIG. 16, a column of “excessive fluctuation determination” is added to the table shown in FIG. The "excessive fluctuation determination" is an item indicating a determination result of whether or not the function is an excessive fluctuation function. The value of the item is 1 or 0. 1 indicates a determination result that the function is an excessive fluctuation function. 0 indicates a determination result that the function is not an excessive fluctuation function.
 図16には、+1000%以上又は-1000%以下である変動率が有ることが過大変動機能であることの条件である場合の判定結果が示されている。したがって、変動率が1400%である機能Gについて、過大変動機能であると判定されている。すなわち、この場合、機能Gが、対象機能として特定される。 FIG. 16 shows the determination result when having a volatility of + 1000% or more or -1000% or less is a condition for the over-variation function. Therefore, the function G having a volatility of 1400% is determined to be an excessive volatility function. That is, in this case, the function G is specified as the target function.
 続いて、パターン抽出部14は、各パターンについて、対象機能の含有数をカウント(計数)する(S206)。パターンごとの対象機能の含有数は、図6のステップS105と同様に、実行回数記憶部122(図8)を参照して特定することができる。 Subsequently, the pattern extraction unit 14 counts (counts) the content of the target function for each pattern (S206). The content number of the target function for each pattern can be specified by referring to the execution number storage unit 122 (FIG. 8) as in step S105 of FIG.
 具体的には、機能Gが対象機能である場合、パターン抽出部14は、図8において機能Gの機能実行回数が1以上であるパターンを特定する。パターン抽出部14は、当該パターンの対象機能の含有数を1とする。一方、機能Gの機能実行回数が空であるパターンについての対象機能の含有数は0とされる。 Specifically, when the function G is the target function, the pattern extraction unit 14 specifies a pattern in which the number of times the function G is executed is 1 or more in FIG. The pattern extraction unit 14 sets the content number of the target function of the pattern to 1. On the other hand, the content number of the target function for the pattern in which the function execution count of the function G is empty is set to 0.
 図17は、第2の実施の形態における対象機能の含有数のカウント結果の一例を示す図である。図17は、実行回数記憶部122の記憶内容が図8に示される通りであって、かつ、機能Gが対象機能である場合の含有数のカウント結果を示す。 FIG. 17 is a diagram showing an example of the counting result of the content number of the target function in the second embodiment. FIG. 17 shows the counting result of the content when the storage content of the execution number storage unit 122 is as shown in FIG. 8 and the function G is the target function.
 すなわち、図8において、パターン90~99のそれぞれについては、機能Gの機能実行回数が1以上である。したがって、図17において、パターン90~99のそれぞれに対する「対象機能の含有数」の値は、1とされており、これら以外のパターンに対する「対象機能の含有数」の値は、0とされている。 That is, in FIG. 8, the number of times the function G is executed is 1 or more for each of the patterns 90 to 99. Therefore, in FIG. 17, the value of the "content of the target function" for each of the patterns 90 to 99 is set to 1, and the value of the "content of the target function" for the patterns other than these is set to 0. There is.
 以降は、第1の実施の形態と同様である。 After that, it is the same as the first embodiment.
 上述したように、第2の実施の形態によれば、第1の実施の形態において要改善パターンとして抽出することが困難な希少パターンの中から要改善パターンを抽出することができる。したがって、システムの機能の実行プロセスのパターンのうち改善が必要な機能を含むパターンを効率的に抽出することができる。 As described above, according to the second embodiment, it is possible to extract the improvement-requiring pattern from the rare patterns that are difficult to extract as the improvement-requiring pattern in the first embodiment. Therefore, among the patterns of the execution process of the functions of the system, the patterns including the functions that need improvement can be efficiently extracted.
 なお、第1の実施の形態と第2の実施の形態とが組み合われてもよい。例えば、図6の処理手順に続いて、図14の処理手順が実行されてもよい。この場合、第1の実施の形態における対象機能と、第2の実施の形態における対象機能とのいずれか一方、又は双方を含むパターンが、要改善パターンとして抽出されてもよい。 Note that the first embodiment and the second embodiment may be combined. For example, the processing procedure of FIG. 14 may be executed following the processing procedure of FIG. In this case, a pattern including either or both of the target function in the first embodiment and the target function in the second embodiment may be extracted as a pattern requiring improvement.
 また、上記各実施の形態では、機能の実行プロセス(パターン)を構成する各機能の実行主体(各機能を実行するユーザ)の区別については、便宜上、省略しているが、例えば、特許文献1における、スイムレーン付きの機能の実行プロセス(パターン)に対して、本実施の形態が適用されてもよい。 Further, in each of the above embodiments, the distinction between the execution subject (user who executes each function) of each function constituting the function execution process (pattern) is omitted for convenience, but for example, Patent Document 1 The present embodiment may be applied to the execution process (pattern) of the function with a swim lane in the above.
 なお、上記各実施の形態において、指標計算部12は、計算部の一例である。対象機能特定部13は、特定部の一例である。パターン抽出部14は、抽出部の一例である。 In each of the above embodiments, the index calculation unit 12 is an example of the calculation unit. The target function specifying unit 13 is an example of the specific unit. The pattern extraction unit 14 is an example of an extraction unit.
 以上、本発明の実施の形態について詳述したが、本発明は斯かる特定の実施形態に限定されるものではなく、請求の範囲に記載された本発明の要旨の範囲内において、種々の変形・変更が可能である。 Although the embodiments of the present invention have been described in detail above, the present invention is not limited to such specific embodiments, and various modifications are made within the scope of the gist of the present invention described in the claims.・ Can be changed.
10     パターン抽出装置
11     パターン生成部
12     指標計算部
13     対象機能特定部
14     パターン抽出部
15     出力部
100    ドライブ装置
101    記録媒体
102    補助記憶装置
103    メモリ装置
104    CPU
105    インタフェース装置
106    表示装置
107    入力装置
121    システムログ記憶部
122    実行回数記憶部
B      バス
10 Pattern extraction device 11 Pattern generation unit 12 Index calculation unit 13 Target function identification unit 14 Pattern extraction unit 15 Output unit 100 Drive device 101 Recording medium 102 Auxiliary storage device 103 Memory device 104 CPU
105 Interface device 106 Display device 107 Input device 121 System log storage unit 122 Execution count storage unit B bus

Claims (7)

  1.  システムの機能の実行プロセスの履歴に基づいて、前記機能ごとに当該機能の時間区間ごとの実行回数を計算する計算部と、
     前記時間区間ごとの実行回数に基づいて、実行時期に偏りが有る前記機能を特定する特定部と、
     前記実行プロセスのパターンの中から、前記特定部が特定した前記機能を含む前記パターンを前記履歴に基づいて抽出する抽出部と、
    を有することを特徴とするパターン抽出装置。
    A calculation unit that calculates the number of times the function is executed for each time interval for each function based on the history of the system function execution process.
    Based on the number of executions for each time interval, a specific part that identifies the function whose execution time is biased, and
    An extraction unit that extracts the pattern including the function specified by the specific unit from the patterns of the execution process based on the history.
    A pattern extraction device characterized by having.
  2.  前記特定部は、前記実行回数が1以上である前記時間区間の数が閾値以下である前記機能、及び隣接する時間区間の実行回数の変動率の絶対値が閾値以上である前記機能のうちのいずれか一方又は双方を特定する、
    ことを特徴とする請求項1記載のパターン抽出装置。
    The specific unit is one of the functions in which the number of executions is 1 or more and the number of time intervals is equal to or less than a threshold value, and the functions in which the absolute value of the fluctuation rate of the number of executions in adjacent time intervals is equal to or more than a threshold value. Identify one or both,
    The pattern extraction device according to claim 1.
  3.  前記抽出部は、前記パターンごとに、前記特定部が特定した前記機能の含有数をカウントし、
     前記抽出部が抽出した前記パターンを前記含有数に基づく優先度付けによって出力する出力部、
    を有することを特徴とする請求項1又は2記載のパターン抽出装置。
    The extraction unit counts the content of the function specified by the specific unit for each pattern.
    An output unit that outputs the pattern extracted by the extraction unit by prioritization based on the content.
    The pattern extraction device according to claim 1 or 2, wherein the pattern extraction device is characterized by having.
  4.  システムの機能の実行プロセスの履歴に基づいて、前記機能ごとに当該機能の時間区間ごとの実行回数を計算する計算手順と、
     前記時間区間ごとの実行回数に基づいて、実行時期に偏りが有る前記機能を特定する特定手順と、
     前記実行プロセスのパターンの中から、前記特定手順が特定した前記機能を含む前記パターンを前記履歴に基づいて抽出する抽出手順と、
    をコンピュータが実行することを特徴とするパターン抽出方法。
    Based on the history of the execution process of the function of the system, the calculation procedure for calculating the number of executions of the function for each time interval for each function, and the calculation procedure.
    A specific procedure for identifying the function whose execution time is biased based on the number of executions for each time interval, and
    An extraction procedure for extracting the pattern including the function specified by the specific procedure from the patterns of the execution process based on the history.
    A pattern extraction method characterized by a computer performing.
  5.  前記特定手順は、前記実行回数が1以上である前記時間区間の数が閾値以下である前記機能、及び隣接する時間区間の実行回数の変動率の絶対値が閾値以上である前記機能のうちのいずれか一方又は双方を特定する、
    ことを特徴とする請求項4記載のパターン抽出方法。
    The specific procedure is one of the functions in which the number of executions is 1 or more and the number of time intervals is equal to or less than a threshold value, and the functions in which the absolute value of the fluctuation rate of the number of executions in adjacent time intervals is equal to or more than a threshold value. Identify one or both,
    The pattern extraction method according to claim 4, wherein the pattern is extracted.
  6.  前記抽出手順は、前記パターンごとに、前記特定手順が特定した前記機能の含有数をカウントし、
     前記抽出手順が抽出した前記パターンを前記含有数に基づく優先度付けによって出力する出力部、
    を有することを特徴とする請求項4又は5記載のパターン抽出方法。
    In the extraction procedure, the content number of the function specified by the specific procedure is counted for each pattern.
    An output unit that outputs the pattern extracted by the extraction procedure by prioritizing based on the content.
    The pattern extraction method according to claim 4 or 5, wherein the pattern extraction method is characterized by having.
  7.  請求項1乃至3いずれか一項記載のパターン抽出装置としてコンピュータを機能させることを特徴とするプログラム。 A program characterized by operating a computer as the pattern extraction device according to any one of claims 1 to 3.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013003884A (en) * 2011-06-17 2013-01-07 Kddi Corp System and method for estimating workflow improvement required place
JP2020004113A (en) * 2018-06-28 2020-01-09 株式会社東芝 Information display device, information display program, and information display method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013003884A (en) * 2011-06-17 2013-01-07 Kddi Corp System and method for estimating workflow improvement required place
JP2020004113A (en) * 2018-06-28 2020-01-09 株式会社東芝 Information display device, information display program, and information display method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ABE, MARI ET AL.: "Process Discovery Job Analysis Using Job Index Threshold Value Calculation Method", IPSJ SIG TECHNICAL REPORT (CSEC)., vol. 2015 -CS, 26 February 2015 (2015-02-26), pages 1 - 6 *

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