WO2016204119A1 - Multi-dimensional data analysis assistance device - Google Patents

Multi-dimensional data analysis assistance device Download PDF

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Publication number
WO2016204119A1
WO2016204119A1 PCT/JP2016/067564 JP2016067564W WO2016204119A1 WO 2016204119 A1 WO2016204119 A1 WO 2016204119A1 JP 2016067564 W JP2016067564 W JP 2016067564W WO 2016204119 A1 WO2016204119 A1 WO 2016204119A1
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Prior art keywords
axis
recommended
item
value
axis item
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PCT/JP2016/067564
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French (fr)
Japanese (ja)
Inventor
謙一郎 水谷
Original Assignee
三菱電機ビルテクノサービス株式会社
三菱電機株式会社
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Application filed by 三菱電機ビルテクノサービス株式会社, 三菱電機株式会社 filed Critical 三菱電機ビルテクノサービス株式会社
Priority to DE112016002709.5T priority Critical patent/DE112016002709T5/en
Priority to CN201680034366.8A priority patent/CN107636703A/en
Priority to KR1020187001133A priority patent/KR20180017167A/en
Publication of WO2016204119A1 publication Critical patent/WO2016204119A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2457—Query processing with adaptation to user needs
    • G06F16/24578—Query processing with adaptation to user needs using ranking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION 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
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management

Definitions

  • the present invention relates to a multidimensional data analysis support apparatus that supports analysis of multidimensional data composed of a large number of data items.
  • the observed data is analyzed by a predetermined analysis procedure, and a report and a table in which the analysis result is shown are arranged in a designated layout. Is configured to create automatically.
  • Patent Document 1 a report showing the result of routine analysis by a predetermined analysis procedure is automatically created. Therefore, when analyzing multidimensional data, the method described in Patent Document 1 is used. Even when the conventional technique is applied, only a typical analysis result can be obtained.
  • the present invention has been made in order to solve the above-described problems, and an object of the present invention is to obtain a multidimensional data analysis support apparatus that realizes easy atypical analysis of multidimensional data.
  • the multidimensional data analysis support device includes an analysis input data, a recommended axis item, an x axis item, a y axis item, and a y axis item for each x axis item and recommended axis item from the analysis input data.
  • a multidimensional data analysis support apparatus to which a comparison condition in which a y-axis intermediate value and a y-axis aggregation formula for calculating a value are defined is input, and the comparison condition and recommended axis items are input from the analysis input data
  • the recommended axis-specific intermediate tabulation unit for calculating the y-axis intermediate value for each x-axis item and the recommended axis item, and the y-axis intermediate value for each of the x-axis item and the recommended axis item calculated by the recommended axis-specific intermediate tabulation unit.
  • a recommendation totaling unit that calculates the value of the y-axis item for each x-axis item and the recommended axis item as a y-axis recommended total value using a y-axis totaling formula, and the x-axis item and the recommended axis calculated by the recommendation totaling unit Y-axis recommended aggregate value for each item Et al.,
  • the correlation between the x-axis item and a y-axis recommendation aggregate value comprising: a data output unit that outputs for each recommendation axis item, a.
  • the y-axis recommended aggregate value is calculated for each x-axis item and recommended axis item from the input data for analysis according to the set comparison condition and recommended axis item, and the calculated x-axis item and recommended axis are calculated.
  • the correlation between the x-axis item and the y-axis recommended total value is output for each recommended axis item.
  • FIG. 1 It is a block diagram which shows the structure of the multidimensional data analysis assistance apparatus in Embodiment 1 of this invention. It is explanatory drawing which shows an example of the table of failure information DB of FIG. It is explanatory drawing which shows an example of the table of contract information DB of FIG. It is explanatory drawing which shows an example of the comparison conditions of FIG. It is explanatory drawing which shows an example of the recommendation axis
  • FIG. 1 is a block diagram showing a configuration of a multidimensional data analysis support apparatus 1 according to Embodiment 1 of the present invention.
  • the multidimensional data analysis support device 1 analyzes the input input data for analysis according to the input comparison condition 4, the recommended axis item 5 and the recommendation method 6, and Output analysis results.
  • the multidimensional data analysis support apparatus 1 is realized by, for example, a CPU that executes a program stored in a memory and a processing circuit such as a system LSI. Each database (DB) in which various data is stored is stored in a memory.
  • DB database
  • each input element to the multidimensional data analysis support apparatus 1 will be described.
  • a case where data included in each of the failure information DB 2 and the contract information DB 3 is input to the multidimensional data analysis support apparatus 1 as analysis input data to be analyzed is illustrated.
  • FIG. 2 is an explanatory diagram showing an example of a table of the failure information DB 2 of FIG.
  • the failure information DB 2 includes a table relating to failure information of the maintenance target device. Specifically, the table in FIG. 2 associates failure ID, contract ID, failure time, and classification item X as data items.
  • the failure ID indicates an identifier for identifying the failure.
  • the contract ID indicates an identifier for identifying the content of the maintenance contract for maintaining the maintenance target device in which the failure corresponding to the failure ID has occurred.
  • the failure time indicates the date on which the failure corresponding to the failure ID occurred.
  • the classification item X indicates what kind of failure is classified into the failure ID.
  • the failure ID is failure 0001
  • the content of the maintenance contract corresponding to the failure ID is contract 00002
  • the failure time corresponding to the failure ID is August 2014. This indicates that the failure type corresponding to the failure ID is class X01.
  • FIG. 3 is an explanatory diagram showing an example of a table of the contract information DB 3 in FIG.
  • the contract information DB 3 includes a table related to maintenance contract information of maintenance target devices. Specifically, the table of FIG. 3 associates a contract ID, a contract time, a model, and a classification item Y as data items.
  • the contract ID indicates an identifier for identifying the content of the maintenance contract of the maintenance target device.
  • the contract time indicates the date on which the maintenance contract corresponding to the contract ID is concluded.
  • the model indicates the model of the maintenance target device corresponding to the contract ID.
  • the classification item Y indicates what type the maintenance contract corresponding to the contract ID is classified.
  • the contract ID is contract 00001
  • the maintenance contract corresponding to the contract ID is set to April 1, 2010, and the maintenance target corresponding to the contract ID is shown in FIG. This indicates that the model of the device is model 1, and the type of maintenance contract corresponding to the contract ID is classification Y02.
  • the multidimensional data analysis support apparatus 1 receives multidimensional data related to the maintenance work of the maintenance target device as shown in FIGS. 2 and 3 as input data for analysis.
  • FIG. 4 is an explanatory diagram showing an example of the comparison condition 4 in FIG.
  • the comparison condition 4 includes an x-axis item, a y-axis item, a y-axis aggregation formula, a first y-axis intermediate value, a first composite function, a second y-axis intermediate value, and a second A composite function and a narrowing condition are defined.
  • Each item of the comparison condition 4 is set by the user.
  • parameters for analyzing the correlation between two parameters that can be derived from the input data for analysis are set in each item of the x-axis item and the y-axis item.
  • the case where the failure occurrence month is set as the x-axis item and the failure rate is set as the y-axis item is illustrated. ing.
  • the first y-axis intermediate value and the second y-axis intermediate value item are parameters necessary for calculating the value of the y-axis item for each x-axis item and recommended axis item 5 described later from the input data for analysis. Is set. Here, it is necessary to calculate a failure rate for each failure month and model from data included in each of the failure information DB 2 and the contract information DB 3.
  • the y-axis intermediate value for calculating the value of the y-axis item for each of the x-axis item and the recommended axis item 5 from the input data for analysis 2 of the first y-axis intermediate value and the second y-axis intermediate value.
  • Two parameters are set. Specifically, the case where the number of failure information DB2 is set as the first y-axis intermediate value and the number of contract information DB3 is set as the second y-axis intermediate value is illustrated.
  • a function necessary for calculating the value of the y-axis item for each x-axis item and the recommended axis item 5 from the input data for analysis is set.
  • a function for calculating the value of the y-axis item for each x-axis item and recommended axis item 5 from the first y-axis intermediate value and the second y-axis intermediate value is set as the y-axis aggregation formula. .
  • [first y-axis intermediate value] / [second y-axis intermediate value] is set as a function of the y-axis aggregation formula is illustrated.
  • a function for synthesizing the first y-axis intermediate value for each x-axis item and the recommended axis item 5 for each same value of the x-axis item is set.
  • an addition function is set as the first synthesis function.
  • a function for synthesizing the second y-axis intermediate value for each x-axis item and the recommended axis item 5 for each same value of the x-axis item is set.
  • an addition function is set as the second synthesis function.
  • Conditions for narrowing down the number of input data for analysis are set in the filtering conditions.
  • a condition for analyzing the classification item X using data belonging to the classification X01 in the data included in the failure information DB 2 is set as a narrowing condition.
  • the failure belonging to the classification X01 is analyzed.
  • FIG. 5 is an explanatory diagram showing an example of the recommended axis item 5 in FIG.
  • FIG. 6 is an explanatory diagram showing an example of the recommendation method 6 of FIG.
  • Each item of the recommendation axis item 5 and the recommendation method 6 is set by the user.
  • each item of the recommended axis item 5 and the recommendation method 6 is specified based on the correlation between the two parameters set in the x axis item and the y axis item of the comparison condition 4. Parameters for further analysis of the tendency are set. Note that a plurality of recommended axis items 5 can be set instead of one. In this example, assuming that the user wants to know a model whose failure is increasing, the model is set as the recommended axis item 5 and the increasing tendency is set as the recommendation method 6. . In this case, an increasing tendency of the failure rate with respect to the failure occurrence month is quantitatively analyzed for each model of the maintenance target device.
  • the multidimensional data analysis support apparatus 1 includes the comparison condition 4, the recommended axis item 5 and the recommendation method 6 corresponding to the conditions for analyzing the input data for analysis as shown in FIGS. Is also input together with the input data for analysis.
  • Various parameters can be set for each of the x-axis item, the y-axis item, the recommended axis item 5 and the recommendation method 6 depending on the content of the input data for analysis.
  • a year or a branch office can be set.
  • a y-axis item for example, repair work time, repair cost, or failure stop time (MTTR) can be set.
  • MTTR failure stop time
  • a contract form, a failure part, a failure phenomenon, a failure cause, a facility application, an installation environment, a management department, or an installation year can be set.
  • a decreasing tendency, a tendency of a mean value magnitude, a rapidly increasing tendency, a rapidly decreasing tendency, or a seasonal fluctuation tendency can be set.
  • a case where a model and an increasing tendency are combined is illustrated.
  • various combinations can be set as combinations of parameters that can be set in the recommended axis item 5 and parameters that can be set in the recommendation method 6.
  • the contract form is set in the recommended axis item 5 and the decreasing tendency is set in the recommendation method 6, the decreasing tendency of the failure rate with respect to the failure occurrence month is quantitatively analyzed for each contract form of the maintenance target device. Become.
  • the first y-axis intermediate value and the second y-axis intermediate value in the comparison condition 4 according to the contents of the input data for analysis and the contents of the parameters set in the x-axis item, the y-axis item, and the recommended axis item 5 Needless to say, the setting contents of the value, the y-axis aggregation formula, the first synthesis function, the second synthesis function, and the narrowing-down condition are appropriately changed.
  • the multidimensional data analysis support device 1 includes a recommended axis-specific intermediate totaling unit 11, an intermediate totaling value DB 12, an integrated totaling unit 13, a recommended totaling unit 14, an integrated totaling value DB 15, a recommended totaling value DB 16, and a recommended evaluation value DB 17. And a data output unit 18.
  • the recommended axis-by-recommended intermediate totaling unit 11 performs the first operation for each x-axis item and recommended axis item 5 according to the comparison condition 4 and the recommended axis item 5 from the failure information DB 2 and the contract information DB 3 input as input data for analysis.
  • the y-axis intermediate value and the second y-axis intermediate value are calculated, and the calculation result is stored in the intermediate total value DB 12.
  • the recommended axis-specific intermediate totaling unit 11 associates each of the failure information DB 2 and the contract information DB 3 with a contract ID, and sets the failure occurrence month set as the x-axis item and the model set as the recommended axis item 5.
  • the number of faults belonging to the classification X01 counted in (1) is defined as the first y-axis intermediate value.
  • the recommended axis-specific intermediate totaling unit 11 sets the number of maintenance contracts counted for each model from the contract information DB 3 as the second y-axis intermediate value.
  • FIG. 7 is an explanatory diagram showing an example of a table of the intermediate summary value DB 12 of FIG.
  • the intermediate total value DB 12 includes a table related to the calculation result by the recommended axis-specific intermediate total unit 11. Specifically, the table of FIG. 7 associates, as data items, an x-axis item, a recommended axis item 5, a first y-axis intermediate value, and a second y-axis intermediate value.
  • the data in the first row indicates that the number of failures that occurred in model 1 in January is 35 and the number of maintenance contracts for model 1 is 5001.
  • the integrated totaling unit 13 calculates the x-axis item stored in the intermediate total value DB 12 and the first y-axis intermediate value and the second y-axis intermediate value for each recommended axis item 5 from the x-axis item.
  • the value of the y-axis item is calculated as a y-axis integrated total value using the first composite function, the second composite function, and the y-axis total formula, and the calculation result is stored in the integrated total value DB 15.
  • the first addition value is calculated by adding the first y-axis intermediate value for each failure month and each model using the addition function for each failure month.
  • the second added value is calculated by adding the second y-axis intermediate value for each failure occurrence month and each model using the addition function for each failure occurrence month.
  • a value obtained by dividing the first addition value by the second addition value for each failure occurrence month is set as a y-axis integrated total value.
  • FIG. 8 is an explanatory diagram showing an example of a table of the integrated total value DB 15 of FIG.
  • the integrated total value DB 15 includes a table related to the calculation result by the integrated total unit 13. Specifically, the table of FIG. 8 associates x-axis items and y-axis integrated aggregate values as data items.
  • the data in the first row indicates that the failure rate occurring in all models when the failure occurrence month is January is 0.0055.
  • the recommendation totaling unit 14 calculates x values from the first y-axis intermediate value and the second y-axis intermediate value for each x-axis item and recommended axis item 5 stored in the intermediate total value DB 12.
  • the value of the y-axis item is calculated as a y-axis recommended total value using the y-axis totaling formula, and the calculation result is stored in the recommended total value DB 16.
  • a value obtained by dividing the first y-axis intermediate value by the second y-axis intermediate value for each failure month and model is set as the y-axis recommended total value.
  • FIG. 9 is an explanatory diagram showing an example of a table of the recommended total value DB 16 of FIG.
  • the recommended total value DB 16 includes a table relating to the calculation result of the y-axis recommended total value by the recommendation totaling unit 14. Specifically, the table of FIG. 9 associates x-axis items, recommended axis items 5, and y-axis recommended aggregate values as data items.
  • the recommendation totaling unit 14 further calculates a recommended evaluation value for each recommended axis item 5 according to the recommendation method 6 from the calculated x-axis item and the y-axis recommended total value for each recommended axis item 5. Then, the calculation result is stored in the recommended evaluation value DB 17. Specifically, a recommended evaluation value is calculated for each model using a statistical formula corresponding to the recommendation method 6.
  • the recommendation method 6 is set as an increasing tendency, in order to quantify the increasing tendency of the failure rate for each model, as a statistical formula corresponding to the increasing tendency, for example, the following formula (1) Can be used.
  • a i is the y-axis recommended total value for i month of model N
  • B i is an integer i (sequence increasing by 1)
  • the recommendation totaling unit 14 further calculates the recommendation rank of the recommended axis item from the calculated recommended evaluation value for each recommended axis item 5, and stores the calculation result in the recommended evaluation value DB 17.
  • the recommendation order is a ranking of the degree of tendency set by the recommendation method 6, and the higher the degree, the higher the order.
  • the failure rate tends to increase as the recommended evaluation value increases.
  • ranking is performed based on the magnitude relationship of the recommended evaluation values for each model, and the recommendation ranking of the model having the largest recommended evaluation value is set to be first.
  • FIG. 10 is an explanatory diagram showing an example of a table of the recommended evaluation value DB 17 of FIG.
  • the recommendation evaluation value DB 17 includes a table relating to the calculation result of the recommendation evaluation value and the recommendation order by the recommendation totaling unit 14. Specifically, the table of FIG. 10 associates the recommended axis item 5, the recommended evaluation value, and the recommendation rank as data items.
  • the data on the first line indicates that the recommendation evaluation value of model 1 is 0.84 and the recommendation ranking is first. That is, the model 1 with the first recommendation ranking shows that the failure rate has the highest tendency among the models 1 to 4.
  • this numerical value is a value calculated according to the formula (1) using the y-axis recommended total value of each month from January to December corresponding to the model 1 from the recommended total value DB 16 shown in FIG. .
  • the data output unit 18 outputs the correlation between the x-axis item and the y-axis integrated total value as the main quality report 7 using the y-axis integrated total value for each x-axis item stored in the integrated total value DB 15. . Specifically, the data output unit 18 outputs the correlation between the failure occurrence month and the failure rate of all models as the main quality report 7.
  • FIG. 11 is an explanatory diagram showing an example of the main quality report 7 of FIG.
  • the main quality report 7 is output in a form in which the y-axis integrated total value for each failure occurrence month is plotted in a graph with the vertical axis representing the failure rate and the horizontal axis representing the failure occurrence month. is there. That is, in the main quality report 7, the correlation between the failure occurrence month and the failure rate of all models is shown in a graph.
  • the data output unit 18 uses the x-axis item and the y-axis recommended total value for each recommended axis item 5 stored in the recommended total value DB 16 to determine the correlation between the x-axis item and the y-axis recommended total value. Each is output as a recommended quality report 8. Specifically, the data output unit 18 outputs the correlation between the failure occurrence month and the failure rate of the model as a recommended quality report 8 for each model.
  • the data output unit 18 also outputs a recommendation rank for each recommended axis item 5 stored in the recommended evaluation value DB 17. Specifically, the data output unit 18 also outputs a recommendation order for each model.
  • FIG. 12 is an explanatory diagram showing an example of the recommended quality report 8 of FIG.
  • FIG. 13 is an explanatory diagram showing another example of the recommended quality report 8 of FIG.
  • the recommended quality report 8 shows the y-axis recommended aggregate value for each failure occurrence month plotted on a graph with the failure rate on the vertical axis and the failure occurrence month on the horizontal axis. Is output. That is, in the recommended quality report 8, the correlation between the failure occurrence month and the failure rate of the model is shown for each model in a graph.
  • the graph of each model also shows the recommendation order. That is, the graph corresponding to the model 1 shown in FIG. 12 indicates that the recommendation rank is first, and the graph corresponding to the model 3 illustrated in FIG. 13 indicates that the recommendation rank is second. Has been.
  • the user can confirm the correlation between the two parameters set in the x-axis item and the y-axis item of the comparison condition 4. Further, the correlation between the x-axis item and the y-axis item can be confirmed for each data item set in the recommended axis item 5. Furthermore, according to the content set by the recommendation method, a specific tendency indicated by the correlation between the x-axis item and the y-axis item obtained for each recommended axis item 5 can be quantitatively confirmed.
  • the main quality report 7 and the recommended quality report 8 illustrate the case where each correlation is represented in a graph format, but each correlation may be represented in any format.
  • the data output unit 18 may output the main quality report 7 and the recommended quality report 8 in any way as long as the user can confirm the contents of the main quality report 7 and the recommended quality report 8. You may comprise so that it may display on the screen of a display apparatus.
  • FIG. 14 is a flowchart showing a series of operation examples of the multidimensional data analysis support apparatus 1 according to Embodiment 1 of the present invention.
  • step S101 the recommended axis-by-axis intermediate totaling unit 11 determines each x-axis item and recommended axis item 5 according to the comparison condition 4 and the recommended axis item 5 from the input data for analysis (here, the failure information DB2 and the contract information DB3). Then, the y-axis intermediate value is calculated and stored in the intermediate total value DB 12.
  • step S102 the integrated tabulation unit 13 calculates the value of the y-axis item for each x-axis item from the intermediate tabulation value DB 12 as the y-axis integrated tabulation value using the y-axis tabulation formula, and stores the calculated value in the integrated tabulation value DB 15. To do.
  • step S103 the recommendation totaling unit 14 calculates the value of the y-axis item for each x-axis item and the recommended axis item 5 as the y-axis recommended total value using the y-axis totaling formula from the intermediate total value DB 12, and recommends it. Stored in the total value DB 16.
  • step S ⁇ b> 104 the recommendation totaling unit 14 calculates a recommended evaluation value for each recommended axis item 5 from the recommended total value DB 16 according to the recommendation method 6, and calculates the recommended axis item 5 from the recommended evaluation value for each recommended axis item 5.
  • the recommendation ranking is calculated, and these calculation results are stored in the recommendation evaluation value DB 17.
  • step S105 the data output unit 18 creates and outputs the main quality report 7 from the integrated summary value DB 15, and creates and outputs the recommended quality report 8 from the recommended summary value DB 16 and the recommended evaluation value DB 17.
  • the main quality report 7 and the recommended quality report 8 are output by executing the processing of step S101 to step S105 by the multidimensional data analysis support apparatus 1, so that the user can analyze the input data for analysis.
  • the recommended axis item 5 and the recommendation method 6 By simply setting the comparison condition 4, the recommended axis item 5 and the recommendation method 6 according to the contents, it is possible to easily perform a desired analysis on the input data for analysis.
  • the recommended axis-specific intermediate totaling unit that calculates the y-axis intermediate value for each x-axis item and recommended axis item according to the comparison condition and the recommended axis item from the input data for analysis, and x
  • a recommendation totaling unit that calculates a value of a y-axis item for each x-axis item and each recommended axis item from a y-axis intermediate value for each axis item and each recommended axis item as a y-axis recommended total value using a y-axis totaling formula
  • a data output unit is provided that outputs a correlation between the x-axis item and the y-axis recommended total value for each recommended axis item from the y-axis recommended total value for each x-axis item and the recommended axis item.
  • the analysis result according to the input by the user is output without depending on the user's analysis skill, so that the user is unexpectedly aware of the multi-dimensional data and is also atypical for the user with less analysis skill.
  • the result of a typical analysis is recognized as preliminary knowledge in maintenance work, and as a result, maintenance quality is improved.
  • the present invention is applied to multidimensional data related to maintenance work of a maintenance target device (for example, an elevator) is illustrated, but the present invention is not limited to this, and what kind of multidimensional data is used. Needless to say, this is also applicable to the above.
  • a maintenance target device for example, an elevator

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Abstract

The present invention is configured so as to calculate, in accordance with a set comparison condition (4) and a recommended axis item (5), a y-axis recommended aggregate value for each x-axis item and recommended axis item (5), from analysis-use input data (2, 3), and to use the calculated y-axis recommended aggregate value for each x-axis item and recommended axis item (5) to output for each recommended axis item (5) a correlation between the x-axis item and the y-axis recommended aggregate value. Thus, it is possible to obtain a multi-dimensional data analysis assistance device that simplifies an atypical analysis of multi-dimensional data comprising multiple data items.

Description

多次元データ分析支援装置Multidimensional data analysis support device
　本発明は、多数のデータ項目からなる多次元データの分析を支援する多次元データ分析支援装置に関するものである。 The present invention relates to a multidimensional data analysis support apparatus that supports analysis of multidimensional data composed of a large number of data items.
　従来から、品質評価を目的として、コンピュータを用いてデータを分析し、その分析結果が示された帳票形式のレポートを作成している。このようなレポートは、各種業務改善に向けた統一指標として重要な役割を担うが、レポートの作成は、多大な時間を要する。そこで、このようなレポートの作成を自動化する試みがなされている（例えば、特許文献１参照）。 Conventionally, for the purpose of quality evaluation, data is analyzed using a computer, and a report in the form of a form showing the analysis result has been created. Such a report plays an important role as a unified index for various business improvements, but it takes a lot of time to create a report. Thus, attempts have been made to automate the creation of such reports (see, for example, Patent Document 1).
　具体的には、特許文献１に記載の従来技術では、観測されたデータを所定の分析手順によって分析し、その分析結果が示された表および図が、指定されたレイアウトに配置されているレポートを自動的に作成するように構成されている。 Specifically, in the related art described in Patent Document 1, the observed data is analyzed by a predetermined analysis procedure, and a report and a table in which the analysis result is shown are arranged in a designated layout. Is configured to create automatically.
特許第５５５６１６４号公報Japanese Patent No. 5556164
　しかしながら、従来技術には以下のような課題がある。
　特許文献１に記載の従来技術では、所定の分析手順によって定型的に分析された結果が示されたレポートを自動作成しているので、多次元データを分析する際に、特許文献１に記載の従来技術を適用した場合であっても、定型的な分析結果が得られるに過ぎない。 However, the prior art has the following problems.
In the prior art described in Patent Document 1, a report showing the result of routine analysis by a predetermined analysis procedure is automatically created. Therefore, when analyzing multidimensional data, the method described in Patent Document 1 is used. Even when the conventional technique is applied, only a typical analysis result can be obtained.
　また、多次元データの中から傾向または問題点を見つけるために、異なる視点での分析または特定の条件下で深掘りする分析等といった非定型的な分析を行う場合、定型的な分析結果から、追加分析の要否または追加分析の条件を、人間が経験的に判断する必要がある。そのため、分析経験の少ない人間にとっては、非定型的な分析を行うことが困難であるという問題がある。 In addition, in order to find trends or problems in multidimensional data, when performing atypical analysis such as analysis from different viewpoints or analysis that digs deep under specific conditions, from typical analysis results, It is necessary for humans to empirically determine whether additional analysis is necessary or conditions for additional analysis. Therefore, there is a problem that it is difficult for a person with little analysis experience to perform an atypical analysis.
　本発明は、上記のような課題を解決するためになされたものであり、多次元データに対する非定型的な分析の容易化を実現する多次元データ分析支援装置を得ることを目的とする。 The present invention has been made in order to solve the above-described problems, and an object of the present invention is to obtain a multidimensional data analysis support apparatus that realizes easy atypical analysis of multidimensional data.
　本発明における多次元データ分析支援装置は、分析用入力データと、推薦軸項目と、ｘ軸項目と、ｙ軸項目と、分析用入力データからｘ軸項目および推薦軸項目ごとにｙ軸項目の値を算出するためのｙ軸中間値およびｙ軸集計式とが規定された比較条件と、が入力される多次元データ分析支援装置であって、分析用入力データから、比較条件および推薦軸項目に従って、ｘ軸項目および推薦軸項目ごとにｙ軸中間値を算出する推薦軸別中間集計部と、推薦軸別中間集計部によって算出されたｘ軸項目および推薦軸項目ごとのｙ軸中間値から、ｘ軸項目および推薦軸項目ごとにｙ軸項目の値を、ｙ軸集計式を用いてｙ軸推薦集計値として算出する推薦集計部と、推薦集計部によって算出されたｘ軸項目および推薦軸項目ごとのｙ軸推薦集計値から、ｘ軸項目およびｙ軸推薦集計値の相関関係を、推薦軸項目ごとに出力するデータ出力部と、を備えたものである。 The multidimensional data analysis support device according to the present invention includes an analysis input data, a recommended axis item, an x axis item, a y axis item, and a y axis item for each x axis item and recommended axis item from the analysis input data. A multidimensional data analysis support apparatus to which a comparison condition in which a y-axis intermediate value and a y-axis aggregation formula for calculating a value are defined is input, and the comparison condition and recommended axis items are input from the analysis input data According to the recommended axis-specific intermediate tabulation unit for calculating the y-axis intermediate value for each x-axis item and the recommended axis item, and the y-axis intermediate value for each of the x-axis item and the recommended axis item calculated by the recommended axis-specific intermediate tabulation unit. A recommendation totaling unit that calculates the value of the y-axis item for each x-axis item and the recommended axis item as a y-axis recommended total value using a y-axis totaling formula, and the x-axis item and the recommended axis calculated by the recommendation totaling unit Y-axis recommended aggregate value for each item Et al., In which the correlation between the x-axis item and a y-axis recommendation aggregate value, comprising: a data output unit that outputs for each recommendation axis item, a.
　本発明によれば、分析用入力データから、設定された比較条件および推薦軸項目に従って、ｘ軸項目および推薦軸項目ごとにｙ軸推薦集計値を算出し、算出されたｘ軸項目および推薦軸項目ごとのｙ軸推薦集計値を用いて、ｘ軸項目およびｙ軸推薦集計値の相関関係を、推薦軸項目ごとに出力するように構成する。これにより、多次元データに対する非定型的な分析の容易化を実現する多次元データ分析支援装置を得ることができる。 According to the present invention, the y-axis recommended aggregate value is calculated for each x-axis item and recommended axis item from the input data for analysis according to the set comparison condition and recommended axis item, and the calculated x-axis item and recommended axis are calculated. Using the y-axis recommended total value for each item, the correlation between the x-axis item and the y-axis recommended total value is output for each recommended axis item. Thereby, it is possible to obtain a multidimensional data analysis support apparatus that realizes easy atypical analysis of multidimensional data.
本発明の実施の形態１における多次元データ分析支援装置の構成を示すブロック図である。It is a block diagram which shows the structure of the multidimensional data analysis assistance apparatus in Embodiment 1 of this invention. 図１の故障情報ＤＢのテーブルの一例を示す説明図である。It is explanatory drawing which shows an example of the table of failure information DB of FIG. 図１の契約情報ＤＢのテーブルの一例を示す説明図である。It is explanatory drawing which shows an example of the table of contract information DB of FIG. 図１の比較条件の一例を示す説明図である。It is explanatory drawing which shows an example of the comparison conditions of FIG. 図１の推薦軸項目の一例を示す説明図である。It is explanatory drawing which shows an example of the recommendation axis | shaft item of FIG. 図１の推薦方式の一例を示す説明図である。It is explanatory drawing which shows an example of the recommendation system of FIG. 図１の中間集計値ＤＢのテーブルの一例を示す説明図である。It is explanatory drawing which shows an example of the table of intermediate | middle total value DB of FIG. 図１の統合集計値ＤＢのテーブルの一例を示す説明図である。It is explanatory drawing which shows an example of the table of integrated total value DB of FIG. 図１の推薦集計値ＤＢのテーブルの一例を示す説明図である。It is explanatory drawing which shows an example of the table of the recommendation total value DB of FIG. 図１の推薦評価値ＤＢのテーブルの一例を示す説明図である。It is explanatory drawing which shows an example of the table of recommendation evaluation value DB of FIG. 図１のメイン品質レポートの一例を示す説明図である。It is explanatory drawing which shows an example of the main quality report of FIG. 図１の推薦品質レポートの一例を示す説明図である。It is explanatory drawing which shows an example of the recommendation quality report of FIG. 図１の推薦品質レポートの別例を示す説明図である。It is explanatory drawing which shows another example of the recommendation quality report of FIG. 本発明の実施の形態１における多次元データ分析支援装置の一連の動作例を示すフローチャートである。It is a flowchart which shows a series of operation examples of the multidimensional data analysis assistance apparatus in Embodiment 1 of this invention.
　以下、本発明による多次元データ分析支援装置を、好適な実施の形態にしたがって図面を用いて説明する。なお、図面の説明においては、同一部分または相当部分には同一符号を付し、重複する説明を省略する。また、実施の形態では、保守対象機器（例えば、昇降機）の保守業務に関する多次元データに対して本願発明を適用する場合を例示する。 Hereinafter, a multidimensional data analysis support apparatus according to the present invention will be described with reference to the drawings according to a preferred embodiment. In the description of the drawings, the same portions or corresponding portions are denoted by the same reference numerals, and redundant description is omitted. Moreover, in embodiment, the case where this invention is applied with respect to the multidimensional data regarding the maintenance operation | work of a maintenance object apparatus (for example, elevator) is illustrated.
　実施の形態１．
　図１は、本発明の実施の形態１における多次元データ分析支援装置１の構成を示すブロック図である。ここで、本実施の形態１における多次元データ分析支援装置１は、入力された分析用入力データに対して、入力された比較条件４、推薦軸項目５および推薦方式６に従って分析を行い、その分析結果を出力する。 Embodiment 1 FIG.
FIG. 1 is a block diagram showing a configuration of a multidimensional data analysis support apparatus 1 according to Embodiment 1 of the present invention. Here, the multidimensional data analysis support device 1 according to the first embodiment analyzes the input input data for analysis according to the input comparison condition 4, the recommended axis item 5 and the recommendation method 6, and Output analysis results.
　なお、多次元データ分析支援装置１は、例えば、メモリに記憶されたプログラムを実行するＣＰＵと、システムＬＳＩ等の処理回路とによって実現される。また、各種データが格納される各データベース（ＤＢ）は、メモリに記憶されている。 The multidimensional data analysis support apparatus 1 is realized by, for example, a CPU that executes a program stored in a memory and a processing circuit such as a system LSI. Each database (DB) in which various data is stored is stored in a memory.
　まず、多次元データ分析支援装置１への各入力要素について説明する。なお、ここでは、多次元データ分析支援装置１に、分析対象の分析用入力データとして、故障情報ＤＢ２および契約情報ＤＢ３のそれぞれに含まれるデータが入力される場合を例示する。 First, each input element to the multidimensional data analysis support apparatus 1 will be described. Here, a case where data included in each of the failure information DB 2 and the contract information DB 3 is input to the multidimensional data analysis support apparatus 1 as analysis input data to be analyzed is illustrated.
　図２は、図１の故障情報ＤＢ２のテーブルの一例を示す説明図である。図２に示すように、故障情報ＤＢ２には、保守対象機器の故障情報に関するテーブルが含まれている。具体的には、図２のテーブルは、データ項目として、故障ＩＤと、契約ＩＤと、故障時期と、分類項目Ｘとを関連付けている。 FIG. 2 is an explanatory diagram showing an example of a table of the failure information DB 2 of FIG. As shown in FIG. 2, the failure information DB 2 includes a table relating to failure information of the maintenance target device. Specifically, the table in FIG. 2 associates failure ID, contract ID, failure time, and classification item X as data items.
　図２において、故障ＩＤは、故障を識別する識別子を示す。契約ＩＤは、その故障ＩＤに対応する故障が発生した保守対象機器を保守するにあたっての保守契約の内容を識別する識別子を示す。故障時期は、その故障ＩＤに対応する故障が発生した年月日を示す。分類項目Ｘは、その故障ＩＤに対応する故障がどのような種類に分類されるかを示す。 In FIG. 2, the failure ID indicates an identifier for identifying the failure. The contract ID indicates an identifier for identifying the content of the maintenance contract for maintaining the maintenance target device in which the failure corresponding to the failure ID has occurred. The failure time indicates the date on which the failure corresponding to the failure ID occurred. The classification item X indicates what kind of failure is classified into the failure ID.
　図２において、例えば、１行目のデータは、故障ＩＤが故障０００１であり、その故障ＩＤに対応する保守契約の内容が契約００００２であり、その故障ＩＤに対応する故障時期が２０１４年８月２１日であり、その故障ＩＤに対応する故障の種類が分類Ｘ０１であることを示している。 In FIG. 2, for example, in the data on the first line, the failure ID is failure 0001, the content of the maintenance contract corresponding to the failure ID is contract 00002, and the failure time corresponding to the failure ID is August 2014. This indicates that the failure type corresponding to the failure ID is class X01.
　図３は、図１の契約情報ＤＢ３のテーブルの一例を示す説明図である。図３に示すように、契約情報ＤＢ３には、保守対象機器の保守契約情報に関するテーブルが含まれている。具体的には、図３のテーブルは、データ項目として、契約ＩＤと、契約時期と、機種と、分類項目Ｙとを関連付けている。 FIG. 3 is an explanatory diagram showing an example of a table of the contract information DB 3 in FIG. As shown in FIG. 3, the contract information DB 3 includes a table related to maintenance contract information of maintenance target devices. Specifically, the table of FIG. 3 associates a contract ID, a contract time, a model, and a classification item Y as data items.
　図３において、契約ＩＤは、保守対象機器の保守契約の内容を識別する識別子を示す。契約時期は、その契約ＩＤに対応する保守契約が締結された年月日を示す。機種は、その契約ＩＤに対応する保守対象機器の機種を示す。分類項目Ｙは、その契約ＩＤに対応する保守契約がどのような種類に分類されるかを示す。 In FIG. 3, the contract ID indicates an identifier for identifying the content of the maintenance contract of the maintenance target device. The contract time indicates the date on which the maintenance contract corresponding to the contract ID is concluded. The model indicates the model of the maintenance target device corresponding to the contract ID. The classification item Y indicates what type the maintenance contract corresponding to the contract ID is classified.
　図３において、例えば、１行目のデータは、契約ＩＤが契約００００１であり、その契約ＩＤに対応する保守契約の締結時期が２０１０年４月１日であり、その契約ＩＤに対応する保守対象機器の機種が機種１であり、その契約ＩＤに対応する保守契約の種類が分類Ｙ０２であることを示している。 In FIG. 3, for example, in the data on the first line, the contract ID is contract 00001, the maintenance contract corresponding to the contract ID is set to April 1, 2010, and the maintenance target corresponding to the contract ID is shown in FIG. This indicates that the model of the device is model 1, and the type of maintenance contract corresponding to the contract ID is classification Y02.
　また、図２および図３の各テーブルには、契約ＩＤのデータ項目が含まれているので、各テーブルは、互いに関連付けられる。 Further, since the data items of the contract ID are included in each table of FIGS. 2 and 3, the tables are associated with each other.
　このように、多次元データ分析支援装置１には、図２および図３に示すような保守対象機器の保守業務に関する多次元データが分析用入力データとして入力される。 As described above, the multidimensional data analysis support apparatus 1 receives multidimensional data related to the maintenance work of the maintenance target device as shown in FIGS. 2 and 3 as input data for analysis.
　図４は、図１の比較条件４の一例を示す説明図である。図４に示すように、比較条件４は、ｘ軸項目、ｙ軸項目、ｙ軸集計式、第１のｙ軸中間値、第１の合成関数、第２のｙ軸中間値、第２の合成関数および絞込み条件が規定される。比較条件４の各項目は、ユーザによって設定される。 FIG. 4 is an explanatory diagram showing an example of the comparison condition 4 in FIG. As shown in FIG. 4, the comparison condition 4 includes an x-axis item, a y-axis item, a y-axis aggregation formula, a first y-axis intermediate value, a first composite function, a second y-axis intermediate value, and a second A composite function and a narrowing condition are defined. Each item of the comparison condition 4 is set by the user.
　図４において、ｘ軸項目およびｙ軸項目の各項目には、分析用入力データから導出可能な２つのパラメータの相関関係を分析する際のパラメータが設定される。ここでは、分析用入力データから、故障発生月と故障率との相関関係を分析するために、ｘ軸項目として故障発生月が設定され、ｙ軸項目として故障率が設定される場合を例示している。 In FIG. 4, parameters for analyzing the correlation between two parameters that can be derived from the input data for analysis are set in each item of the x-axis item and the y-axis item. Here, in order to analyze the correlation between the failure occurrence month and the failure rate from the input data for analysis, the case where the failure occurrence month is set as the x-axis item and the failure rate is set as the y-axis item is illustrated. ing.
　第１のｙ軸中間値および第２のｙ軸中間値の項目には、分析用入力データからｘ軸項目および後述する推薦軸項目５ごとにｙ軸項目の値を算出するために必要なパラメータが設定される。ここでは、故障情報ＤＢ２および契約情報ＤＢ３のそれぞれに含まれるデータから、故障発生月および機種ごとに故障率を算出する必要がある。 The first y-axis intermediate value and the second y-axis intermediate value item are parameters necessary for calculating the value of the y-axis item for each x-axis item and recommended axis item 5 described later from the input data for analysis. Is set. Here, it is necessary to calculate a failure rate for each failure month and model from data included in each of the failure information DB 2 and the contract information DB 3.
　したがって、分析用入力データからｘ軸項目および推薦軸項目５ごとにｙ軸項目の値を算出するためのｙ軸中間値として、第１のｙ軸中間値および第２のｙ軸中間値の２つのパラメータを設定している。具体的には、第１のｙ軸中間値として故障情報ＤＢ２の件数が設定され、第２のｙ軸中間値として契約情報ＤＢ３の件数が設定される場合を例示している。 Accordingly, as the y-axis intermediate value for calculating the value of the y-axis item for each of the x-axis item and the recommended axis item 5 from the input data for analysis, 2 of the first y-axis intermediate value and the second y-axis intermediate value. Two parameters are set. Specifically, the case where the number of failure information DB2 is set as the first y-axis intermediate value and the number of contract information DB3 is set as the second y-axis intermediate value is illustrated.
　ｙ軸集計式の項目には、分析用入力データからｘ軸項目および推薦軸項目５ごとにｙ軸項目の値を算出するために必要な関数が設定される。ここでは、第１のｙ軸中間値および第２のｙ軸中間値から、ｘ軸項目および推薦軸項目５ごとにｙ軸項目の値を算出するための関数がｙ軸集計式として設定される。具体的には、ｙ軸集計式の関数として、［第１のｙ軸中間値］／［第２のｙ軸中間値］が設定される場合を例示している。 In the item of the y-axis aggregation formula, a function necessary for calculating the value of the y-axis item for each x-axis item and the recommended axis item 5 from the input data for analysis is set. Here, a function for calculating the value of the y-axis item for each x-axis item and recommended axis item 5 from the first y-axis intermediate value and the second y-axis intermediate value is set as the y-axis aggregation formula. . Specifically, the case where [first y-axis intermediate value] / [second y-axis intermediate value] is set as a function of the y-axis aggregation formula is illustrated.
　第１の合成関数の項目には、ｘ軸項目および推薦軸項目５ごとの第１のｙ軸中間値を、ｘ軸項目の同じ値ごとに合成するための関数が設定される。ここでは、第１の合成関数として、加算関数が設定される場合を例示する。 In the item of the first synthesis function, a function for synthesizing the first y-axis intermediate value for each x-axis item and the recommended axis item 5 for each same value of the x-axis item is set. Here, a case where an addition function is set as the first synthesis function is illustrated.
　第２の合成関数の項目には、ｘ軸項目および推薦軸項目５ごとの第２のｙ軸中間値を、ｘ軸項目の同じ値ごとに合成するための関数が設定される。ここでは、第２の合成関数として、加算関数が設定される場合を例示する。 In the item of the second synthesis function, a function for synthesizing the second y-axis intermediate value for each x-axis item and the recommended axis item 5 for each same value of the x-axis item is set. Here, a case where an addition function is set as the second synthesis function is illustrated.
　絞込み条件には、分析用入力データのデータ数を絞り込むための条件が設定される。ここでは、故障情報ＤＢ２に含まれるデータにおいて分類項目Ｘが分類Ｘ０１に属するデータを用いて分析する条件が絞込み条件として設定される。この場合、分類Ｘ０１に属する故障について分析されることとなる。 絞 Conditions for narrowing down the number of input data for analysis are set in the filtering conditions. In this case, a condition for analyzing the classification item X using data belonging to the classification X01 in the data included in the failure information DB 2 is set as a narrowing condition. In this case, the failure belonging to the classification X01 is analyzed.
　図５は、図１の推薦軸項目５の一例を示す説明図である。図６は、図１の推薦方式６の一例を示す説明図である。推薦軸項目５および推薦方式６の各項目は、ユーザによって設定される。 FIG. 5 is an explanatory diagram showing an example of the recommended axis item 5 in FIG. FIG. 6 is an explanatory diagram showing an example of the recommendation method 6 of FIG. Each item of the recommendation axis item 5 and the recommendation method 6 is set by the user.
　図５および図６において、推薦軸項目５および推薦方式６の各項目には、比較条件４のｘ軸項目およびｙ軸項目の各項目に設定された２つのパラメータの相関関係を基に、特定の傾向をさらに分析するためのパラメータが設定される。なお、推薦軸項目５は、１つではなく複数設定することもできる。ここでは、故障が増加傾向にある機種を知りたいとユーザが考えていると仮定して、推薦軸項目５として機種が設定され、推薦方式６として増加傾向が設定される場合を例示している。この場合、保守対象機器の機種ごとに故障発生月に対する故障率の増加傾向が定量的に分析されることとなる。 5 and 6, each item of the recommended axis item 5 and the recommendation method 6 is specified based on the correlation between the two parameters set in the x axis item and the y axis item of the comparison condition 4. Parameters for further analysis of the tendency are set. Note that a plurality of recommended axis items 5 can be set instead of one. In this example, assuming that the user wants to know a model whose failure is increasing, the model is set as the recommended axis item 5 and the increasing tendency is set as the recommendation method 6. . In this case, an increasing tendency of the failure rate with respect to the failure occurrence month is quantitatively analyzed for each model of the maintenance target device.
　このように、多次元データ分析支援装置１には、図４～図６に示すような、分析用入力データを分析するための条件に相当する、比較条件４、推薦軸項目５および推薦方式６も分析用入力データと併せて入力される。 As described above, the multidimensional data analysis support apparatus 1 includes the comparison condition 4, the recommended axis item 5 and the recommendation method 6 corresponding to the conditions for analyzing the input data for analysis as shown in FIGS. Is also input together with the input data for analysis.
　なお、分析用入力データの内容によって、ｘ軸項目、ｙ軸項目、推薦軸項目５および推薦方式６のそれぞれにさまざまなパラメータが設定可能である。 Various parameters can be set for each of the x-axis item, the y-axis item, the recommended axis item 5 and the recommendation method 6 depending on the content of the input data for analysis.
　ｘ軸項目としては、例えば、年度、または支社などを設定することができる。ｙ軸項目としては、例えば、修理作業時間、修理コスト、または故障停止時間（ＭＴＴＲ）などを設定することができる。推薦軸項目５としては、例えば、契約形態、故障部位、故障現象、故障原因、設備用途、設置環境、管理部門、または設置年数などを設定することができる。推薦方式６としては、例えば、減少傾向、平均値の大小傾向、急上昇傾向、急下降傾向、または季節変動傾向などを設定することができる。 * As the x-axis item, for example, a year or a branch office can be set. As the y-axis item, for example, repair work time, repair cost, or failure stop time (MTTR) can be set. As the recommended axis item 5, for example, a contract form, a failure part, a failure phenomenon, a failure cause, a facility application, an installation environment, a management department, or an installation year can be set. As the recommendation method 6, for example, a decreasing tendency, a tendency of a mean value magnitude, a rapidly increasing tendency, a rapidly decreasing tendency, or a seasonal fluctuation tendency can be set.
　なお、本実施の形態１では、推薦軸項目５に設定可能なパラメータと、推薦方式６に設定可能なパラメータとの組み合わせの一例として、機種と増加傾向とを組み合わせた場合を例示しているが、推薦軸項目５に設定可能なパラメータと、推薦方式６に設定可能なパラメータとの組み合わせとして、さまざまな組み合わせを設定することができることはいうまでもない。例えば、推薦軸項目５に契約形態を設定し、推薦方式６に減少傾向を設定した場合、保守対象機器の契約形態ごとに故障発生月に対する故障率の減少傾向が定量的に分析されることとなる。 In the first embodiment, as an example of a combination of a parameter that can be set in the recommended axis item 5 and a parameter that can be set in the recommendation method 6, a case where a model and an increasing tendency are combined is illustrated. Needless to say, various combinations can be set as combinations of parameters that can be set in the recommended axis item 5 and parameters that can be set in the recommendation method 6. For example, when the contract form is set in the recommended axis item 5 and the decreasing tendency is set in the recommendation method 6, the decreasing tendency of the failure rate with respect to the failure occurrence month is quantitatively analyzed for each contract form of the maintenance target device. Become.
　なお、分析用入力データの内容と、ｘ軸項目、ｙ軸項目および推薦軸項目５に設定されるパラメータの内容とに従って、比較条件４における第１のｙ軸中間値、第２のｙ軸中間値、ｙ軸集計式、第１の合成関数、第２の合成関数および絞込み条件の設定内容が適宜変更されるのはいうまでもない。 The first y-axis intermediate value and the second y-axis intermediate value in the comparison condition 4 according to the contents of the input data for analysis and the contents of the parameters set in the x-axis item, the y-axis item, and the recommended axis item 5 Needless to say, the setting contents of the value, the y-axis aggregation formula, the first synthesis function, the second synthesis function, and the narrowing-down condition are appropriately changed.
　次に、多次元データ分析支援装置１の各構成要素について説明する。図１において、多次元データ分析支援装置１は、推薦軸別中間集計部１１、中間集計値ＤＢ１２、統合集計部１３、推薦集計部１４、統合集計値ＤＢ１５、推薦集計値ＤＢ１６、推薦評価値ＤＢ１７およびデータ出力部１８を備える。 Next, each component of the multidimensional data analysis support apparatus 1 will be described. In FIG. 1, the multidimensional data analysis support device 1 includes a recommended axis-specific intermediate totaling unit 11, an intermediate totaling value DB 12, an integrated totaling unit 13, a recommended totaling unit 14, an integrated totaling value DB 15, a recommended totaling value DB 16, and a recommended evaluation value DB 17. And a data output unit 18.
　推薦軸別中間集計部１１は、分析用入力データとして入力された故障情報ＤＢ２および契約情報ＤＢ３のそれぞれから、比較条件４および推薦軸項目５に従って、ｘ軸項目および推薦軸項目５ごとに第１のｙ軸中間値および第２のｙ軸中間値を算出し、算出結果を中間集計値ＤＢ１２に格納する。 The recommended axis-by-recommended intermediate totaling unit 11 performs the first operation for each x-axis item and recommended axis item 5 according to the comparison condition 4 and the recommended axis item 5 from the failure information DB 2 and the contract information DB 3 input as input data for analysis. The y-axis intermediate value and the second y-axis intermediate value are calculated, and the calculation result is stored in the intermediate total value DB 12.
　具体的には、推薦軸別中間集計部１１は、故障情報ＤＢ２および契約情報ＤＢ３のそれぞれを契約ＩＤで関連付け、ｘ軸項目として設定された故障発生月および推薦軸項目５として設定された機種ごとにカウントした分類Ｘ０１に属する故障の件数を、第１のｙ軸中間値とする。また、推薦軸別中間集計部１１は、契約情報ＤＢ３から、機種ごとにカウントした保守契約の件数を、第２のｙ軸中間値とする。 Specifically, the recommended axis-specific intermediate totaling unit 11 associates each of the failure information DB 2 and the contract information DB 3 with a contract ID, and sets the failure occurrence month set as the x-axis item and the model set as the recommended axis item 5. The number of faults belonging to the classification X01 counted in (1) is defined as the first y-axis intermediate value. Also, the recommended axis-specific intermediate totaling unit 11 sets the number of maintenance contracts counted for each model from the contract information DB 3 as the second y-axis intermediate value.
　ここで、中間集計値ＤＢ１２について説明する。図７は、図１の中間集計値ＤＢ１２のテーブルの一例を示す説明図である。図７に示すように、中間集計値ＤＢ１２には、推薦軸別中間集計部１１による算出結果に関するテーブルが含まれている。具体的には、図７のテーブルは、データ項目として、ｘ軸項目と、推薦軸項目５と、第１のｙ軸中間値と、第２のｙ軸中間値とを関連付けている。 Here, the intermediate summary value DB 12 will be described. FIG. 7 is an explanatory diagram showing an example of a table of the intermediate summary value DB 12 of FIG. As shown in FIG. 7, the intermediate total value DB 12 includes a table related to the calculation result by the recommended axis-specific intermediate total unit 11. Specifically, the table of FIG. 7 associates, as data items, an x-axis item, a recommended axis item 5, a first y-axis intermediate value, and a second y-axis intermediate value.
　図７において、例えば、１行目のデータは、１月に機種１で発生した故障の件数が３５であり、機種１の保守契約の件数が５００１であることを示している。 In FIG. 7, for example, the data in the first row indicates that the number of failures that occurred in model 1 in January is 35 and the number of maintenance contracts for model 1 is 5001.
　図１の説明に戻り、統合集計部１３は、中間集計値ＤＢ１２に格納されているｘ軸項目および推薦軸項目５ごとの第１のｙ軸中間値および第２のｙ軸中間値から、ｘ軸項目ごとにｙ軸項目の値を、第１の合成関数、第２の合成関数およびｙ軸集計式を用いてｙ軸統合集計値として算出し、算出結果を統合集計値ＤＢ１５に格納する。 Returning to the description of FIG. 1, the integrated totaling unit 13 calculates the x-axis item stored in the intermediate total value DB 12 and the first y-axis intermediate value and the second y-axis intermediate value for each recommended axis item 5 from the x-axis item. For each axis item, the value of the y-axis item is calculated as a y-axis integrated total value using the first composite function, the second composite function, and the y-axis total formula, and the calculation result is stored in the integrated total value DB 15.
　具体的には、故障発生月および機種ごとの第１のｙ軸中間値を、故障発生月ごとに加算関数を用いてそれぞれ加算することで、第１の加算値を算出する。また、故障発生月および機種ごとの第２のｙ軸中間値を、故障発生月ごとに加算関数を用いてそれぞれ加算することで、第２の加算値を算出する。さらに、故障発生月ごとに、第１の加算値を第２の加算値で割ったときの値を、ｙ軸統合集計値とする。 Specifically, the first addition value is calculated by adding the first y-axis intermediate value for each failure month and each model using the addition function for each failure month. Further, the second added value is calculated by adding the second y-axis intermediate value for each failure occurrence month and each model using the addition function for each failure occurrence month. Further, a value obtained by dividing the first addition value by the second addition value for each failure occurrence month is set as a y-axis integrated total value.
　ここで、統合集計値ＤＢ１５について説明する。図８は、図１の統合集計値ＤＢ１５のテーブルの一例を示す説明図である。図８に示すように、統合集計値ＤＢ１５には、統合集計部１３による算出結果に関するテーブルが含まれている。具体的には、図８のテーブルは、データ項目として、ｘ軸項目と、ｙ軸統合集計値とを関連付けている。 Here, the integrated total value DB 15 will be described. FIG. 8 is an explanatory diagram showing an example of a table of the integrated total value DB 15 of FIG. As shown in FIG. 8, the integrated total value DB 15 includes a table related to the calculation result by the integrated total unit 13. Specifically, the table of FIG. 8 associates x-axis items and y-axis integrated aggregate values as data items.
　図８において、例えば、１行目のデータは、故障発生月が１月であるときの、すべての機種で発生した故障率が０．００５５であることを示している。また、この数値は、図７に示す中間集計値ＤＢ１２から、１月に対応する第１の加算値（＝３５＋３６＋３６＋３７）および第２の加算値（＝５００１＋６００９＋８０８２＋７２８２）を算出し、第１の加算値を第２の加算値で割ることで得られる値である。 In FIG. 8, for example, the data in the first row indicates that the failure rate occurring in all models when the failure occurrence month is January is 0.0055. In addition, the first addition value (= 35 + 36 + 36 + 37) and the second addition value (= 5001 + 6009 + 8082 + 7282) corresponding to January are calculated from the intermediate total value DB 12 shown in FIG. It is a value obtained by dividing by the second added value.
　図１の説明に戻り、推薦集計部１４は、中間集計値ＤＢ１２に格納されているｘ軸項目および推薦軸項目５ごとの第１のｙ軸中間値および第２のｙ軸中間値から、ｘ軸項目および推薦軸項目５ごとにｙ軸項目の値を、ｙ軸集計式を用いてｙ軸推薦集計値として算出し、算出結果を推薦集計値ＤＢ１６に格納する。具体的には、故障発生月および機種ごとに、第１のｙ軸中間値を第２のｙ軸中間値で割ったときの値を、ｙ軸推薦集計値とする。 Returning to the description of FIG. 1, the recommendation totaling unit 14 calculates x values from the first y-axis intermediate value and the second y-axis intermediate value for each x-axis item and recommended axis item 5 stored in the intermediate total value DB 12. For each axis item and recommended axis item 5, the value of the y-axis item is calculated as a y-axis recommended total value using the y-axis totaling formula, and the calculation result is stored in the recommended total value DB 16. Specifically, a value obtained by dividing the first y-axis intermediate value by the second y-axis intermediate value for each failure month and model is set as the y-axis recommended total value.
　ここで、推薦集計値ＤＢ１６について説明する。図９は、図１の推薦集計値ＤＢ１６のテーブルの一例を示す説明図である。図９に示すように、推薦集計値ＤＢ１６には、推薦集計部１４によるｙ軸推薦集計値の算出結果に関するテーブルが含まれている。具体的には、図９のテーブルは、データ項目として、ｘ軸項目と、推薦軸項目５と、ｙ軸推薦集計値とを関連付けている。 Here, the recommended total value DB 16 will be described. FIG. 9 is an explanatory diagram showing an example of a table of the recommended total value DB 16 of FIG. As shown in FIG. 9, the recommended total value DB 16 includes a table relating to the calculation result of the y-axis recommended total value by the recommendation totaling unit 14. Specifically, the table of FIG. 9 associates x-axis items, recommended axis items 5, and y-axis recommended aggregate values as data items.
　図９において、例えば、１行目のデータは、故障発生月が１月であるときの機種１で発生した故障率が０．００７０であることを示している。また、この数値は、図７に示す中間集計値ＤＢ１２から、１月および機種１に対応する第１のｙ軸中間値（＝３５）および第２のｙ軸中間値（＝５００１）を用いて、第１のｙ軸中間値を第２のｙ軸中間値で割ることで得られる値である。 In FIG. 9, for example, the data in the first row indicates that the failure rate occurring in the model 1 when the failure occurrence month is January is 0.0070. Further, this numerical value is obtained by using the first y-axis intermediate value (= 35) and the second y-axis intermediate value (= 5001) corresponding to January and the model 1 from the intermediate total value DB 12 shown in FIG. , A value obtained by dividing the first y-axis intermediate value by the second y-axis intermediate value.
　図１の説明に戻り、推薦集計部１４は、さらに、算出したｘ軸項目および推薦軸項目５ごとのｙ軸推薦集計値から、推薦方式６に従って、推薦軸項目５ごとに推薦評価値を算出し、算出結果を推薦評価値ＤＢ１７に格納する。具体的には、推薦方式６に対応した統計式を用いて、機種ごとに推薦評価値を算出する。 Returning to the description of FIG. 1, the recommendation totaling unit 14 further calculates a recommended evaluation value for each recommended axis item 5 according to the recommendation method 6 from the calculated x-axis item and the y-axis recommended total value for each recommended axis item 5. Then, the calculation result is stored in the recommended evaluation value DB 17. Specifically, a recommended evaluation value is calculated for each model using a statistical formula corresponding to the recommendation method 6.
　なお、ここでは、推薦方式６を増加傾向と設定しているので、機種ごとの故障率の増加傾向を数値化するために、増加傾向に対応した統計式として、例えば、以下の式（１）を用いることができる。 Here, since the recommendation method 6 is set as an increasing tendency, in order to quantify the increasing tendency of the failure rate for each model, as a statistical formula corresponding to the increasing tendency, for example, the following formula (1) Can be used.
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
　ただし、式（１）において、Ａiを機種Ｎのｉ月のｙ軸推薦集計値、Ｂiを整数ｉ（１ずつ増加する系列）とおき、｛Ａi｝と｛Ｂi｝（双方とも長さｎ＝１２）の相関係数Ｒを推薦評価値としている。 In Equation (1), A i is the y-axis recommended total value for i month of model N, B i is an integer i (sequence increasing by 1), and {A i } and {B i } (both The correlation coefficient R of length n = 12) is used as the recommended evaluation value.
　また、推薦集計部１４は、さらに、算出した推薦軸項目５ごとの推薦評価値から、推薦軸項目の推薦順位を算出し、算出結果を推薦評価値ＤＢ１７に格納する。なお、推薦順位とは、推薦方式６で設定した傾向の度合いを順位付けしたものであり、その度合いが大きいほど順位が上位となる。 Further, the recommendation totaling unit 14 further calculates the recommendation rank of the recommended axis item from the calculated recommended evaluation value for each recommended axis item 5, and stores the calculation result in the recommended evaluation value DB 17. Note that the recommendation order is a ranking of the degree of tendency set by the recommendation method 6, and the higher the degree, the higher the order.
　例えば、上記で例示したとおり、式（１）を用いて、機種ごとに推薦評価値を算出した場合、推薦評価値が大きいほど故障率の増加傾向が高くなる。この場合、機種ごとの推薦評価値の大小関係から、順位付けを行い、最も大きい推薦評価値を取る機種の推薦順位を１位とする。 For example, as illustrated above, when the recommended evaluation value is calculated for each model using Expression (1), the failure rate tends to increase as the recommended evaluation value increases. In this case, ranking is performed based on the magnitude relationship of the recommended evaluation values for each model, and the recommendation ranking of the model having the largest recommended evaluation value is set to be first.
　ここで、推薦評価値ＤＢ１７について説明する。図１０は、図１の推薦評価値ＤＢ１７のテーブルの一例を示す説明図である。図１０に示すように、推薦評価値ＤＢ１７には、推薦集計部１４による推薦評価値および推薦順位の算出結果に関するテーブルが含まれている。具体的には、図１０のテーブルは、データ項目として、推薦軸項目５と、推薦評価値と、推薦順位とを関連付けている。 Here, the recommended evaluation value DB 17 will be described. FIG. 10 is an explanatory diagram showing an example of a table of the recommended evaluation value DB 17 of FIG. As shown in FIG. 10, the recommendation evaluation value DB 17 includes a table relating to the calculation result of the recommendation evaluation value and the recommendation order by the recommendation totaling unit 14. Specifically, the table of FIG. 10 associates the recommended axis item 5, the recommended evaluation value, and the recommendation rank as data items.
　図１０において、例えば、１行目のデータは、機種１の推薦評価値が０．８４であり、推薦順位が１位であることを示している。すなわち、推薦順位が１位である機種１は、機種１～４の中で故障率の増加傾向が最も高いことを示している。また、この数値は、図９に示す推薦集計値ＤＢ１６から、機種１に対応する１月から１２月までの各月のｙ軸推薦集計値を用いて、式（１）に従って算出した値である。 In FIG. 10, for example, the data on the first line indicates that the recommendation evaluation value of model 1 is 0.84 and the recommendation ranking is first. That is, the model 1 with the first recommendation ranking shows that the failure rate has the highest tendency among the models 1 to 4. In addition, this numerical value is a value calculated according to the formula (1) using the y-axis recommended total value of each month from January to December corresponding to the model 1 from the recommended total value DB 16 shown in FIG. .
　データ出力部１８は、統合集計値ＤＢ１５に格納されているｘ軸項目ごとのｙ軸統合集計値を用いて、ｘ軸項目およびｙ軸統合集計値の相関関係を、メイン品質レポート７として出力する。具体的には、データ出力部１８は、故障発生月と、すべての機種の故障率との相関関係を、メイン品質レポート７として出力する。 The data output unit 18 outputs the correlation between the x-axis item and the y-axis integrated total value as the main quality report 7 using the y-axis integrated total value for each x-axis item stored in the integrated total value DB 15. . Specifically, the data output unit 18 outputs the correlation between the failure occurrence month and the failure rate of all models as the main quality report 7.
　ここで、メイン品質レポート７について説明する。図１１は、図１のメイン品質レポート７の一例を示す説明図である。図１１に示すように、メイン品質レポート７は、故障発生月ごとのｙ軸統合集計値を、縦軸を故障率、横軸を故障発生月としたグラフにプロットした形で出力されたものである。すなわち、メイン品質レポート７では、故障発生月と、すべての機種の故障率との相関関係がグラフで示されている。 Here, the main quality report 7 will be described. FIG. 11 is an explanatory diagram showing an example of the main quality report 7 of FIG. As shown in FIG. 11, the main quality report 7 is output in a form in which the y-axis integrated total value for each failure occurrence month is plotted in a graph with the vertical axis representing the failure rate and the horizontal axis representing the failure occurrence month. is there. That is, in the main quality report 7, the correlation between the failure occurrence month and the failure rate of all models is shown in a graph.
　また、データ出力部１８は、推薦集計値ＤＢ１６に格納されているｘ軸項目および推薦軸項目５ごとのｙ軸推薦集計値を用いて、ｘ軸項目およびｙ軸推薦集計値の相関関係を機種ごとに推薦品質レポート８として出力する。具体的には、データ出力部１８は、故障発生月と、機種の故障率の相関関係を、機種ごとに推薦品質レポート８として出力する。 Further, the data output unit 18 uses the x-axis item and the y-axis recommended total value for each recommended axis item 5 stored in the recommended total value DB 16 to determine the correlation between the x-axis item and the y-axis recommended total value. Each is output as a recommended quality report 8. Specifically, the data output unit 18 outputs the correlation between the failure occurrence month and the failure rate of the model as a recommended quality report 8 for each model.
　さらに、データ出力部１８は、推薦評価値ＤＢ１７に格納されている推薦軸項目５ごとの推薦順位を併せて出力する。具体的には、データ出力部１８は、機種ごとの推薦順位を併せて出力する。 Furthermore, the data output unit 18 also outputs a recommendation rank for each recommended axis item 5 stored in the recommended evaluation value DB 17. Specifically, the data output unit 18 also outputs a recommendation order for each model.
　ここで、推薦品質レポート８について説明する。図１２は、図１の推薦品質レポート８の一例を示す説明図である。図１３は、図１の推薦品質レポート８の別例を示す説明図である。図１２および図１３に示すように、推薦品質レポート８は、故障発生月ごとのｙ軸推薦集計値を、縦軸を故障率、横軸を故障発生月としたグラフにプロットした形で機種ごとに出力されたものである。すなわち、推薦品質レポート８では、故障発生月と、機種の故障率との相関関係がグラフで機種ごとに示されている。 Here, the recommended quality report 8 will be described. FIG. 12 is an explanatory diagram showing an example of the recommended quality report 8 of FIG. FIG. 13 is an explanatory diagram showing another example of the recommended quality report 8 of FIG. As shown in FIG. 12 and FIG. 13, the recommended quality report 8 shows the y-axis recommended aggregate value for each failure occurrence month plotted on a graph with the failure rate on the vertical axis and the failure occurrence month on the horizontal axis. Is output. That is, in the recommended quality report 8, the correlation between the failure occurrence month and the failure rate of the model is shown for each model in a graph.
　また、図１２および図１３に示すように、各機種のグラフには、推薦順位も併せて示されている。すなわち、図１２に示す機種１に対応するグラフには、推薦順位が１位であることが示され、図１３に示す機種３に対応するグラフには、推薦順位が２位であることが示されている。 In addition, as shown in FIGS. 12 and 13, the graph of each model also shows the recommendation order. That is, the graph corresponding to the model 1 shown in FIG. 12 indicates that the recommendation rank is first, and the graph corresponding to the model 3 illustrated in FIG. 13 indicates that the recommendation rank is second. Has been.
　このように構成することで、ユーザは、比較条件４のｘ軸項目およびｙ軸項目で設定した２つのパラメータの相関関係を確認できる。また、ｘ軸項目およびｙ軸項目の相関関係を、推薦軸項目５で設定したデータ項目ごとに確認することができる。さらに、推薦方式で設定した内容に従って、推薦軸項目５ごとに得られたｘ軸項目およびｙ軸項目の相関関係が示す特定の傾向を定量的に確認することができる。 With this configuration, the user can confirm the correlation between the two parameters set in the x-axis item and the y-axis item of the comparison condition 4. Further, the correlation between the x-axis item and the y-axis item can be confirmed for each data item set in the recommended axis item 5. Furthermore, according to the content set by the recommendation method, a specific tendency indicated by the correlation between the x-axis item and the y-axis item obtained for each recommended axis item 5 can be quantitatively confirmed.
　なお、図１２および図１３では、ユーザに対して各機種の故障率の増加傾向を定量的に明示するために、各機種に対応する推薦順位が示される場合を例示しているが、各機種に対応する推薦評価値が示されるようにしてもよい。 12 and 13 exemplify a case in which a recommendation order corresponding to each model is shown in order to quantitatively clearly indicate the increasing tendency of the failure rate of each model to the user. The recommended evaluation value corresponding to the may be displayed.
　なお、ここでは、メイン品質レポート７および推薦品質レポート８において、各相関関係がグラフ形式で表される場合を例示しているが、どのような形式で各相関関係が表されていてもよい。また、データ出力部１８は、ユーザがメイン品質レポート７および推薦品質レポート８の内容を確認することができれば、これらをどのように出力してもよく、例えば、紙に印刷する形で出力するように構成してもよいし、表示装置の画面に表示するように構成してもよい。 Note that, here, the main quality report 7 and the recommended quality report 8 illustrate the case where each correlation is represented in a graph format, but each correlation may be represented in any format. In addition, the data output unit 18 may output the main quality report 7 and the recommended quality report 8 in any way as long as the user can confirm the contents of the main quality report 7 and the recommended quality report 8. You may comprise so that it may display on the screen of a display apparatus.
　次に、多次元データ分析支援装置１の一連の動作例について、図１４を参照しながら説明する。図１４は、本発明の実施の形態１における多次元データ分析支援装置１の一連の動作例を示すフローチャートである。 Next, a series of operation examples of the multidimensional data analysis support apparatus 1 will be described with reference to FIG. FIG. 14 is a flowchart showing a series of operation examples of the multidimensional data analysis support apparatus 1 according to Embodiment 1 of the present invention.
　ステップＳ１０１において、推薦軸別中間集計部１１は、分析用入力データ（ここでは、故障情報ＤＢ２および契約情報ＤＢ３）から、比較条件４および推薦軸項目５に従って、ｘ軸項目および推薦軸項目５ごとにｙ軸中間値を算出して中間集計値ＤＢ１２に格納する。 In step S101, the recommended axis-by-axis intermediate totaling unit 11 determines each x-axis item and recommended axis item 5 according to the comparison condition 4 and the recommended axis item 5 from the input data for analysis (here, the failure information DB2 and the contract information DB3). Then, the y-axis intermediate value is calculated and stored in the intermediate total value DB 12.
　ステップＳ１０２において、統合集計部１３は、中間集計値ＤＢ１２から、ｘ軸項目ごとにｙ軸項目の値を、ｙ軸集計式を用いてｙ軸統合集計値として算出して統合集計値ＤＢ１５に格納する。 In step S102, the integrated tabulation unit 13 calculates the value of the y-axis item for each x-axis item from the intermediate tabulation value DB 12 as the y-axis integrated tabulation value using the y-axis tabulation formula, and stores the calculated value in the integrated tabulation value DB 15. To do.
　ステップＳ１０３において、推薦集計部１４は、中間集計値ＤＢ１２から、ｘ軸項目および推薦軸項目５ごとにｙ軸項目の値を、ｙ軸集計式を用いてｙ軸推薦集計値として算出して推薦集計値ＤＢ１６に格納する。 In step S103, the recommendation totaling unit 14 calculates the value of the y-axis item for each x-axis item and the recommended axis item 5 as the y-axis recommended total value using the y-axis totaling formula from the intermediate total value DB 12, and recommends it. Stored in the total value DB 16.
　ステップＳ１０４において、推薦集計部１４は、推薦集計値ＤＢ１６から、推薦方式６に従って、推薦軸項目５ごとに推薦評価値を算出するとともに、推薦軸項目５ごとの推薦評価値から推薦軸項目５の推薦順位を算出し、これらの算出結果を推薦評価値ＤＢ１７に格納する。 In step S <b> 104, the recommendation totaling unit 14 calculates a recommended evaluation value for each recommended axis item 5 from the recommended total value DB 16 according to the recommendation method 6, and calculates the recommended axis item 5 from the recommended evaluation value for each recommended axis item 5. The recommendation ranking is calculated, and these calculation results are stored in the recommendation evaluation value DB 17.
　ステップＳ１０５において、データ出力部１８は、統合集計値ＤＢ１５からメイン品質レポート７を作成して出力するとともに、推薦集計値ＤＢ１６および推薦評価値ＤＢ１７から推薦品質レポート８を作成して出力する。 In step S105, the data output unit 18 creates and outputs the main quality report 7 from the integrated summary value DB 15, and creates and outputs the recommended quality report 8 from the recommended summary value DB 16 and the recommended evaluation value DB 17.
　このように、多次元データ分析支援装置１によってステップＳ１０１～ステップＳ１０５の各処理が実行されることで、メイン品質レポート７および推薦品質レポート８が出力されるので、ユーザは、分析用入力データの内容に応じて、比較条件４、推薦軸項目５および推薦方式６を設定するだけで、分析用入力データに対する所望の分析を容易に行うことができる。 In this way, the main quality report 7 and the recommended quality report 8 are output by executing the processing of step S101 to step S105 by the multidimensional data analysis support apparatus 1, so that the user can analyze the input data for analysis. By simply setting the comparison condition 4, the recommended axis item 5 and the recommendation method 6 according to the contents, it is possible to easily perform a desired analysis on the input data for analysis.
　以上、本実施の形態１によれば、分析用入力データから、比較条件および推薦軸項目に従って、ｘ軸項目および推薦軸項目ごとにｙ軸中間値を算出する推薦軸別中間集計部と、ｘ軸項目および推薦軸項目ごとのｙ軸中間値から、ｘ軸項目および推薦軸項目ごとにｙ軸項目の値を、ｙ軸集計式を用いてｙ軸推薦集計値として算出する推薦集計部と、ｘ軸項目および推薦軸項目ごとのｙ軸推薦集計値から、ｘ軸項目およびｙ軸推薦集計値の相関関係を、推薦軸項目ごとに出力するデータ出力部を備えて構成する。 As described above, according to the first embodiment, the recommended axis-specific intermediate totaling unit that calculates the y-axis intermediate value for each x-axis item and recommended axis item according to the comparison condition and the recommended axis item from the input data for analysis, and x A recommendation totaling unit that calculates a value of a y-axis item for each x-axis item and each recommended axis item from a y-axis intermediate value for each axis item and each recommended axis item as a y-axis recommended total value using a y-axis totaling formula; A data output unit is provided that outputs a correlation between the x-axis item and the y-axis recommended total value for each recommended axis item from the y-axis recommended total value for each x-axis item and the recommended axis item.
　これにより、多次元データに対する非定型的な分析の容易化を実現することができる。また、ユーザの分析スキルによることなく、ユーザによる入力に応じた分析結果が出力されるので、多次元データに関してユーザに予想外の気づきを与えるとともに、分析スキルの少ないユーザに対しても、非定型的な分析の結果を周知することができる。さらに、特徴的な分析事例がユーザに提示されることで、そのような分析事例が保守業務の際の予備知識として認知されるので、その結果、保守品質の向上につながる。 This makes it possible to facilitate atypical analysis of multidimensional data. In addition, the analysis result according to the input by the user is output without depending on the user's analysis skill, so that the user is unexpectedly aware of the multi-dimensional data and is also atypical for the user with less analysis skill. The result of a typical analysis. Further, when a characteristic analysis example is presented to the user, such an analysis example is recognized as preliminary knowledge in maintenance work, and as a result, maintenance quality is improved.
　なお、本実施の形態１では、保守対象機器（例えば、昇降機）の保守業務に関する多次元データに対して本願発明を適用する場合を例示したが、これに限定されず、どのような多次元データに対しても適用可能であることはいうまでもない。 In the first embodiment, the case where the present invention is applied to multidimensional data related to maintenance work of a maintenance target device (for example, an elevator) is illustrated, but the present invention is not limited to this, and what kind of multidimensional data is used. Needless to say, this is also applicable to the above.

Claims (4)

  1. 　　分析用入力データと、
    　　推薦軸項目と、
    　　ｘ軸項目と、ｙ軸項目と、前記分析用入力データから前記ｘ軸項目および前記推薦軸項目ごとに前記ｙ軸項目の値を算出するためのｙ軸中間値およびｙ軸集計式とが規定された比較条件と、
    　が入力される多次元データ分析支援装置であって、
    　前記分析用入力データから、前記比較条件および前記推薦軸項目に従って、前記ｘ軸項目および前記推薦軸項目ごとに前記ｙ軸中間値を算出する推薦軸別中間集計部と、
    　前記推薦軸別中間集計部によって算出された前記ｘ軸項目および前記推薦軸項目ごとの前記ｙ軸中間値から、前記ｘ軸項目および前記推薦軸項目ごとに前記ｙ軸項目の値を、前記ｙ軸集計式を用いてｙ軸推薦集計値として算出する推薦集計部と、
    　前記推薦集計部によって算出された前記ｘ軸項目および前記推薦軸項目ごとの前記ｙ軸推薦集計値から、前記ｘ軸項目および前記ｙ軸推薦集計値の相関関係を、前記推薦軸項目ごとに出力するデータ出力部と、
    　を備えた多次元データ分析支援装置。     Input data for analysis,
    Recommended axis item,
    The x-axis item, the y-axis item, and the y-axis intermediate value and the y-axis aggregation formula for calculating the value of the y-axis item for each of the x-axis item and the recommended axis item from the input data for analysis are defined. Compared comparison conditions,
    Is a multidimensional data analysis support device,
    From the input data for analysis, according to the comparison condition and the recommended axis item, an intermediate totaling unit for each recommended axis that calculates the y-axis intermediate value for each of the x-axis item and the recommended axis item;
    The value of the y-axis item for each of the x-axis item and the recommended axis item is calculated from the x-axis item and the y-axis intermediate value for each recommended axis item calculated by the recommended axis-specific intermediate totaling unit. A recommendation counting unit that calculates a y-axis recommended total value using an axis totaling formula;
    A correlation between the x-axis item and the y-axis recommended total value is output for each recommended axis item from the x-axis item and the y-axis recommended total value for each recommended axis item calculated by the recommendation totaling unit. A data output unit to
    Multidimensional data analysis support device equipped with.
  2. 　前記比較条件は、推薦方式がさらに規定され、
    　前記推薦集計部は、
    　　算出された前記ｘ軸項目および前記推薦軸項目ごとの前記ｙ軸推薦集計値から、前記推薦方式に従って、前記推薦軸項目ごとに推薦評価値をさらに算出し、
    　前記データ出力部は、
    　　前記推薦集計部によって算出された前記推薦軸項目ごとの前記推薦評価値をさらに出力する
    　請求項１に記載の多次元データ分析支援装置。     The comparison condition further defines a recommendation method,
    The recommendation counting unit includes:
    Further calculating a recommended evaluation value for each recommended axis item according to the recommendation method from the calculated y-axis recommended aggregate value for each of the x-axis item and the recommended axis item,
    The data output unit includes:
    The multidimensional data analysis support apparatus according to claim 1, further outputting the recommendation evaluation value for each of the recommended axis items calculated by the recommendation aggregation unit.
  3. 　前記推薦集計部は、
    　　算出された前記推薦軸項目ごとの前記推薦評価値の大小関係から、前記推薦軸項目の推薦順位を算出し、
    　前記データ出力部は、
    　　前記推薦軸項目ごとの前記推薦評価値の代わりに、前記推薦集計部によって算出された前記推薦軸項目の推薦順位をさらに出力する
    　請求項２に記載の多次元データ分析支援装置。     The recommendation counting unit includes:
    From the calculated magnitude relationship of the recommendation evaluation values for each of the recommended axis items, the recommendation ranking of the recommended axis items is calculated,
    The data output unit includes:
    The multidimensional data analysis support device according to claim 2, further outputting a recommendation rank of the recommended axis item calculated by the recommendation totaling unit instead of the recommendation evaluation value for each recommended axis item.
  4. 　前記推薦軸別中間集計部によって算出された前記ｘ軸項目および前記推薦軸項目ごとの前記ｙ軸中間値から、前記ｘ軸項目ごとに前記ｙ軸項目の値を、前記ｙ軸集計式を用いてｙ軸統合集計値として算出する統合集計部をさらに備え、
    　前記データ出力部は、
    　　前記統合集計部によって算出された前記ｘ軸項目ごとの前記ｙ軸統合集計値から、前記ｘ軸項目および前記ｙ軸統合集計値の相関関係をさらに出力する
    　請求項１から３のいずれか１項に記載の多次元データ分析支援装置。     Based on the x-axis item calculated by the recommended axis-specific intermediate totaling unit and the y-axis intermediate value for each recommended axis item, the value of the y-axis item for each x-axis item is calculated using the y-axis aggregation formula. And an integrated tabulation unit that calculates the y-axis integrated tabulation value.
    The data output unit includes:
    The correlation of the said x-axis item and the said y-axis integrated total value is further output from the said y-axis integrated total value for every said x-axis item calculated by the said integrated total part, The any one of Claim 1 to 3 Multidimensional data analysis support device described in 1.
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