WO2016063502A1 - Knowledge management device, knowledge management method, and program recording medium - Google Patents

Knowledge management device, knowledge management method, and program recording medium Download PDF

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Publication number
WO2016063502A1
WO2016063502A1 PCT/JP2015/005212 JP2015005212W WO2016063502A1 WO 2016063502 A1 WO2016063502 A1 WO 2016063502A1 JP 2015005212 W JP2015005212 W JP 2015005212W WO 2016063502 A1 WO2016063502 A1 WO 2016063502A1
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Prior art keywords
node
factor
countermeasure
list
value
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PCT/JP2015/005212
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French (fr)
Japanese (ja)
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和大 船越
啓 榊
繁 細野
絵梨子 沼田
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日本電気株式会社
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Priority to JP2016555076A priority Critical patent/JPWO2016063502A1/en
Publication of WO2016063502A1 publication Critical patent/WO2016063502A1/en

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    • 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
    • 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
    • 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

Definitions

  • the present invention relates to a knowledge management device, a knowledge management method, and a program recording medium, in particular, a knowledge management device, a knowledge management method, and a program for supporting factor analysis when a goal cannot be achieved in research and development.
  • This relates to a recording medium.
  • Patent Document 1 discloses an information processing apparatus that objectively evaluates the degree of difficulty in achieving a goal and the degree of insufficient examination. This device outputs work guidelines according to the balance of the evaluation values together with the degree of difficulty in achieving each goal and the degree of insufficient examination.
  • the present invention aims to provide a knowledge management device, a knowledge management method, and a program for solving the above-described problems.
  • the knowledge management apparatus stores plan information storage means for storing a node value target that is a value of the node in association with the node, and a factor for changing the node value of the node in association with the node.
  • a cause-and-effect relationship storage means, and a factor specifying means for acquiring a node value record of a node and outputting a factor associated with the node if the record does not achieve the target associated with the node; Prepare.
  • a knowledge management method stores a node value target that is a value of the node in association with a node, stores a factor that changes the node value of the node in association with a node, and If the record of the value is acquired and the record does not achieve the target associated with the node, the factor associated with the node is output.
  • a recording medium stores, in a computer, processing for storing a node value target that is a value of the node in association with the node, and a factor for changing the node value of the node in association with the node. Records a program that executes a process and a process of outputting a factor associated with the node if the result of the node value of the node is acquired and the result does not achieve the target associated with the node .
  • the knowledge management device makes it easy for the user to analyze the cause when the goal cannot be achieved in the performance of research and development and business.
  • FIG. 1 is a block diagram showing the configuration of the knowledge management apparatus 101 according to the first embodiment.
  • FIG. 2 is a diagram illustrating the structure of the plan information 200.
  • FIG. 3 shows an example of the data structure of the causal relationship information 300 stored in the causal relationship storage unit 140.
  • FIG. 4 is a diagram illustrating a hardware configuration of a computer apparatus 700 that implements the knowledge management apparatus 101.
  • FIG. 5 is a diagram illustrating the operation of the factor specifying unit 150.
  • FIG. 6 is a block diagram illustrating a configuration of the knowledge management device 500 according to the second embodiment.
  • FIG. 7 shows an example of the countermeasure information 600 stored in the countermeasure storage unit 510.
  • FIG. 8 is a block diagram illustrating a configuration of the knowledge management device 550 according to the third embodiment.
  • FIG. 9 shows an example of the plan information 200 given to the knowledge management device 500.
  • FIG. 10 shows a partial expression example of the plan information (target) 910 on the computer device 700.
  • FIG. 11 shows the difference information 930.
  • FIG. 12 shows a partial expression example on the computer device 700 of the difference information 930.
  • FIG. 13 shows the main difference information 940 obtained from the difference information 930 by the main difference specifying process.
  • FIG. 14 shows a partial expression example of the main difference information 940 on the computer device 700.
  • FIG. 15 shows a format example of the causal relationship information 300 stored in the causal relationship storage unit 140.
  • FIG. 16 shows a partial expression example of the countermeasure information 600 on the computer device 700.
  • FIG. 17 shows an arrangement index of a specific data example.
  • FIG. 18 is a block diagram illustrating a configuration of a knowledge management apparatus 801 according to the fourth embodiment.
  • FIG. 1 is a block diagram showing a configuration of a knowledge management apparatus 101 according to the first embodiment of the present invention.
  • the knowledge management device 101 includes a target input unit 110, a result input unit 120, a plan information storage unit 130, a causal relationship storage unit 140, and a factor identification unit 150.
  • the target input unit 110, the result input unit 120, and the factor specifying unit 150 are configured by logic circuits.
  • the plan information storage unit 130 and the causal relationship storage unit 140 are configured by a storage device such as a semiconductor storage device or a disk storage device.
  • the target input unit 110, the result input unit 120, or the factor specifying unit 150 may be realized by a program stored in the storage unit 702 of the computer device 700 illustrated in FIG. 4 and executed by the CPU 701 (Central Processing Unit). good.
  • the target input unit 110 and the actual result input unit 120 read structured target data and actual result data (collectively, collectively referred to as plan information 200) from the outside.
  • the read plan information 200 is input to the plan information storage unit 130 and the factor specifying unit 150.
  • the plan information storage unit 130 stores the input plan information 200 so that the plan information 200 can be referred to from the outside, for example, as meta information of each design and development product.
  • the causal relationship storage unit 140 stores the causal relationship information 300 representing the relationship between environmental factors and each node of the structure of the plan information 200 (referred to as a causal relationship) as a many-to-many relationship.
  • the factor identifying unit 150 acquires the plan information 200 obtained from the target input unit 110, the result input unit 120, and the plan information storage unit 130, and the causal relationship information 300 obtained from the causal relationship storage unit 140. Then, the factor identifying unit 150 identifies a factor that causes a gap between the target and the performance with reference to the causal relationship information 300.
  • FIG. 2 is a diagram illustrating an example of the structure of the plan information 200.
  • the plan information 200 is a directed graph with a label represented by an information node 201 and an information link 202 that is a link between the information nodes 201.
  • Specific data of the plan information 200 is expressed as an information node 201 on the graph.
  • the information link 202 expresses an inclusion relationship or dependency relationship between data expressed by the information node 201.
  • Each information node 201 can have one or more attributes.
  • the attribute is, for example, a node label 203 indicating the content of data represented by the information node 201 such as “investment recovery plan”.
  • the node label is, for example, an identifier such as a character string or a URI (UniformifierResource ⁇ ⁇ ⁇ Identifier) in which a data structure is separately defined.
  • Another attribute of the information node 201 is, for example, a node value 204 that is a numerical value indicating a specific amount of money such as “2 million yen”, time, man-hour, and ratio.
  • the node value is also called an attribute value.
  • the plan information storage unit 130 stores plan information 200 representing a target and plan information 200 representing an actual result.
  • the plan information 200 includes, for example, an information node 201 representing an investment recovery plan, a target domain, a partner, and a value under a root node representing a goal.
  • the plan information 200 is referred to from, for example, a specific design / development product, and functions as metadata indicating a strategic goal or a strategy result for the design / development product.
  • FIG. 3 shows an example of the data structure of the causal relationship information 300 stored in the causal relationship storage unit 140.
  • the causal relationship information 300 includes one or more causal relationship nodes 301.
  • the example of the causal relationship information 300 in FIG. 3 includes six causal relationship nodes 301 of X1 to X6.
  • the causality node 301 has one or more plan information node links 302.
  • the plan information node link 302 is link information for one information node 201 of the plan information 200 (indicated by a broken line in FIG. 3).
  • the causal relationship node 301 has one or more factor links 304.
  • the factor link 304 is link information to the factor node 303 that represents a factor that affects the attribute of the information node 201 linked by the plan information node link 302.
  • the causal relationship information 300 may be expressed using a plurality of causal relationship nodes 301.
  • the causality node 301 In addition to the positive influence (when a factor occurs or increases, the attribute value of the plan information 200 increases), the causality node 301 has a negative effect (when the factor occurs or increases, the attribute of the plan information 200). The value may decrease).
  • the causal relationship node 301 may have an attribute for distinguishing a positive influence from a negative influence.
  • the causal relationship node 301 in FIG. 3 is an example having a positive influence, and “+” described in the causal relation node 301 represents a positive influence.
  • the causal node 301 named X1 has a plan information node link 302 with respect to the information node 201 of the node label 203 of “use unit price” of the plan information 200. Furthermore, this causal relationship node 301 has a factor link 304 to a factor node 303 having a factor of “price index”. The causal relationship node 301 has an attribute of “positive influence”. That is, the causal relationship node 301 named X1 indicates that the factor of the price index has a positive influence on the information node 201 having the attribute of the usage unit price.
  • the knowledge management apparatus 101 When there is a causal relationship node 301 having a plan information node link 302 for a lower level information node 201, the knowledge management apparatus 101 is also affected by the same factor in the upper level information node 201 of the information node 201. You may judge. Details will be described later (step S4 in FIG. 5).
  • FIG. 4 is a diagram illustrating a hardware configuration of the computer apparatus 700 that implements the knowledge management apparatus 101.
  • the computer device 700 includes a CPU 701, a storage unit 702, a storage device 703, an input unit 704, an output unit 705, and a communication unit 706.
  • the computer device 700 may include a recording medium 707 (or storage medium) supplied from outside and read / written by the storage device 703.
  • the recording medium 707 is a non-temporary recording medium that is a non-volatile recording medium that stores information non-temporarily.
  • the recording medium 707 may be a temporary recording medium that temporarily stores information.
  • the CPU 701 controls the overall operation of the computer apparatus 700 by operating an operating system (not shown). For example, the CPU 701 reads the program and data from the recording medium 707 mounted on the storage device 703 and writes the read program and data to the storage unit 702.
  • the program causes the computer device 700 to execute an operation of a flowchart described later.
  • the CPU 701 executes various processes as the target input unit 110, the result input unit 120, or the factor specifying unit 150 shown in FIG. 1 according to the read program and based on the read data.
  • the CPU 701 may download the program and its data to the storage unit 702 from an external device (not shown) connected to a communication network (not shown).
  • the storage unit 702 stores the program and data.
  • the storage unit 702 may store plan information 200 and causal relationship information 300, for example.
  • the storage unit 702 may be included in some of the target input unit 110, the result input unit 120, the plan information storage unit 130, the causal relationship storage unit 140, and the factor identification unit 150.
  • the storage device 703 is, for example, an optical disk, a flexible disk, a magnetic optical disk, an external hard disk device, a semiconductor memory device, and the like, and includes a recording medium 707.
  • the storage device 703 or the recording medium 707 stores the program in a computer-readable manner.
  • the storage device 703 may store the plan information 200 and the causal relationship information 300, for example.
  • the storage device 703 may be included as part of the target input unit 110, the result input unit 120, the plan information storage unit 130, the causal relationship storage unit 140, and the factor identification unit 150.
  • the input unit 704 receives an operation input by an operator and an input of information from the outside.
  • Devices used for the input operation are, for example, a mouse, a keyboard, a built-in key button, and a touch panel.
  • the input unit 704 may be included as part of the target input unit 110, the result input unit 120, the plan information storage unit 130, the causal relationship storage unit 140, and the factor identification unit 150.
  • the output unit 705 is realized by a display device, for example.
  • the output unit 705 is used, for example, for an input request to the operator by GUI (Graphical User Interface), output presentation to the operator, and the like.
  • the output unit 705 may be included as a part of the target input unit 110, the result input unit 120, the plan information storage unit 130, the causal relationship storage unit 140, and the factor identification unit 150.
  • the communication unit 706 implements an interface with an external system.
  • the communication unit 706 may be included as a part of the target input unit 110, the result input unit 120, the plan information storage unit 130, the causal relationship storage unit 140, and the factor identification unit 150.
  • each element of the knowledge management apparatus 101 shown in FIG. 1 may be realized by the computer apparatus 700 having a hardware configuration shown in FIG.
  • the means for realizing each unit included in the computer apparatus 700 is not limited to the above. That is, the computer device 700 may be realized by one physically coupled device, or may be realized by a plurality of these devices by connecting two or more physically separated devices in a wired or wireless manner. Good.
  • the CPU 701 may read and execute the program code stored in the recording medium 707. Alternatively, the CPU 701 may store the code of the program stored in the recording medium 707 in the storage unit 702, the storage device 703, or both.
  • the present embodiment includes an embodiment of a recording medium 707 that stores the program (software) executed by the CPU 701 of the computer apparatus 700 temporarily or non-temporarily.
  • a storage medium that stores information non-temporarily is also referred to as a non-volatile storage medium.
  • the factor identifying unit 150 first performs difference calculation (step S1) on the input of the two plan information 200 of the target and the actual value, and creates a difference graph.
  • the target and the actual result of the plan information 200 are an enumerated data set including at least data of a set of ⁇ information node 201, predicate, object> as elements.
  • This data is at least classified into link information and attribute value information.
  • the former is a structure of ⁇ information node 201, a predicate indicating a link relation, an object indicating information node 201>
  • the latter is a structure of ⁇ information node 201, a predicate indicating attribute value, an object indicating node value 204>. It has. However, the order may be changed.
  • the link information is, for example, ⁇ N1, hasLink, N2>
  • the attribute value information is ⁇ N3, hasValue, 600>.
  • the factor identification unit 150 estimates the correspondence between the target and actual information nodes 201 based on this structure, and calculates the difference between the attribute values of the corresponding nodes. It is assumed that the result data includes ⁇ n, a predicate indicating that the object is an attribute value, b1>, and the target data includes ⁇ n, a predicate indicating that the object is an attribute value, and b2>. .
  • “n” is the information node 201, and b1 and b2 are node values (attribute values).
  • the factor specifying unit 150 calculates b1-b2, and includes difference information ⁇ n, a predicate indicating that the object is an attribute value, and b1-b2> in the difference graph.
  • the factor identifying unit 150 can use, for example, maximum likelihood estimation or Bayesian estimation as an estimation method of the correspondence relationship of the information node 201.
  • specification part 150 does not need to estimate the correspondence of the information node 201.
  • the difference graph has the same structure as the plan information 200 or a subset structure thereof.
  • the factor specifying unit 150 performs a rounding process (Step S2) on the value of each node (hereinafter referred to as a difference node) of the calculated difference graph to obtain a rounded difference graph.
  • the rounding process removes non-strategic differences by rounding differences within a given error range.
  • the factor identifying unit 150 may not perform this process.
  • the factor specifying unit 150 extracts a set of difference nodes having a large difference, that is, a set of main difference nodes, from the rounded difference graph or the difference graph.
  • the factor specifying unit 150 may perform absolute evaluation of the difference, or may perform relative evaluation with respect to the target value or the actual value.
  • the factor specifying unit 150 may extract a specified number of difference nodes in descending order of difference.
  • the factor specifying unit 150 expands the causal relationship when the causal relationship information 300 stored in the causal relationship storage unit 140 is minimized.
  • each element included in the causal relationship information 300 is ⁇ causal relationship identifier, factor, attribute indicating positive or negative influence, node of difference graph>.
  • the factor specifying unit 150 has link information ⁇ node c, attribute d, node b> in the difference graph for the data ⁇ identifier x, factor a, +, node b> of the causal relationship node 301.
  • data ⁇ identifier x, factor a, +, node c> of the causality node 301 is generated.
  • the node c is an upper node of the node b.
  • the factor identifying unit 150 associates the factor a with the upper node c of the node b. Since the upper node c depends on the lower node b, it is affected by the same factor a as the lower node b. The factor identifying unit 150 reflects this in the causal relationship information 300 by performing this process.
  • the factor specifying unit 150 can skip this causal relationship structure development process (step S4).
  • the factor specifying unit 150 extracts factors that affect the main difference node from the expanded causal relationship set.
  • the main difference information 940 includes ⁇ identifier i, a predicate indicating that the object is a name, information node name> and ⁇ identifier i, a predicate indicating that the object is a value, a difference value>, or Or any one of them.
  • the causal relationship information 300 includes ⁇ identifier x, factor a, +, node i> as the causal relationship node 301, the factor specifying unit 150 extracts the factor a.
  • the factor identifying unit 150 may be output instead of a.
  • the knowledge management apparatus 101 makes it easy for the user to analyze the cause when the goal cannot be achieved in the performance of research and development, business, or the like.
  • the first reason is that the causal relationship storage unit 140 stores the causal relationship information 300.
  • the causal relationship information 300 associates the node value 204 of the node included in the plan information 200 with the factor that affects the node value 204.
  • the second reason is that the factor identifying unit 150 identifies a factor that affects the node value 204 in which the difference between the target and the actual result occurs in the plan information 200 with reference to the causal relationship information 300. is there.
  • FIG. 6 is a block diagram showing the configuration of the knowledge management device 500 according to the second embodiment of the present invention.
  • the knowledge management device 500 according to the present embodiment includes a countermeasure storage unit 510 and a countermeasure derivation unit 520 in addition to the configuration of the knowledge management device 101 according to the first embodiment.
  • Components similar to those of the knowledge management apparatus 101 of the first embodiment function in the same manner as the knowledge management apparatus 101 of the first embodiment. That is, for example, the factor specifying unit 150 included in the part extracts and outputs a set of factors that generate a difference between the target and the actual result of the plan information 200.
  • the countermeasure storage unit 510 stores information on countermeasures to be taken against the factors (hereinafter referred to as countermeasure information 600, details will be described later).
  • the measure deriving unit 520 receives the set of factors output from the factor specifying unit 150 and the measure information 600, extracts effective measures for the set of factors output by the factor specifying unit 150, and outputs them.
  • the countermeasure derivation unit 520 is composed of a logic circuit.
  • the countermeasure storage unit 510 includes a storage device such as a semiconductor storage device or a disk storage device.
  • the countermeasure derivation unit 520 may be realized by a program stored in the storage unit 702 of the computer device 700 and executed by the CPU 701.
  • FIG. 7 shows an example of the countermeasure information 600 stored in the countermeasure storage unit 510.
  • the countermeasure information 600 is information having a structure in which a factor node 610 representing a factor and a countermeasure node 620 representing a countermeasure are associated in many-to-many relationship with the factor / countermeasure relation information 611.
  • the countermeasure information 600 may include a hierarchical countermeasure description. In this case, the relationship between the comprehensive countermeasure A of the upper layer and the specific countermeasure B of the lower layer is represented by an inter-measure link 630 between the countermeasure node 620 of the countermeasure A and the countermeasure node 620 of the countermeasure B.
  • the measure information 600 is more formally defined as, for example, the following expressions (1) to (4). This structure or property may be extended and used.
  • V r is a set of factors
  • V c is a set of countermeasures.
  • E rc xy is a binary relationship indicating that the countermeasure x directly covers the factor y
  • E c xy is a binary relationship indicating that the countermeasure x directly covers the countermeasure y.
  • E ci xy is a binary relationship indicating that the countermeasure x covers the countermeasure y
  • E rci xy is a binary relationship indicating the relationship where the countermeasure x covers the factor y.
  • z in Formula (3) and Formula (4) means the element of Vc which satisfies the conditions described after the comma, respectively. [Description of operation] Each part generally operates as follows.
  • the measure deriving unit 520 uses the measure information 600 stored in the measure storing unit 510 to derive a set of measures covering the elements included in the set of factors that is the output of the factor specifying unit 150. This derivation means obtaining x satisfying E rci x y for all factors ⁇ y ⁇ V r in the above definition.
  • the countermeasure derivation unit 520 performs this derivation using, for example, a transposed index or a greedy method with multiple paths.
  • the countermeasure deriving unit 520 may use a greedy method that improves memory efficiency and disk efficiency.
  • the countermeasure derivation unit 520 operates as follows when the greedy method using a typical transposed index is used.
  • the countermeasure derivation unit 520 adds a new countermeasure to the set of countermeasures that are solution candidates, and calculates a set of covering factors.
  • the countermeasure to be added needs to be a countermeasure corresponding to the factor newly added to the factor set.
  • the measure deriving unit 520 first creates a list of measures (transposed index) that covers each factor. Using this list, the countermeasure derivation unit 520 adds a countermeasure that maximizes the number of factors newly added to the factor set to the set of candidate solutions.
  • the countermeasure derivation unit 520 searches for a set Y x of factors y satisfying E rci x y for each countermeasure x ⁇ V c .
  • the countermeasure derivation unit 520 outputs x as a countermeasure set.
  • the countermeasure derivation unit 520 calculates a union by combining the set Y x for the countermeasure x and the set Y x ′ for the countermeasure x ′ different from the countermeasure x. At this time, the countermeasure derivation unit 520 selects x ′ so that the union Y x ⁇ Y x ′ is maximized, that is, the number of factors newly covered by Y x ′ is maximized. . If Y x ⁇ Y x ′ includes all the input factor sets, the countermeasure deriving unit 520 outputs a countermeasure set ⁇ x, x ′ ⁇ .
  • the measure derivation unit 520 makes the same determination as above for the measure x ′′ that maximizes Y x ⁇ Y x ′ ⁇ Y x ′′. . Thereafter, the countermeasure deriving unit 520 adds the set of factors covered by the countermeasure as described above, and repeats the determination as to whether or not all the input factor sets are included. Finally, the countermeasure derivation unit 520 outputs a set of countermeasures covering all the factors. [Description of effects]
  • the knowledge management device 500 according to the present embodiment makes it easy for the user to take measures against the cause when the goal cannot be achieved in the performance of research and development and business. The reason is that the countermeasure derivation unit 520 refers to the countermeasure information 600 and presents a countermeasure for the factor that the target cannot be achieved.
  • the knowledge management apparatus 500 of the present embodiment can reduce the time required for disk input / output in deriving the countermeasure. The reason is that since the countermeasure derivation unit 520 uses an inverted index when deriving the countermeasure, it is not necessary to read the countermeasure information 600 read once.
  • FIG. 8 is a block diagram showing the configuration of the knowledge management device 550 according to the third embodiment of the present invention.
  • the knowledge management device 550 of the present embodiment includes a countermeasure storage unit 560 and a countermeasure derivation unit 570 in addition to the configuration of the knowledge management apparatus 101 of the first embodiment described above.
  • Components similar to those of the knowledge management apparatus 101 of the first embodiment function in the same manner as the knowledge management apparatus 101 of the first embodiment. That is, for example, the factor specifying unit 150 included in the part extracts and outputs a set of factors that generate a difference between the target and the actual result of the plan information 200.
  • the countermeasure storage unit 560 stores the countermeasure information 600 as in the second embodiment.
  • the countermeasure information 600 of the present embodiment is extended to weighted data. That is, the countermeasure node 620 of the countermeasure information 600 is given a weight.
  • the countermeasure deriving unit 570 receives the set of factors output from the factor identifying unit 150 and the countermeasure information 600 as in the second embodiment, and extracts effective measures for the set of factors output by the factor identifying unit 150. And output.
  • the countermeasure deriving unit 570 is extended to process weighted data. For example, the countermeasure derivation unit 570 searches for countermeasures in descending order of countermeasures.
  • the countermeasure derivation unit 570 is configured by a logic circuit.
  • the countermeasure storage unit 560 includes a storage device such as a semiconductor storage device or a disk storage device.
  • the countermeasure derivation unit 570 may be realized by a program stored in the storage unit 702 of the computer device 700 and executed by the CPU 701. [Description of operation]
  • the countermeasure derivation unit 570 using the multi-pass method operates as follows.
  • a set of factors input from the factor specifying unit 150 is Y.
  • the countermeasure derivation unit 570 determines, for each countermeasure x ⁇ V c , the x that maximizes the common part Y ⁇ Y x with the set Y among the set Y x of the factors y satisfying E rci x y. Explore.
  • the countermeasure derivation unit 570 outputs x as a solution.
  • the countermeasure derivation unit 570 obtains a set Y x ′ of factors y satisfying E rci x ′ y for each countermeasure x′ ⁇ V c different from the countermeasure x, and obtains a common part Y ⁇ Y x ⁇ Y. Search for x ′ that maximizes x ′ .
  • the countermeasure derivation unit 570 outputs ⁇ x, x ′ ⁇ as a solution.
  • the countermeasure derivation unit 570 repeatedly applies the above procedure, sequentially adds countermeasures, and obtains a set of countermeasures covering all the set Y.
  • the knowledge management device 550 of the present embodiment makes it easy for the user to take measures against the cause when the goal cannot be achieved in the performance of research and development and business. The reason is that the countermeasure derivation unit 570 refers to the countermeasure information 600 and presents a countermeasure for the factor that the target cannot be achieved.
  • the knowledge management device 550 makes it easy for the user to prioritize appropriate, for example, effective measures.
  • the reason is that the countermeasure derivation unit 570 searches for the countermeasure in consideration of the weight assigned to the countermeasure.
  • the knowledge management device 550 of the present embodiment can reduce the memory capacity used in deriving the countermeasure.
  • the reason is that the countermeasure deriving unit 570 does not create an inverted index because it uses the multi-pass method.
  • the knowledge management device 550 of this embodiment improves the input / output efficiency of the physical disk in deriving the countermeasure.
  • the reason is that the countermeasure deriving unit 570 uses the multi-pass method and therefore does not use random access for scanning the physical disk.
  • FIG. 18 is a block diagram illustrating a configuration of a knowledge management apparatus 801 according to the fourth embodiment.
  • the knowledge management device 801 includes a plan information storage unit 830, a causal relationship storage unit 840, and a factor identification unit 850.
  • the plan information storage unit 830 stores a node value target that is a value of the node in association with the node.
  • the causal relationship storage unit 840 stores a factor that changes the node value of the node in association with the node.
  • the factor specifying unit 850 acquires the node value result of the node, and outputs the factor associated with the node when the result does not achieve the target associated with the node.
  • the knowledge management device 801 makes it easy for the user to analyze the cause when the goal cannot be achieved in the performance of research and development and business.
  • the reason is that the factor identifying unit 850 identifies the factor that affects the node where the difference between the plan target and the actual performance occurs with reference to the causal relationship storage unit 840.
  • FIG. 9 shows a specific example of the plan information 200 given to the knowledge management device 500.
  • the given plan information 200 is plan information (target) 910 including a target and plan information (actual) 920 including an actual value.
  • FIG. 10 shows a partial expression example of the plan information (target) 910 on the computer device 700.
  • the plan information (target) 910 includes information nodes 201 N1 to N4.
  • the node N1 indicates that the attribute value is OR-Node, and the target value is either monthly sales of the node N2 or on-demand sales of the node N3.
  • the node N3 indicates that on-demand sales are obtained by the operand and operator defined by the node N4, and the target value is 600.
  • FIG. 9 shows that the plan information (actual result) 920 also has the same structure as the plan information (target) 910. However, for example, the on-demand sales node value 204 (actual value) of the node N3 indicates 240.
  • the factor specifying unit 150 performs the processing from S1 to S5 for these inputs. First, the factor identifying unit 150 obtains difference information 930 by difference calculation (step S1).
  • FIG. 11 shows the difference information 930.
  • the difference information 930 has the same structure as the plan information (target) 910 and the plan information (actual result) 920.
  • the node value 204 of the difference information 930 is a difference between the node values 204 of the corresponding information nodes 201 of the plan information (target) 910 and the plan information (actual result) 920.
  • the node value 204 of on-demand sales of the node N3 is ⁇ 360, which is the difference between 600 and 240 described above. The same can be said for the node values 204 of other nodes of the difference information 930.
  • FIG. 12 shows a partial expression example on the computer device 700 of the difference information 930.
  • the factor specifying unit 150 performs, for example, an integer difference rounding process (step S2). However, this process does not affect the result in this embodiment. Therefore, the output of this process remains as shown in FIGS.
  • the factor identifying unit 150 performs a main difference identifying process (step S3).
  • the factor specifying unit 150 selects, for example, three that have a large ratio of the difference to the target value.
  • FIG. 13 shows the main difference information 940 obtained from the difference information 930 by this main difference specifying process.
  • the difference information 930 indicates that the difference in on-demand sales at the node N3 is 60% different from the target. Similarly, the difference information 930 indicates that the number of uses of the node N6 is 60%, the use frequency of the node N9 is 50%, and the number of customers of the node N8 is 20%.
  • the factor specifying unit 150 includes 60% of the on-demand sales of the node N3, 60% of the usage count of the node N6, and 50% of the usage frequency of the node N9 in the main difference information 940.
  • FIG. 14 shows a partial expression example of the main difference information 940 on the computer device 700. This main difference information 940 becomes the input of the main cause-effect relationship specifying process (step S5).
  • the causal relationship storage unit 140 stores the causal relationship information 300 of FIG.
  • FIG. 15 shows a format example of the causal relationship information 300 stored in the causal relationship storage unit 140.
  • the factor identifying unit 150 executes the causal relationship structure development process (step S4), the factor (for example, importance) associated with the lower node (for example, usage frequency) is changed to the upper node ( For example, the number of uses and sales on demand). This means that a new record is added to the table of FIG.
  • the main difference information 940 shown in FIG. 13 and the causal relationship information 300 output in the causal relationship structure development process (step S4) are input to the main cause-and-effect relationship specifying process (step S5).
  • N3, N6, and N9 are the main differences, and these plan information names are on-demand sales, usage count, and usage frequency, respectively.
  • the factor specifying unit 150 searches the causal relationship information 300 for these plan information names.
  • the factor specifying unit 150 obtains convenience, recognition, price index, and importance as factors that have a positive impact on on-demand sales.
  • the factor identifying unit 150 obtains convenience, importance, and recognition as factors that have a positive effect on the number of uses.
  • the factor identifying unit 150 obtains convenience and importance as factors that have a positive influence on the usage frequency.
  • the factor specifying unit 150 outputs ⁇ price index, importance, convenience, awareness ⁇ as a set of factors.
  • the factor specifying unit 150 may output this subset in consideration of the importance of the factor, the number of times of selection, and the like.
  • the factor specifying unit 150 outputs ⁇ convenience, importance, price index ⁇ .
  • the countermeasure storage unit 510 stores countermeasure information 600 shown in FIG. FIG. 16 shows a partial expression example of the countermeasure information 600 on the computer device 700.
  • the countermeasure deriving unit 520 uses the countermeasure information 600 to derive a countermeasure covering the set of factors output by the factor identifying unit 150 in the main factor-effect relationship identifying process (step S5).
  • the measure deriving unit 520 calculates as follows. First, the countermeasure derivation unit 520 calculates a transposed index. That is, the measure deriving unit 520 obtains all measures covering each factor from all factors covered by each measure (may be limited to the factors input from the factor specifying unit 150).
  • a set of measures covering convenience is ⁇ premium strategy, integration with other company's products, multi-functionality, cooperative operation with other company's products, functional enhancement ⁇ .
  • a set of measures covering the importance is ⁇ advertisement, cross-advertisement, exclusion strategy, integration with other products of the company ⁇ .
  • the set of measures covering the price index is ⁇ changing the structure of usage fees, step-by-step pricing, provision of low-priced versions, premium strategy, integration with other products ⁇ .
  • the countermeasure deriving unit 520 sequentially searches the factors covered by each of these countermeasures, and obtains the transposed index, that is, the column data of the table shown in FIG.
  • the countermeasure derivation unit 520 selects integration with other products of the company, which is the countermeasure that maximizes the covering. This countermeasure and integration with other products of the company cover all three factors ⁇ convenience, importance, price index ⁇ output by the factor specifying unit 150. Therefore, the countermeasure derivation unit 520 outputs integration with other products of the company as a countermeasure.
  • the output of the factor identification unit 150 is ⁇ recognition, convenience, importance ⁇ .
  • the countermeasure deriving unit 520 first selects integration with other products of the company after calculating the inverted index of FIG. Next, the countermeasure deriving unit 520 selects one of the countermeasures ⁇ advertisement, cross advertisement ⁇ covering the degree of recognition that is not yet covered. Since the set of countermeasures covers all the input factors at this point, the countermeasure derivation unit 520 may select ⁇ integrate and promote with other products of the company ⁇ or ⁇ integrate with other products of the company and cross-advertisement ⁇ . Output one of the countermeasure sets.
  • the present invention can be applied to uses such as judging progress in planning and supporting decision making of measures to be taken.
  • the present invention can also be applied to uses such as proposing a goal to be set for a product similar to a managed product.

Abstract

　In the event that an objective cannot be achieved when research and development are performed, business is conducted, etc., the present invention facilitates the role played by a user in the failure. A knowledge management device is provided with: a plan information holding means for storing, in association with a node, the objective of a node value that is the value of the node; a cause-and-effect-relationship-holding means for storing, in association with the node, the factor that causes the node value of the node to change; and a factor specification means for acquiring the performance record of the node value of the node and, when the performance record does not achieve the objective associated with the node, outputting the factor associated with the node.

Description

知識管理装置、知識管理方法、及び、プログラムの記録媒体Knowledge management device, knowledge management method, and program recording medium
　本発明は、知識管理装置、知識管理方法、及び、プログラムの記録媒体、特に、研究開発等において、目標を達成できなかった場合の要因分析を支援する知識管理装置、知識管理方法、及び、プログラムの記録媒体に関する。 The present invention relates to a knowledge management device, a knowledge management method, and a program recording medium, in particular, a knowledge management device, a knowledge management method, and a program for supporting factor analysis when a goal cannot be achieved in research and development. This relates to a recording medium.
　研究開発、事業等の遂行において、その推進者は、投資と回収等について目標を立て、その達成を目指す。しかし、多くの場合、研究開発、事業等は計画通り進まず、目標を達成できない。そのような場合、目標を達成できない要因を分析することが必要となる。 In conducting R & D and business, the proponents set goals for investment and recovery, and aim to achieve them. However, in many cases, research and development, business, etc. do not proceed as planned and the target cannot be achieved. In such cases, it is necessary to analyze the factors that fail to achieve the goal.
　特許文献１は、目標の達成難易度および検討不十分度を客観的に評価する情報処理装置を開示する。この装置は、各目標の達成難易度および検討不十分度とともに、それらの評価値のバランスに応じた作業指針を出力する。 Patent Document 1 discloses an information processing apparatus that objectively evaluates the degree of difficulty in achieving a goal and the degree of insufficient examination. This device outputs work guidelines according to the balance of the evaluation values together with the degree of difficulty in achieving each goal and the degree of insufficient examination.
特開2000-181960号公報Japanese Unexamined Patent Publication No. 2000-181960
　研究開発、事業等が計画通り進まない要因は、目標の達成難度、検討不十分に限られない。研究開発、事業等の内容、取り巻く環境などに応じて種々の要因が存在する。特許文献１が開示する装置は、そのような種々の要因の分析には利用できない。 要 因 Factors that prevent R & D, business, etc. from proceeding as planned are not limited to the difficulty of achieving the target and insufficient consideration. There are various factors depending on the contents of research and development, business, and the surrounding environment. The apparatus disclosed in Patent Document 1 cannot be used for analysis of such various factors.
　本願発明は、上述の課題を解決するための知識管理装置、知識管理方法、及び、プログラムを提供することを目的とする。 The present invention aims to provide a knowledge management device, a knowledge management method, and a program for solving the above-described problems.
　本発明の一実施形態の知識管理装置は、ノードに関連付けて当該ノードの値であるノード値の目標を記憶する計画情報保管手段と、ノードに関連付けて当該ノードのノード値を変化させる要因を記憶する因果関係保管手段と、ノードのノード値の実績を取得して、実績が当該ノードに関連付けられた目標を達成していないと、当該ノードに関連付けられた要因を出力する要因特定手段と、を備える。 The knowledge management apparatus according to an embodiment of the present invention stores plan information storage means for storing a node value target that is a value of the node in association with the node, and a factor for changing the node value of the node in association with the node. A cause-and-effect relationship storage means, and a factor specifying means for acquiring a node value record of a node and outputting a factor associated with the node if the record does not achieve the target associated with the node; Prepare.
　本発明の一実施形態の知識管理方法は、ノードに関連付けて当該ノードの値であるノード値の目標を記憶し、ノードに関連付けて当該ノードのノード値を変化させる要因を記憶し、ノードのノード値の実績を取得して、実績が当該ノードに関連付けられた目標を達成していないと、当該ノードに関連付けられた要因を出力する。 A knowledge management method according to an embodiment of the present invention stores a node value target that is a value of the node in association with a node, stores a factor that changes the node value of the node in association with a node, and If the record of the value is acquired and the record does not achieve the target associated with the node, the factor associated with the node is output.
　本発明の一実施形態の記録媒体は、コンピュータに、ノードに関連付けて当該ノードの値であるノード値の目標を記憶する処理と、ノードに関連付けて当該ノードのノード値を変化させる要因を記憶する処理と、ノードのノード値の実績を取得して、実績が当該ノードに関連付けられた目標を達成していないと、当該ノードに関連付けられた要因を出力する処理と、を実行させるプログラムを記録する。 A recording medium according to an embodiment of the present invention stores, in a computer, processing for storing a node value target that is a value of the node in association with the node, and a factor for changing the node value of the node in association with the node. Records a program that executes a process and a process of outputting a factor associated with the node if the result of the node value of the node is acquired and the result does not achieve the target associated with the node .
　本発明にかかる知識管理装置は、研究開発、事業等の遂行において、目標が達成できない場合、ユーザがその要因を分析することを容易にする。 The knowledge management device according to the present invention makes it easy for the user to analyze the cause when the goal cannot be achieved in the performance of research and development and business.
図１は、第１の実施形態に係る知識管理装置１０１の構成を示すブロック図である。FIG. 1 is a block diagram showing the configuration of the knowledge management apparatus 101 according to the first embodiment. 図２は、計画情報２００の構造を示す図である。FIG. 2 is a diagram illustrating the structure of the plan information 200. 図３は、因果関係保管部１４０に保管される因果関係情報３００のデータ構造の例を示す。FIG. 3 shows an example of the data structure of the causal relationship information 300 stored in the causal relationship storage unit 140. 図４は、知識管理装置１０１を実現するコンピュータ装置７００のハードウェア構成を示す図である。FIG. 4 is a diagram illustrating a hardware configuration of a computer apparatus 700 that implements the knowledge management apparatus 101. 図５は、要因特定部１５０の動作を示す図である。FIG. 5 is a diagram illustrating the operation of the factor specifying unit 150. 図６は、第２の実施形態に係る知識管理装置５００の構成を示すブロック図である。FIG. 6 is a block diagram illustrating a configuration of the knowledge management device 500 according to the second embodiment. 図７は、対策保管部５１０に保存される対策情報６００の例を示す。FIG. 7 shows an example of the countermeasure information 600 stored in the countermeasure storage unit 510. 図８は、第３の実施形態に係る知識管理装置５５０の構成を示すブロック図である。FIG. 8 is a block diagram illustrating a configuration of the knowledge management device 550 according to the third embodiment. 図９は、知識管理装置５００に与えられる計画情報２００の例を示す。FIG. 9 shows an example of the plan information 200 given to the knowledge management device 500. 図１０は、計画情報（目標）９１０の部分的なコンピュータ装置７００上の表現例を示す。FIG. 10 shows a partial expression example of the plan information (target) 910 on the computer device 700. 図１１は差分情報９３０を示す。FIG. 11 shows the difference information 930. 図１２は、差分情報９３０のコンピュータ装置７００上の部分的な表現例を示す。FIG. 12 shows a partial expression example on the computer device 700 of the difference information 930. 図１３は、主要差分特定処理により、差分情報９３０から得られた主要差分情報９４０を示す。FIG. 13 shows the main difference information 940 obtained from the difference information 930 by the main difference specifying process. 図１４は、主要差分情報９４０のコンピュータ装置７００上での部分的な表現例を示す。FIG. 14 shows a partial expression example of the main difference information 940 on the computer device 700. 図１５は、因果関係保管部１４０が格納している因果関係情報３００の形式例を示す。FIG. 15 shows a format example of the causal relationship information 300 stored in the causal relationship storage unit 140. 図１６は、対策情報６００のコンピュータ装置７００上の部分的な表現例を示す。FIG. 16 shows a partial expression example of the countermeasure information 600 on the computer device 700. 図１７は、具体的データ例の配置インデックスを示す。FIG. 17 shows an arrangement index of a specific data example. 図１８は、第４の実施形態に係る知識管理装置８０１の構成を示すブロック図である。FIG. 18 is a block diagram illustrating a configuration of a knowledge management apparatus 801 according to the fourth embodiment.
　＜第１の実施の形態＞
 [構成の説明]
　次に、発明を実施するための形態について図面を参照して詳細に説明する。 <First Embodiment>
[Description of configuration]
Next, embodiments for carrying out the invention will be described in detail with reference to the drawings.
　図１は、本発明の第１の実施形態に係る知識管理装置１０１の構成を示すブロック図である。知識管理装置１０１は、目標入力部１１０、実績入力部１２０、計画情報保管部１３０、因果関係保管部１４０、および、要因特定部１５０を含む。 FIG. 1 is a block diagram showing a configuration of a knowledge management apparatus 101 according to the first embodiment of the present invention. The knowledge management device 101 includes a target input unit 110, a result input unit 120, a plan information storage unit 130, a causal relationship storage unit 140, and a factor identification unit 150.
　目標入力部１１０、実績入力部１２０、および、要因特定部１５０は、論理回路で構成される。計画情報保管部１３０、および、因果関係保管部１４０は、半導体記憶装置、ディスク記憶装置等の記憶装置で構成される。目標入力部１１０、実績入力部１２０、または、要因特定部１５０は、図４に示すコンピュータ装置７００の記憶部７０２に格納されて、ＣＰＵ７０１（Central Processing Unit）により実行されるプログラムで実現されても良い。 The target input unit 110, the result input unit 120, and the factor specifying unit 150 are configured by logic circuits. The plan information storage unit 130 and the causal relationship storage unit 140 are configured by a storage device such as a semiconductor storage device or a disk storage device. The target input unit 110, the result input unit 120, or the factor specifying unit 150 may be realized by a program stored in the storage unit 702 of the computer device 700 illustrated in FIG. 4 and executed by the CPU 701 (Central Processing Unit). good.
　各部はそれぞれ概略つぎのように動作する。 Each part operates as follows.
　目標入力部１１０および実績入力部１２０は、それぞれ構造化された目標データおよび実績データ（これらを併せて計画情報２００と総称する）を外部から読み込む。読み込まれた計画情報２００は計画情報保管部１３０および要因特定部１５０に入力される。 The target input unit 110 and the actual result input unit 120 read structured target data and actual result data (collectively, collectively referred to as plan information 200) from the outside. The read plan information 200 is input to the plan information storage unit 130 and the factor specifying unit 150.
　計画情報保管部１３０は、入力された計画情報２００を保管し、例えば各設計開発成果物のメタ情報として、計画情報２００を外部から参照できるようにする。因果関係保管部１４０は、環境要因と、計画情報２００の構造の各ノードとの関係（因果関係と呼称する）を多対多の関係として表した因果関係情報３００を保管する。 The plan information storage unit 130 stores the input plan information 200 so that the plan information 200 can be referred to from the outside, for example, as meta information of each design and development product. The causal relationship storage unit 140 stores the causal relationship information 300 representing the relationship between environmental factors and each node of the structure of the plan information 200 (referred to as a causal relationship) as a many-to-many relationship.
　要因特定部１５０は、目標入力部１１０、実績入力部１２０、および、計画情報保管部１３０から得られる計画情報２００と、因果関係保管部１４０から得られる因果関係情報３００を取得する。そして、要因特定部１５０は、目標と実績のギャップを発生させる要因を、因果関係情報３００を参照して特定する。 The factor identifying unit 150 acquires the plan information 200 obtained from the target input unit 110, the result input unit 120, and the plan information storage unit 130, and the causal relationship information 300 obtained from the causal relationship storage unit 140. Then, the factor identifying unit 150 identifies a factor that causes a gap between the target and the performance with reference to the causal relationship information 300.
　図２は、計画情報２００の構造の例を示す図である。計画情報２００は、情報ノード２０１、および、情報ノード２０１間のリンクである情報リンク２０２で表現されるラベル付き有向グラフである。計画情報２００の具体的データは、グラフ上の情報ノード２０１として表現される。情報リンク２０２は、情報ノード２０１が表現するデータ間の包含関係、あるいは、依存関係を表現する。 FIG. 2 is a diagram illustrating an example of the structure of the plan information 200. The plan information 200 is a directed graph with a label represented by an information node 201 and an information link 202 that is a link between the information nodes 201. Specific data of the plan information 200 is expressed as an information node 201 on the graph. The information link 202 expresses an inclusion relationship or dependency relationship between data expressed by the information node 201.
　各情報ノード２０１は、１以上の属性を持つことができる。属性は、例えば、「投資回収計画」といった当該情報ノード２０１が表現するデータの内容を示すノードラベル２０３である。ノードラベルは、例えば、文字列、あるいは、別途データ構造が定義されるＵＲＩ（Uniform Resource Identifier）のような識別子である。情報ノード２０１の他の属性は、例えば、「２００万円」といった具体的な金額や時間、工数、割合を示す数値であるノード値２０４である。ノード値は、属性値とも呼ばれる。 Each information node 201 can have one or more attributes. The attribute is, for example, a node label 203 indicating the content of data represented by the information node 201 such as “investment recovery plan”. The node label is, for example, an identifier such as a character string or a URI (UniformifierResource さ れ る Identifier) in which a data structure is separately defined. Another attribute of the information node 201 is, for example, a node value 204 that is a numerical value indicating a specific amount of money such as “2 million yen”, time, man-hour, and ratio. The node value is also called an attribute value.
　計画情報保管部１３０は、目標を表す計画情報２００と、実績を表す計画情報２００の２つを格納する。図２が示すように、計画情報２００は、例えば、目標を表すルートノード配下に、投資回収計画、ターゲットドメイン、パートナー、および、価値を表す情報ノード２０１を含む。 The plan information storage unit 130 stores plan information 200 representing a target and plan information 200 representing an actual result. As illustrated in FIG. 2, the plan information 200 includes, for example, an information node 201 representing an investment recovery plan, a target domain, a partner, and a value under a root node representing a goal.
　計画情報２００は、例えば、特定の設計開発成果物から参照されて、当該設計開発成果物に対する戦略目標あるいは戦略実績を示すメタデータとして機能する。 The plan information 200 is referred to from, for example, a specific design / development product, and functions as metadata indicating a strategic goal or a strategy result for the design / development product.
　図３は、因果関係保管部１４０に保管される因果関係情報３００のデータ構造の例を示す。因果関係情報３００は、１以上の因果関係ノード３０１を含む。図３の因果関係情報３００の例は、Ｘ１乃至Ｘ６の６つの因果関係ノード３０１を包含している。 FIG. 3 shows an example of the data structure of the causal relationship information 300 stored in the causal relationship storage unit 140. The causal relationship information 300 includes one or more causal relationship nodes 301. The example of the causal relationship information 300 in FIG. 3 includes six causal relationship nodes 301 of X1 to X6.
　因果関係ノード３０１は、１以上の計画情報ノードリンク３０２を持つ。計画情報ノードリンク３０２は、計画情報２００（図３において破線で示す）の１つの情報ノード２０１に対するリンク情報である。さらに、因果関係ノード３０１は、１以上の要因リンク３０４を持つ。要因リンク３０４は、計画情報ノードリンク３０２でリンクされた情報ノード２０１の属性に影響を与える要因を表す要因ノード３０３へのリンク情報である。 The causality node 301 has one or more plan information node links 302. The plan information node link 302 is link information for one information node 201 of the plan information 200 (indicated by a broken line in FIG. 3). Further, the causal relationship node 301 has one or more factor links 304. The factor link 304 is link information to the factor node 303 that represents a factor that affects the attribute of the information node 201 linked by the plan information node link 302.
　ただし、ある因果関係ノード３０１が、複数の情報ノード２０１の属性に対する計画情報ノードリンク３０２を持つとき、因果関係情報３００は、これらを複数の因果関係ノード３０１を用いて表されてもよい。 However, when a certain causal relationship node 301 has a plan information node link 302 for the attributes of a plurality of information nodes 201, the causal relationship information 300 may be expressed using a plurality of causal relationship nodes 301.
　また、因果関係ノード３０１は、正の影響（要因が発生あるいは増加したとき、計画情報２００の属性値が増加する）の他、負の影響（要因が発生あるいは増加したとき、計画情報２００の属性値が減少する）を表現してもよい。因果関係ノード３０１は、正の影響と負の影響を区別するための属性を有していても良い。図３の因果関係ノード３０１は正の影響を持つ例であり、因果関係ノード３０１内に記載された「＋」は、正の影響を表現している。 In addition to the positive influence (when a factor occurs or increases, the attribute value of the plan information 200 increases), the causality node 301 has a negative effect (when the factor occurs or increases, the attribute of the plan information 200). The value may decrease). The causal relationship node 301 may have an attribute for distinguishing a positive influence from a negative influence. The causal relationship node 301 in FIG. 3 is an example having a positive influence, and “+” described in the causal relation node 301 represents a positive influence.
　図３において、例えば、Ｘ１と名付けられた因果関係ノード３０１は、計画情報２００の「利用単価」というノードラベル２０３の情報ノード２０１に対して計画情報ノードリンク３０２を有する。さらに、この因果関係ノード３０１は、「物価指数」という要因を持つ要因ノード３０３に対して要因リンク３０４を有する。また、この因果関係ノード３０１は、「正の影響」という属性を持つ。すなわち、Ｘ１と名付けられた因果関係ノード３０１は、物価指数という要因が、利用単価という属性を持つ情報ノード２０１に、正の影響を持つことを示す。 3, for example, the causal node 301 named X1 has a plan information node link 302 with respect to the information node 201 of the node label 203 of “use unit price” of the plan information 200. Furthermore, this causal relationship node 301 has a factor link 304 to a factor node 303 having a factor of “price index”. The causal relationship node 301 has an attribute of “positive influence”. That is, the causal relationship node 301 named X1 indicates that the factor of the price index has a positive influence on the information node 201 having the attribute of the usage unit price.
　知識管理装置１０１は、下位レベルの情報ノード２０１に対して計画情報ノードリンク３０２を有する因果関係ノード３０１が存在するとき、当該情報ノード２０１の上位レベルの情報ノード２０１も、同じ要因の影響を受けると判断しても良い。詳細は、後述する（図５のステップＳ４）。 When there is a causal relationship node 301 having a plan information node link 302 for a lower level information node 201, the knowledge management apparatus 101 is also affected by the same factor in the upper level information node 201 of the information node 201. You may judge. Details will be described later (step S4 in FIG. 5).
　次に、知識管理装置１０１が、コンピュータ装置７００と、そのうえで稼働するプログラムで構成した場合の、ハードウェア構成について説明する。 Next, a hardware configuration when the knowledge management apparatus 101 is configured by the computer apparatus 700 and a program that operates on the computer apparatus 700 will be described.
　図４は、知識管理装置１０１を実現するコンピュータ装置７００のハードウェア構成を示す図である。コンピュータ装置７００は、ＣＰＵ７０１、記憶部７０２、記憶装置７０３、入力部７０４、出力部７０５及び通信部７０６を含む。 FIG. 4 is a diagram illustrating a hardware configuration of the computer apparatus 700 that implements the knowledge management apparatus 101. As illustrated in FIG. The computer device 700 includes a CPU 701, a storage unit 702, a storage device 703, an input unit 704, an output unit 705, and a communication unit 706.
　さらに、コンピュータ装置７００は、外部から供給され、記憶装置７０３で読み書きされる記録媒体７０７（または記憶媒体）を含んで良い。例えば、記録媒体７０７は、情報を非一時的に記憶する不揮発性記録媒体である、非一時的記録媒体である。また、記録媒体７０７は、情報を一時的に記憶する一時的記録媒体であってもよい。 Further, the computer device 700 may include a recording medium 707 (or storage medium) supplied from outside and read / written by the storage device 703. For example, the recording medium 707 is a non-temporary recording medium that is a non-volatile recording medium that stores information non-temporarily. Further, the recording medium 707 may be a temporary recording medium that temporarily stores information.
　ＣＰＵ７０１は、オペレーティングシステム（不図示）を動作させて、コンピュータ装置７００の全体の動作を制御する。例えば、ＣＰＵ７０１は、記憶装置７０３に装着された記録媒体７０７から、そのプログラムやデータを読み込み、読み込んだそのプログラムやそのデータを記憶部７０２に書き込む。 The CPU 701 controls the overall operation of the computer apparatus 700 by operating an operating system (not shown). For example, the CPU 701 reads the program and data from the recording medium 707 mounted on the storage device 703 and writes the read program and data to the storage unit 702.
　ここで、プログラムは、例えば、後述のフローチャートの動作をコンピュータ装置７００に実行させる。そして、ＣＰＵ７０１は、その読み込んだプログラムに従って、またその読み込んだデータに基づいて、図１に示す目標入力部１１０、実績入力部１２０、または、要因特定部１５０として各種の処理を実行する。 Here, for example, the program causes the computer device 700 to execute an operation of a flowchart described later. The CPU 701 executes various processes as the target input unit 110, the result input unit 120, or the factor specifying unit 150 shown in FIG. 1 according to the read program and based on the read data.
　尚、ＣＰＵ７０１は、通信網（不図示）に接続される外部の装置（不図示）から、記憶部７０２にそのプログラムやそのデータをダウンロードしてもよい。 Note that the CPU 701 may download the program and its data to the storage unit 702 from an external device (not shown) connected to a communication network (not shown).
　記憶部７０２は、そのプログラムやそのデータを記憶する。記憶部７０２は、例えば計画情報２００、および、因果関係情報３００を記憶しても良い。記憶部７０２は、目標入力部１１０、実績入力部１２０、計画情報保管部１３０、因果関係保管部１４０、要因特定部１５０の一部に含まれても良い。 The storage unit 702 stores the program and data. The storage unit 702 may store plan information 200 and causal relationship information 300, for example. The storage unit 702 may be included in some of the target input unit 110, the result input unit 120, the plan information storage unit 130, the causal relationship storage unit 140, and the factor identification unit 150.
　記憶装置７０３は、例えば、光ディスクや、フレキシブルディスク、磁気光ディスク、外付けハードディスク装置、半導体メモリ装置などであって、記録媒体７０７を含む。記憶装置７０３、または、記録媒体７０７は、そのプログラムをコンピュータ読み取り可能に記憶する。記憶装置７０３は、例えば、計画情報２００、および、因果関係情報３００を記憶して良い。記憶装置７０３は、目標入力部１１０、実績入力部１２０、計画情報保管部１３０、因果関係保管部１４０、要因特定部１５０の一部として含まれても良い。 The storage device 703 is, for example, an optical disk, a flexible disk, a magnetic optical disk, an external hard disk device, a semiconductor memory device, and the like, and includes a recording medium 707. The storage device 703 or the recording medium 707 stores the program in a computer-readable manner. The storage device 703 may store the plan information 200 and the causal relationship information 300, for example. The storage device 703 may be included as part of the target input unit 110, the result input unit 120, the plan information storage unit 130, the causal relationship storage unit 140, and the factor identification unit 150.
　入力部７０４は、オペレータによる操作の入力や外部からの情報の入力を受け付ける。入力操作に用いられるデバイスは、例えば、マウスや、キーボード、内蔵のキーボタン及びタッチパネルなどである。入力部７０４は、目標入力部１１０、実績入力部１２０、計画情報保管部１３０、因果関係保管部１４０、要因特定部１５０の一部として含まれても良い。 The input unit 704 receives an operation input by an operator and an input of information from the outside. Devices used for the input operation are, for example, a mouse, a keyboard, a built-in key button, and a touch panel. The input unit 704 may be included as part of the target input unit 110, the result input unit 120, the plan information storage unit 130, the causal relationship storage unit 140, and the factor identification unit 150.
　出力部７０５は、例えばディスプレイ装置で実現される。出力部７０５は、例えばＧＵＩ（Graphical User Interface）によるオペレータへの入力要求や、オペレータに対する出力提示などのために用いられる。出力部７０５は、目標入力部１１０、実績入力部１２０、計画情報保管部１３０、因果関係保管部１４０、要因特定部１５０の一部として含まれても良い。 The output unit 705 is realized by a display device, for example. The output unit 705 is used, for example, for an input request to the operator by GUI (Graphical User Interface), output presentation to the operator, and the like. The output unit 705 may be included as a part of the target input unit 110, the result input unit 120, the plan information storage unit 130, the causal relationship storage unit 140, and the factor identification unit 150.
　通信部７０６は、外部システムとのインタフェースを実現する。通信部７０６は、目標入力部１１０、実績入力部１２０、計画情報保管部１３０、因果関係保管部１４０、要因特定部１５０の一部として含まれても良い。 The communication unit 706 implements an interface with an external system. The communication unit 706 may be included as a part of the target input unit 110, the result input unit 120, the plan information storage unit 130, the causal relationship storage unit 140, and the factor identification unit 150.
　以上説明したように、図１に示す知識管理装置１０１の各要素は、図４に示すハードウェア構成のコンピュータ装置７００によって実現されても良い。但し、コンピュータ装置７００が備える各部の実現手段は、上記に限定されない。すなわち、コンピュータ装置７００は、物理的に結合した１つの装置により実現されてもよいし、物理的に分離した２つ以上の装置を有線または無線で接続し、これら複数の装置により実現されてもよい。 As described above, each element of the knowledge management apparatus 101 shown in FIG. 1 may be realized by the computer apparatus 700 having a hardware configuration shown in FIG. However, the means for realizing each unit included in the computer apparatus 700 is not limited to the above. That is, the computer device 700 may be realized by one physically coupled device, or may be realized by a plurality of these devices by connecting two or more physically separated devices in a wired or wireless manner. Good.
　尚、上述のプログラムのコードを記録した記録媒体７０７が、コンピュータ装置７００に供給される場合、ＣＰＵ７０１は、記録媒体７０７に格納されたそのプログラムのコードを読み出して実行してもよい。あるいは、ＣＰＵ７０１は、記録媒体７０７に格納されたそのプログラムのコードを、記憶部７０２、記憶装置７０３またはその両方に格納してもよい。 When the recording medium 707 in which the program code is recorded is supplied to the computer device 700, the CPU 701 may read and execute the program code stored in the recording medium 707. Alternatively, the CPU 701 may store the code of the program stored in the recording medium 707 in the storage unit 702, the storage device 703, or both.
　すなわち、本実施形態は、コンピュータ装置７００のＣＰＵ７０１が実行するそのプログラム（ソフトウェア）を、一時的にまたは非一時的に、記憶する記録媒体７０７の実施形態を含む。尚、情報を非一時的に記憶する記憶媒体は、不揮発性記憶媒体とも呼ばれる。
 [動作の説明]
　次に、本実施の形態の知識管理装置１０１の動作について図を参照して詳細に説明する。図５は、要因特定部１５０の動作を示す図である。 That is, the present embodiment includes an embodiment of a recording medium 707 that stores the program (software) executed by the CPU 701 of the computer apparatus 700 temporarily or non-temporarily. A storage medium that stores information non-temporarily is also referred to as a non-volatile storage medium.
[Description of operation]
Next, the operation of the knowledge management apparatus 101 of this embodiment will be described in detail with reference to the drawings. FIG. 5 is a diagram illustrating the operation of the factor specifying unit 150.
　要因特定部１５０は、まず、目標および実績値の２つの計画情報２００の入力に対し差分計算（ステップＳ１）を実施し、差分グラフを作成する。 The factor identifying unit 150 first performs difference calculation (step S1) on the input of the two plan information 200 of the target and the actual value, and creates a difference graph.
　計画情報２００の目標、および、実績は、ともに図２に示したグラフ構造を持つ。計画情報２００の目標、および、実績は、少なくとも＜情報ノード２０１、述語、目的語＞の組のデータを要素とする列挙データ集合である。このデータは、最低限、リンク情報と属性値情報に分類される。前者は、＜情報ノード２０１、リンク関係を示す述語、情報ノード２０１を示す目的語＞、後者は、＜情報ノード２０１、属性値であることを示す述語、ノード値２０４を示す目的語＞の構造をもつ。ただし、順番は入れ換わってもよい。たとえば、目標データの一例として、後述の＜具体的なデータ例を用いた説明＞で使用する図１０に示す構造がある。図１０の場合、リンク情報は、たとえば、＜Ｎ１、ｈａｓＬｉｎｋ、Ｎ２＞で、属性値情報は＜Ｎ３、ｈａｓＶａｌｕｅ、６００＞である。 * Both the goals and results of the plan information 200 have the graph structure shown in FIG. The target and the actual result of the plan information 200 are an enumerated data set including at least data of a set of <information node 201, predicate, object> as elements. This data is at least classified into link information and attribute value information. The former is a structure of <information node 201, a predicate indicating a link relation, an object indicating information node 201>, and the latter is a structure of <information node 201, a predicate indicating attribute value, an object indicating node value 204>. It has. However, the order may be changed. For example, as an example of the target data, there is a structure shown in FIG. 10 used in <Description using specific data example> described later. In the case of FIG. 10, the link information is, for example, <N1, hasLink, N2>, and the attribute value information is <N3, hasValue, 600>.
　要因特定部１５０は、この構造を基に、目標と実績の情報ノード２０１の対応関係を推定し、対応するノードが持つ属性値の差分を計算する。実績データに＜ｎ、目的語が属性値であることを示す述語、ｂ１＞が、目標データに＜ｎ、目的語が属性値であることを示す述語、ｂ２＞が、それぞれ含まれていたとする。ここで、「ｎ」は、情報ノード２０１であり、ｂ１、ｂ２は、ノード値（属性値）であるものとする。このとき、要因特定部１５０は、ｂ１－ｂ２を計算し、差分情報＜ｎ、目的語が属性値であることを示す述語、ｂ１―ｂ２＞を差分グラフに含める。 The factor identification unit 150 estimates the correspondence between the target and actual information nodes 201 based on this structure, and calculates the difference between the attribute values of the corresponding nodes. It is assumed that the result data includes <n, a predicate indicating that the object is an attribute value, b1>, and the target data includes <n, a predicate indicating that the object is an attribute value, and b2>. . Here, “n” is the information node 201, and b1 and b2 are node values (attribute values). At this time, the factor specifying unit 150 calculates b1-b2, and includes difference information <n, a predicate indicating that the object is an attribute value, and b1-b2> in the difference graph.
　要因特定部１５０は、情報ノード２０１の対応関係の推定方法として、例えば、最尤推定やベイズ推定などを用いることが可能である。なお、計画情報２００の目標、および、実績が同一構造のグラフである場合、要因特定部１５０は、情報ノード２０１の対応関係の推定をする必要は無い。この場合、差分グラフは、計画情報２００と同一構造、または、そのサブセット構造を持つ。 The factor identifying unit 150 can use, for example, maximum likelihood estimation or Bayesian estimation as an estimation method of the correspondence relationship of the information node 201. In addition, when the target of plan information 200 and a track record are the graphs of the same structure, the factor specific | specification part 150 does not need to estimate the correspondence of the information node 201. FIG. In this case, the difference graph has the same structure as the plan information 200 or a subset structure thereof.
　次に、要因特定部１５０は、計算された差分グラフの各ノード（以降、差分ノードと呼ぶ）の値に対して丸め処理（ステップＳ２）を実施し、丸め差分グラフを求める。丸め処理は、与えられた誤差範囲内の差分を丸めることによって、戦略上本質的でない差異を除去する。ただし、要因特定部１５０は、本処理を実施しなくても良い。 Next, the factor specifying unit 150 performs a rounding process (Step S2) on the value of each node (hereinafter referred to as a difference node) of the calculated difference graph to obtain a rounded difference graph. The rounding process removes non-strategic differences by rounding differences within a given error range. However, the factor identifying unit 150 may not perform this process.
　そして、主要差分特定処理（ステップＳ３）で、要因特定部１５０は、丸め差分グラフあるいは差分グラフから、大きな差をもつ差分ノードの集合、すなわち主要差分ノードの集合を抽出する。要因特定部１５０は、この主要差分特定処理で、差分を絶対評価してもよいし、目標値あるいは実績値に対する相対評価を行なっても良い。また、要因特定部１５０は、差分の大きい順に、指定した個数の差分ノードを抽出してもよい。 
　ステップＳ１の後、因果関係構造展開処理（ステップＳ４）で、要因特定部１５０は、因果関係保管部１４０に保管された因果関係情報３００が最小化されていた場合、因果関係を展開する。因果関係情報３００が包含する各要素の構造は、＜因果関係識別子、要因、正または負の影響を示す属性、差分グラフのノード＞である。具体的には、要因特定部１５０は、因果関係ノード３０１のデータ＜識別子x、 要因a、 +、 ノードｂ＞に対して、差分グラフにリンク情報＜ノードｃ、属性ｄ、ノードｂ＞があるとき、因果関係ノード３０１のデータ＜識別子ｘ、要因ａ、＋、ノードｃ＞を生成する。ここで、ノードｃは、ノードｂの上位ノードである。 In the main difference specifying process (step S3), the factor specifying unit 150 extracts a set of difference nodes having a large difference, that is, a set of main difference nodes, from the rounded difference graph or the difference graph. In the main difference specifying process, the factor specifying unit 150 may perform absolute evaluation of the difference, or may perform relative evaluation with respect to the target value or the actual value. In addition, the factor specifying unit 150 may extract a specified number of difference nodes in descending order of difference.
After step S1, in the causal relationship structure expansion process (step S4), the factor specifying unit 150 expands the causal relationship when the causal relationship information 300 stored in the causal relationship storage unit 140 is minimized. The structure of each element included in the causal relationship information 300 is <causal relationship identifier, factor, attribute indicating positive or negative influence, node of difference graph>. Specifically, the factor specifying unit 150 has link information <node c, attribute d, node b> in the difference graph for the data <identifier x, factor a, +, node b> of the causal relationship node 301. At this time, data <identifier x, factor a, +, node c> of the causality node 301 is generated. Here, the node c is an upper node of the node b.
　すなわち、要因特定部１５０は、差分グラフのノードｂに要因ａが関連付けられていれば、ノードｂの上位ノードｃにも要因ａを関連付ける。上位ノードｃは、下位ノードｂに依存するため、下位ノードｂと同じ要因ａの影響を受けるのである。要因特定部１５０は、本処理を行うことで、このことを因果関係情報３００に反映する。 That is, if the factor a is associated with the node b of the difference graph, the factor identifying unit 150 associates the factor a with the upper node c of the node b. Since the upper node c depends on the lower node b, it is affected by the same factor a as the lower node b. The factor identifying unit 150 reflects this in the causal relationship information 300 by performing this process.
　因果関係情報３００が最小化されていない場合には、要因特定部１５０は、この因果関係構造展開処理（ステップＳ４）をスキップすることができる。 If the causal relationship information 300 is not minimized, the factor specifying unit 150 can skip this causal relationship structure development process (step S4).
　最後に、主要因果関係特定処理（ステップＳ５）では、要因特定部１５０は、展開された因果関係集合のうち、主要差分ノードに影響を与える要因を抽出する。 Finally, in the main cause-and-effect relationship specifying process (step S5), the factor specifying unit 150 extracts factors that affect the main difference node from the expanded causal relationship set.
　例えば、主要差分情報９４０が、＜識別子ｉ、目的語が名前であることを示す述語、情報ノード名＞および＜識別子ｉ、目的語が値であることを示す述語、差分値＞の両者、あるいは、何れか一方を含むとする。このとき、因果関係情報３００が、因果関係ノード３０１として＜識別子ｘ、要因ａ、＋、ノードｉ＞を包含していれば、要因特定部１５０は、要因ａを抽出する。 For example, the main difference information 940 includes <identifier i, a predicate indicating that the object is a name, information node name> and <identifier i, a predicate indicating that the object is a value, a difference value>, or Or any one of them. At this time, if the causal relationship information 300 includes <identifier x, factor a, +, node i> as the causal relationship node 301, the factor specifying unit 150 extracts the factor a.
　因果関係情報３００が、因果関係ノード３０１として＜識別子ｙ、要因ｂ、＋、ノードｉ＞を、＜識別子x、 要因a、 +、 ノードｂ＞と同時に包含するとき、要因特定部１５０は、要因ａの代わりに要因ｂを出力してもよい。
 [効果の説明] 
　知識管理装置１０１は、研究開発、事業等の遂行において、目標が達成できない場合、ユーザがその要因を分析することを容易にする。 When the causal relationship information 300 includes <identifier y, factor b, +, node i> as the causal relationship node 301 together with <identifier x, factor a, +, node b>, the factor identifying unit 150 The factor b may be output instead of a.
[Description of effects]
The knowledge management apparatus 101 makes it easy for the user to analyze the cause when the goal cannot be achieved in the performance of research and development, business, or the like.
　その第１の理由は、因果関係保管部１４０が、因果関係情報３００を格納するからである。因果関係情報３００は、計画情報２００に含まれるノードのノード値２０４とそこに影響する要因を関連づけている。 The first reason is that the causal relationship storage unit 140 stores the causal relationship information 300. The causal relationship information 300 associates the node value 204 of the node included in the plan information 200 with the factor that affects the node value 204.
　その第２の理由は、要因特定部１５０が、計画情報２００において、目標と実績の差分が発生しているノード値２０４に影響を与える要因を、因果関係情報３００を参照して特定するからである。 The second reason is that the factor identifying unit 150 identifies a factor that affects the node value 204 in which the difference between the target and the actual result occurs in the plan information 200 with reference to the causal relationship information 300. is there.
　＜第２の実施の形態＞
[構成の説明]
　次に、本発明の第２の実施形態について図面を参照して詳細に説明する。以下、本実施形態の説明が不明確にならない範囲で、前述の説明と重複する内容については説明を省略する。 <Second Embodiment>
[Description of configuration]
Next, a second embodiment of the present invention will be described in detail with reference to the drawings. Hereinafter, the description overlapping with the above description is omitted as long as the description of the present embodiment is not obscured.
　図６は、本発明の第２の実施形態に係る知識管理装置５００の構成を示すブロック図である。本実施の形態の知識管理装置５００は、前述の第１の実施形態の知識管理装置１０１の構成に加え、対策保管部５１０、および、対策導出部５２０を含む。 FIG. 6 is a block diagram showing the configuration of the knowledge management device 500 according to the second embodiment of the present invention. The knowledge management device 500 according to the present embodiment includes a countermeasure storage unit 510 and a countermeasure derivation unit 520 in addition to the configuration of the knowledge management device 101 according to the first embodiment.
　第１の実施形態の知識管理装置１０１と同様の構成部分は、第１の実施形態の知識管理装置１０１と同様に機能する。すなわち当該部分に含まれる、例えば、要因特定部１５０は、計画情報２００の目標と実績の差分を発生させる要因の集合を抽出して出力する。 Components similar to those of the knowledge management apparatus 101 of the first embodiment function in the same manner as the knowledge management apparatus 101 of the first embodiment. That is, for example, the factor specifying unit 150 included in the part extracts and outputs a set of factors that generate a difference between the target and the actual result of the plan information 200.
　対策保管部５１０は、要因に対しとるべき対策の情報（以下、対策情報６００と呼称し、詳細は後述する）を保存する。対策導出部５２０は、要因特定部１５０が出力した要因の集合と対策情報６００を入力とし、要因特定部１５０が出力した要因の集合に対して有効な対策を抽出して出力する。 The countermeasure storage unit 510 stores information on countermeasures to be taken against the factors (hereinafter referred to as countermeasure information 600, details will be described later). The measure deriving unit 520 receives the set of factors output from the factor specifying unit 150 and the measure information 600, extracts effective measures for the set of factors output by the factor specifying unit 150, and outputs them.
　対策導出部５２０は、論理回路で構成される。対策保管部５１０は、半導体記憶装置、ディスク記憶装置等の記憶装置で構成される。対策導出部５２０は、コンピュータ装置７００の記憶部７０２に格納されて、ＣＰＵ７０１により実行されるプログラムで実現されても良い。 The countermeasure derivation unit 520 is composed of a logic circuit. The countermeasure storage unit 510 includes a storage device such as a semiconductor storage device or a disk storage device. The countermeasure derivation unit 520 may be realized by a program stored in the storage unit 702 of the computer device 700 and executed by the CPU 701.
　図７は、対策保管部５１０に保存される対策情報６００の例を示す。対策情報６００は、要因を表す要因ノード６１０と対策を表す対策ノード６２０を、要因・対策関係情報６１１で多対多に関連付けた構造を持った情報である。対策情報６００は、階層的な対策記述を包含していても良い。この場合、上位層の包括的な対策Ａと下位層の具体的な対策Ｂの関係は、対策Ａの対策ノード６２０と対策Ｂの対策ノード６２０間の対策間リンク６３０で表される。 FIG. 7 shows an example of the countermeasure information 600 stored in the countermeasure storage unit 510. The countermeasure information 600 is information having a structure in which a factor node 610 representing a factor and a countermeasure node 620 representing a countermeasure are associated in many-to-many relationship with the factor / countermeasure relation information 611. The countermeasure information 600 may include a hierarchical countermeasure description. In this case, the relationship between the comprehensive countermeasure A of the upper layer and the specific countermeasure B of the lower layer is represented by an inter-measure link 630 between the countermeasure node 620 of the countermeasure A and the countermeasure node 620 of the countermeasure B.
　対策情報６００は、より形式的には、例えば以下の、式（１）乃至式（４）のように定義される。この構造あるいは性質は、拡張されて使用されても良い。
The measure information 600 is more formally defined as, for example, the following expressions (1) to (4). This structure or property may be extended and used.

Figure JPOXMLDOC01-appb-I000001
Figure JPOXMLDOC01-appb-I000001
　ここで、Ｖｒは要因の集合、Ｖｃは対策の集合である。また、Ｅｒｃｘｙは対策ｘが要因ｙを直接被覆する関係を示す２項関係、Ｅｃｘｙは対策ｘが対策ｙを直接被覆する関係を示す２項関係である。Ｅｃｉｘｙは対策ｘが対策ｙを被覆する関係を示す２項関係、Ｅｒｃｉｘｙは、対策ｘが要因ｙを被覆する関係を示す２項関係である。また、式（３）及び式（４）におけるｚは、各々、カンマ以降に記載された条件を満足するＶｃの要素を意味する。
 [動作の説明] 
　各部はそれぞれ概略つぎのように動作する。 Here, V r is a set of factors, and V c is a set of countermeasures. E rc xy is a binary relationship indicating that the countermeasure x directly covers the factor y, and E c xy is a binary relationship indicating that the countermeasure x directly covers the countermeasure y. E ci xy is a binary relationship indicating that the countermeasure x covers the countermeasure y, and E rci xy is a binary relationship indicating the relationship where the countermeasure x covers the factor y. Moreover, z in Formula (3) and Formula (4) means the element of Vc which satisfies the conditions described after the comma, respectively.
[Description of operation]
Each part generally operates as follows.
　対策導出部５２０は、対策保管部５１０に保管された対策情報６００を用いて、要因特定部１５０の出力である要因の集合に含まれる要素を被覆する対策の集合を導出する。この導出は、上述の定義において、全ての要因∀y∈Vrについて、Ｅｒｃｉ x y を充足するx を求めることを意味する。 The measure deriving unit 520 uses the measure information 600 stored in the measure storing unit 510 to derive a set of measures covering the elements included in the set of factors that is the output of the factor specifying unit 150. This derivation means obtaining x satisfying E rci x y for all factors ∀y∈V r in the above definition.
　この導出は、集合被覆問題として知られている。対策導出部５２０は、例えば、転置インデックスあるいは複数パスによる貪欲法を用いてこの導出を実行する。また、対策導出部５２０は、メモリ効率やディスク効率を改善した貪欲法などを用いてもよい。 This derivation is known as the collective covering problem. The countermeasure derivation unit 520 performs this derivation using, for example, a transposed index or a greedy method with multiple paths. The countermeasure deriving unit 520 may use a greedy method that improves memory efficiency and disk efficiency.
　対策導出部５２０は、典型的な転置インデックスを用いた貪欲法を使用したとき以下のように動作する。 The countermeasure derivation unit 520 operates as follows when the greedy method using a typical transposed index is used.
　対策導出部５２０は、解の候補となっている対策の集合に新たな対策を加え、被覆する要因の集合を計算する。ここで、加えるべき対策は、要因集合に新しく追加される要因に対応する対策である必要がある。 The countermeasure derivation unit 520 adds a new countermeasure to the set of countermeasures that are solution candidates, and calculates a set of covering factors. Here, the countermeasure to be added needs to be a countermeasure corresponding to the factor newly added to the factor set.
　そのためには、対策導出部５２０は、まず各要因がカバーされる対策のリスト（転置インデックス）を作成する。このリストを用いて、対策導出部５２０は、新しく要因集合に追加される要因の数が最大となるような対策を、候補解の集合に加えていく。 For this purpose, the measure deriving unit 520 first creates a list of measures (transposed index) that covers each factor. Using this list, the countermeasure derivation unit 520 adds a countermeasure that maximizes the number of factors newly added to the factor set to the set of candidate solutions.
　対策導出部５２０は、最初に、各対策ｘ∈Ｖｃについて、Ｅｒｃｉ ｘ ｙを充足する要因ｙの集合Ｙｘを探索する。入力された要因の集合の要素を全て包含するＹｘが発見されれば、対策導出部５２０は、ｘを対策の集合として出力する。 First, the countermeasure derivation unit 520 searches for a set Y x of factors y satisfying E rci x y for each countermeasure xεV c . When Y x including all elements of the input factor set is found, the countermeasure derivation unit 520 outputs x as a countermeasure set.
　発見されなければ、対策導出部５２０は、対策ｘについての集合Ｙｘと対策ｘと異なる対策ｘ’についての集合Ｙｘ’の組合せによる和集合を計算する。このとき、対策導出部５２０は、和集合Ｙｘ∪Ｙｘ’が最大となるように、すなわち、Ｙｘ’によって新たにカバーされる要因の数が最大となるように、x’を選択する。Ｙｘ∪Ｙｘ’がもし、入力された要因の集合を全て包含すれば、対策導出部５２０は、対策の集合{ｘ、ｘ’}を出力する。 If not found, the countermeasure derivation unit 520 calculates a union by combining the set Y x for the countermeasure x and the set Y x ′ for the countermeasure x ′ different from the countermeasure x. At this time, the countermeasure derivation unit 520 selects x ′ so that the union Y x ∪Y x ′ is maximized, that is, the number of factors newly covered by Y x ′ is maximized. . If Y x ∪Y x ′ includes all the input factor sets, the countermeasure deriving unit 520 outputs a countermeasure set {x, x ′}.
　Ｙｘ∪Ｙｘ’が要因の集合の要素を全て包含していなければ、対策導出部５２０は、Ｙｘ∪Ｙｘ’∪Ｙｘ”が最大となる対策ｘ”について上記と同じ判定を行なう。以降、対策導出部５２０は、上記のように対策が被覆する要因の集合を加えて、入力された要因の集合を全て包含するかの判定を繰り返す。最終的に、対策導出部５２０は、要因を全て被覆する対策の集合を出力する。
[効果の説明] 
　本実施の形態の知識管理装置５００は、研究開発、事業等の遂行において、目標が達成できない場合、ユーザがその要因にたいして対策をとることを容易にする。その理由は、対策導出部５２０が、対策情報６００を参照して、目標が達成できないその要因に対する対策を提示するからである。 If Y x ∪Y x ′ does not include all the elements of the factor set, the measure derivation unit 520 makes the same determination as above for the measure x ″ that maximizes Y x ∪Y x ′ ∪Y x ″. . Thereafter, the countermeasure deriving unit 520 adds the set of factors covered by the countermeasure as described above, and repeats the determination as to whether or not all the input factor sets are included. Finally, the countermeasure derivation unit 520 outputs a set of countermeasures covering all the factors.
[Description of effects]
The knowledge management device 500 according to the present embodiment makes it easy for the user to take measures against the cause when the goal cannot be achieved in the performance of research and development and business. The reason is that the countermeasure derivation unit 520 refers to the countermeasure information 600 and presents a countermeasure for the factor that the target cannot be achieved.
　さらに、本実施の形態の知識管理装置５００は、対策の導出に当たり、ディスク入出力に要する時間を短縮することができる。その理由は、対策導出部５２０が、対策の導出に際して転置インデックスを使用する為、１度読み出した対策情報６００を再度読み出す必要がないからである。 Furthermore, the knowledge management apparatus 500 of the present embodiment can reduce the time required for disk input / output in deriving the countermeasure. The reason is that since the countermeasure derivation unit 520 uses an inverted index when deriving the countermeasure, it is not necessary to read the countermeasure information 600 read once.
　＜第３の実施の形態＞
 [構成の説明] 
　次に、本発明の第３の実施の形態について図面を参照して詳細に説明する。 <Third Embodiment>
[Description of configuration]
Next, a third embodiment of the present invention will be described in detail with reference to the drawings.
　図８は、本発明の第３の実施形態に係る知識管理装置５５０の構成を示すブロック図である。本実施の形態の知識管理装置５５０は、前述の第１の実施形態の知識管理装置１０１の構成に加え、対策保管部５６０、および、対策導出部５７０を含む。 FIG. 8 is a block diagram showing the configuration of the knowledge management device 550 according to the third embodiment of the present invention. The knowledge management device 550 of the present embodiment includes a countermeasure storage unit 560 and a countermeasure derivation unit 570 in addition to the configuration of the knowledge management apparatus 101 of the first embodiment described above.
　第１の実施形態の知識管理装置１０１と同様の構成部分は、第１の実施形態の知識管理装置１０１と同様に機能する。すなわち当該部分に含まれる、例えば、要因特定部１５０は、計画情報２００の目標と実績の差分を発生させる要因の集合を抽出して出力する。 Components similar to those of the knowledge management apparatus 101 of the first embodiment function in the same manner as the knowledge management apparatus 101 of the first embodiment. That is, for example, the factor specifying unit 150 included in the part extracts and outputs a set of factors that generate a difference between the target and the actual result of the plan information 200.
　対策保管部５６０は、第２の実施形態と同様に対策情報６００を保存する。ただし、本実施の形態の対策情報６００は、重み付きデータに拡張される。すなわち、対策情報６００の対策ノード６２０には重みが付与されている。 The countermeasure storage unit 560 stores the countermeasure information 600 as in the second embodiment. However, the countermeasure information 600 of the present embodiment is extended to weighted data. That is, the countermeasure node 620 of the countermeasure information 600 is given a weight.
　対策導出部５７０は、第２の実施形態と同様に要因特定部１５０が出力した要因の集合と対策情報６００を入力とし、要因特定部１５０が出力した要因の集合に対して有効な対策を抽出して出力する。ただし、対策導出部５７０は、重み付きデータを処理するように拡張される。対策導出部５７０は、例えば、対策の探索において、重みの大きな対策の順に探索する。 The countermeasure deriving unit 570 receives the set of factors output from the factor identifying unit 150 and the countermeasure information 600 as in the second embodiment, and extracts effective measures for the set of factors output by the factor identifying unit 150. And output. However, the countermeasure deriving unit 570 is extended to process weighted data. For example, the countermeasure derivation unit 570 searches for countermeasures in descending order of countermeasures.
　対策導出部５７０は、論理回路で構成される。対策保管部５６０は、半導体記憶装置、ディスク記憶装置等の記憶装置で構成される。対策導出部５７０は、コンピュータ装置７００の記憶部７０２に格納されて、ＣＰＵ７０１により実行されるプログラムで実現されても良い。
 [動作の説明] 
　本実施の形態において、複数パス法を用いた対策導出部５７０は以下のように動作する。 The countermeasure derivation unit 570 is configured by a logic circuit. The countermeasure storage unit 560 includes a storage device such as a semiconductor storage device or a disk storage device. The countermeasure derivation unit 570 may be realized by a program stored in the storage unit 702 of the computer device 700 and executed by the CPU 701.
[Description of operation]
In the present embodiment, the countermeasure derivation unit 570 using the multi-pass method operates as follows.
　要因特定部１５０から入力された要因の集合をＹとする。初期状態において、対策導出部５７０は、各対策ｘ∈Ｖｃについて、Ｅｒｃｉ ｘ ｙを充足する要因ｙの集合Ｙｘのうち、集合Ｙとの共通部分Ｙ∩Ｙｘが最大となるｘを探索する。Ｙ⊆Ｙｘ、すなわちＹ∩Ｙｘ＝Ｙであるとき、対策導出部５７０は、ｘを解として出力する。 A set of factors input from the factor specifying unit 150 is Y. In the initial state, the countermeasure derivation unit 570 determines, for each countermeasure xεV c , the x that maximizes the common part Y∩Y x with the set Y among the set Y x of the factors y satisfying E rci x y. Explore. When Y⊆Y x , that is, Y∩Y x = Y, the countermeasure derivation unit 570 outputs x as a solution.
　それ以外のとき、対策導出部５７０は、対策ｘと異なる各対策ｘ’∈Ｖｃについて、Ｅｒｃｉ ｘ’ ｙを充足する要因ｙの集合Ｙｘ’を求め、共通部分Ｙ∩Ｙｘ∩Ｙｘ’が最大となるようなｘ’を探索する。Ｙ⊆Ｙｘ∪Ｙｘ’であるとき、対策導出部５７０は、｛ｘ、ｘ’｝を解として出力する。 Otherwise, the countermeasure derivation unit 570 obtains a set Y x ′ of factors y satisfying E rci x ′ y for each countermeasure x′∈V c different from the countermeasure x, and obtains a common part Y∩Y x ∩Y. Search for x ′ that maximizes x ′ . When Y⊆Y x ∪Y x ′ , the countermeasure derivation unit 570 outputs {x, x ′} as a solution.
　それ以外のとき、対策導出部５７０は、以上の手順を繰り返し適用し、順次対策を追加し、集合Ｙを全て被覆する対策の集合を求める。
[効果の説明] 
　本実施の形態の知識管理装置５５０は、研究開発、事業等の遂行において、目標が達成できない場合、ユーザがその要因に対して対策をとることを容易にする。その理由は、対策導出部５７０が、対策情報６００を参照して、目標が達成できないその要因に対する対策を提示するからである。 In other cases, the countermeasure derivation unit 570 repeatedly applies the above procedure, sequentially adds countermeasures, and obtains a set of countermeasures covering all the set Y.
[Description of effects]
The knowledge management device 550 of the present embodiment makes it easy for the user to take measures against the cause when the goal cannot be achieved in the performance of research and development and business. The reason is that the countermeasure derivation unit 570 refers to the countermeasure information 600 and presents a countermeasure for the factor that the target cannot be achieved.
　さらに、本実施の形態の知識管理装置５５０は、ユーザが適切な、例えば効果的な、対策を優先して実施することを容易にする。その理由は、対策導出部５７０が、対策に付与された重みを考慮して、対策を検索するからである。 Furthermore, the knowledge management device 550 according to the present embodiment makes it easy for the user to prioritize appropriate, for example, effective measures. The reason is that the countermeasure derivation unit 570 searches for the countermeasure in consideration of the weight assigned to the countermeasure.
　さらに、本実施の形態の知識管理装置５５０は、対策の導出に当たり、使用するメモリ容量を小さくすることができる。その理由は、対策導出部５７０が、複数パス法を用いるため、転置インデックスを作成しないからである。 Furthermore, the knowledge management device 550 of the present embodiment can reduce the memory capacity used in deriving the countermeasure. The reason is that the countermeasure deriving unit 570 does not create an inverted index because it uses the multi-pass method.
　さらに、本実施の形態の知識管理装置５５０は、対策の導出に当たり、物理ディスクの入出力効率を向上させる。その理由は、対策導出部５７０が、複数パス法を用いるため、物理ディスクに対する走査にはランダムアクセスを使用しないからである。 Furthermore, the knowledge management device 550 of this embodiment improves the input / output efficiency of the physical disk in deriving the countermeasure. The reason is that the countermeasure deriving unit 570 uses the multi-pass method and therefore does not use random access for scanning the physical disk.
　＜第４の実施の形態＞
図１８は、第４の実施形態に係る知識管理装置８０１の構成を示すブロック図である。知識管理装置８０１は、計画情報保管部８３０、因果関係保管部８４０、および、要因特定部８５０を含む。 <Fourth embodiment>
FIG. 18 is a block diagram illustrating a configuration of a knowledge management apparatus 801 according to the fourth embodiment. The knowledge management device 801 includes a plan information storage unit 830, a causal relationship storage unit 840, and a factor identification unit 850.
　計画情報保管部８３０は、ノードに関連付けて当該ノードの値であるノード値の目標を記憶する。因果関係保管部８４０は、ノードに関連付けて当該ノードのノード値を変化させる要因を記憶する。要因特定部８５０は、ノードのノード値の実績を取得して、実績が当該ノードに関連付けられた目標を達成していないと、当該ノードに関連付けられた要因を出力する。 The plan information storage unit 830 stores a node value target that is a value of the node in association with the node. The causal relationship storage unit 840 stores a factor that changes the node value of the node in association with the node. The factor specifying unit 850 acquires the node value result of the node, and outputs the factor associated with the node when the result does not achieve the target associated with the node.
　知識管理装置８０１は、研究開発、事業等の遂行において、目標が達成できない場合、ユーザがその要因を分析することを容易にする。 The knowledge management device 801 makes it easy for the user to analyze the cause when the goal cannot be achieved in the performance of research and development and business.
　その理由は、要因特定部８５０が、計画の目標と実績の差分が発生しているノードに影響を与える要因を、因果関係保管部８４０を参照して特定するからである。 The reason is that the factor identifying unit 850 identifies the factor that affects the node where the difference between the plan target and the actual performance occurs with reference to the causal relationship storage unit 840.
　＜具体的なデータ例を用いた説明＞
　次に、具体的なデータ例を用いて本発明にかかる装置の動作を説明する。以下の説明は、図６に示す知識管理装置５００を例とした説明である。 <Explanation using specific data examples>
Next, the operation of the apparatus according to the present invention will be described using specific data examples. The following description is an example of the knowledge management device 500 shown in FIG.
　図９は、知識管理装置５００に与えられる計画情報２００の具体例を示す。与えられる計画情報２００は、目標を含む計画情報（目標）９１０と実績値を含む計画情報（実績）９２０である。図１０は、計画情報（目標）９１０の部分的なコンピュータ装置７００上の表現例を示す。 FIG. 9 shows a specific example of the plan information 200 given to the knowledge management device 500. The given plan information 200 is plan information (target) 910 including a target and plan information (actual) 920 including an actual value. FIG. 10 shows a partial expression example of the plan information (target) 910 on the computer device 700.
　図９及び図１０によれば、計画情報（目標）９１０は、Ｎ１乃至Ｎ４という情報ノード２０１を包含する。ノードＮ１は、属性値がOR-Nodeであり、目標値がノードＮ２の月間売上またはノードＮ３のオンデマンド売り上げの何れかであることを示している。ノードＮ３は、オンデマンド売り上げが、ノードＮ４が定義するオペランドと演算子で得られること、目標値が６００であることを示している。 9 and 10, the plan information (target) 910 includes information nodes 201 N1 to N4. The node N1 indicates that the attribute value is OR-Node, and the target value is either monthly sales of the node N2 or on-demand sales of the node N3. The node N3 indicates that on-demand sales are obtained by the operand and operator defined by the node N4, and the target value is 600.
　図９は、計画情報（実績）９２０も、計画情報（目標）９１０と同じ構造を持つことを示している。ただし、例えば、ノードＮ３のオンデマンド売り上げのノード値２０４（実績値）は、２４０であることを示している。 FIG. 9 shows that the plan information (actual result) 920 also has the same structure as the plan information (target) 910. However, for example, the on-demand sales node value 204 (actual value) of the node N3 indicates 240.
　図５によれば、これらの入力に対して、要因特定部１５０は、Ｓ１からＳ５の処理を実施する。まず、要因特定部１５０は、差分計算（ステップＳ１）により差分情報９３０を得る。 Referring to FIG. 5, the factor specifying unit 150 performs the processing from S1 to S5 for these inputs. First, the factor identifying unit 150 obtains difference information 930 by difference calculation (step S1).
　図１１は差分情報９３０を示す。図１１から明らかなように、差分情報９３０は、計画情報（目標）９１０および計画情報（実績）９２０と同じ構造を持つ。差分情報９３０のノード値２０４は、計画情報（目標）９１０と計画情報（実績）９２０の対応する情報ノード２０１のノード値２０４の差分となっている。例えば、ノードＮ３のオンデマンド売り上げのノード値２０４は、上述の６００と２４０の差分で－３６０となっている。差分情報９３０の他のノードのノード値２０４についても、同様のことが言える。図１２は、差分情報９３０のコンピュータ装置７００上の部分的な表現例を示す。 FIG. 11 shows the difference information 930. As is clear from FIG. 11, the difference information 930 has the same structure as the plan information (target) 910 and the plan information (actual result) 920. The node value 204 of the difference information 930 is a difference between the node values 204 of the corresponding information nodes 201 of the plan information (target) 910 and the plan information (actual result) 920. For example, the node value 204 of on-demand sales of the node N3 is −360, which is the difference between 600 and 240 described above. The same can be said for the node values 204 of other nodes of the difference information 930. FIG. 12 shows a partial expression example on the computer device 700 of the difference information 930.
　次に、要因特定部１５０は、例えば、整数化の差分丸め処理（ステップＳ２）を実施する。但し、この処理は、この実施例では結果に影響しない。したがって、この処理の出力は、図１１及び図１２に示すままである。 Next, the factor specifying unit 150 performs, for example, an integer difference rounding process (step S2). However, this process does not affect the result in this embodiment. Therefore, the output of this process remains as shown in FIGS.
　その後、要因特定部１５０は、主要差分特定処理（ステップＳ３）を実施する。要因特定部１５０は、本ステップで、例えば、差分の目標値に対する比率が大きなものを３つ選択する。 Thereafter, the factor identifying unit 150 performs a main difference identifying process (step S3). In this step, the factor specifying unit 150 selects, for example, three that have a large ratio of the difference to the target value.
　図１３は、この主要差分特定処理により、差分情報９３０から得られた主要差分情報９４０を示す。差分情報９３０は、ノードＮ３のオンデマンド売上の差分が目標に対して６０％の差を生じたことを示している。同様に、差分情報９３０は、ノードＮ６の利用回数が６０％、ノードＮ９の利用頻度が５０％、ノードＮ８の顧客数が２０％の差を生じたことを示している。 FIG. 13 shows the main difference information 940 obtained from the difference information 930 by this main difference specifying process. The difference information 930 indicates that the difference in on-demand sales at the node N3 is 60% different from the target. Similarly, the difference information 930 indicates that the number of uses of the node N6 is 60%, the use frequency of the node N9 is 50%, and the number of customers of the node N8 is 20%.
　この結果、要因特定部１５０は、ノードＮ３のオンデマンド売上の６０％、ノードＮ６の利用回数の６０％、そしてノードＮ９の利用頻度の５０％を、主要差分情報９４０に含める。図１４は、この主要差分情報９４０のコンピュータ装置７００上での部分的な表現例を示す。この主要差分情報９４０が、主要因果関係特定処理（ステップＳ５）の入力となる。 As a result, the factor specifying unit 150 includes 60% of the on-demand sales of the node N3, 60% of the usage count of the node N6, and 50% of the usage frequency of the node N9 in the main difference information 940. FIG. 14 shows a partial expression example of the main difference information 940 on the computer device 700. This main difference information 940 becomes the input of the main cause-effect relationship specifying process (step S5).
　また、因果関係保管部１４０は、図３の因果関係情報３００を保管していたとする。図１５は、因果関係保管部１４０が格納している因果関係情報３００の形式例を示す。ここで、要因特定部１５０が因果関係構造展開処理（ステップＳ４）を実行すると、下位ノード（例えば、利用頻度）に関連付けられていた要因（例えば、重要度）が、当該下位ノードの上位ノード（例えば、利用回数、オンデマンド売り上げ）にも関連付けられる。これは、図１５の表に新たなレコードが追加されることを意味する。 Further, it is assumed that the causal relationship storage unit 140 stores the causal relationship information 300 of FIG. FIG. 15 shows a format example of the causal relationship information 300 stored in the causal relationship storage unit 140. Here, when the factor identifying unit 150 executes the causal relationship structure development process (step S4), the factor (for example, importance) associated with the lower node (for example, usage frequency) is changed to the upper node ( For example, the number of uses and sales on demand). This means that a new record is added to the table of FIG.
　主要因果関係特定処理（ステップＳ５）には前述のとおり、図１３に示す主要差分情報９４０と、因果関係構造展開処理（ステップＳ４）で出力された因果関係情報３００が入力される。図１４によると、Ｎ３、Ｎ６、Ｎ９が主要差分であり、これらの計画情報名はそれぞれオンデマンド売上、利用回数および利用頻度である。要因特定部１５０は、これらの計画情報名を因果関係情報３００から検索する。 As described above, the main difference information 940 shown in FIG. 13 and the causal relationship information 300 output in the causal relationship structure development process (step S4) are input to the main cause-and-effect relationship specifying process (step S5). According to FIG. 14, N3, N6, and N9 are the main differences, and these plan information names are on-demand sales, usage count, and usage frequency, respectively. The factor specifying unit 150 searches the causal relationship information 300 for these plan information names.
　この結果、要因特定部１５０は、オンデマンド売上に正の影響を与える要因として、利便性、認知度、物価指数および重要度を得る。また、要因特定部１５０は、利用回数に正の影響を与える要因として、利便性と重要度、そして認知度を得る。そして要因特定部１５０は、利用頻度に正の影響を与える要因として利便性と重要度を得る。 As a result, the factor specifying unit 150 obtains convenience, recognition, price index, and importance as factors that have a positive impact on on-demand sales. In addition, the factor identifying unit 150 obtains convenience, importance, and recognition as factors that have a positive effect on the number of uses. The factor identifying unit 150 obtains convenience and importance as factors that have a positive influence on the usage frequency.
　そのため、要因特定部１５０は、要因の集合として｛物価指数、重要度、利便性、認知度｝を出力する。要因特定部１５０は、要因の重要性、選択された回数などを考慮して、この部分集合を出力しても良い。ここでは、要因特定部１５０は、｛利便性、重要度、物価指数｝を出力したとする。 Therefore, the factor specifying unit 150 outputs {price index, importance, convenience, awareness} as a set of factors. The factor specifying unit 150 may output this subset in consideration of the importance of the factor, the number of times of selection, and the like. Here, it is assumed that the factor specifying unit 150 outputs {convenience, importance, price index}.
　対策保管部５１０は、図７に示す対策情報６００を保存しているとする。図１６は、対策情報６００のコンピュータ装置７００上の部分的な表現例を示す。 Suppose that the countermeasure storage unit 510 stores countermeasure information 600 shown in FIG. FIG. 16 shows a partial expression example of the countermeasure information 600 on the computer device 700.
　対策導出部５２０は、対策情報６００を用いて、要因特定部１５０が主要因果関係特定処理（ステップＳ５）で出力した要因の集合を被覆する対策を導出する。 The countermeasure deriving unit 520 uses the countermeasure information 600 to derive a countermeasure covering the set of factors output by the factor identifying unit 150 in the main factor-effect relationship identifying process (step S5).
　具体的に対策導出部５２０は、以下のように計算する。まず、対策導出部５２０は、転置インデックスを計算する。すなわち、対策導出部５２０は、各対策が被覆する全要因（要因特定部１５０から入力された要因に限定してもよい）から、各要因を被覆する全対策を求める。 Specifically, the measure deriving unit 520 calculates as follows. First, the countermeasure derivation unit 520 calculates a transposed index. That is, the measure deriving unit 520 obtains all measures covering each factor from all factors covered by each measure (may be limited to the factors input from the factor specifying unit 150).
　この処理は、図１６に示される表の行データから図１７に示す表の列データを求める計算に等しい。利便性を被覆する対策の集合は、｛プレミアム化戦略、自社他製品との統合、多機能化、他社製品との協調動作、機能強化｝である。重要度を被覆する対策の集合は、｛宣伝を行なう、クロス宣伝、排他戦略、自社他製品との統合｝である。物価指数を被覆する対策の集合は、｛利用料の構造を変化させる、段階的料金設定、廉価版提供、プレミアム化戦略、自社他製品との統合｝である。対策導出部５２０は、これらの各対策が被覆する要因を順次探索して、転置インデックス、すなわち、図１７に示す表の列データを求める。 This process is equivalent to the calculation for obtaining the column data of the table shown in FIG. 17 from the row data of the table shown in FIG. A set of measures covering convenience is {premium strategy, integration with other company's products, multi-functionality, cooperative operation with other company's products, functional enhancement}. A set of measures covering the importance is {advertisement, cross-advertisement, exclusion strategy, integration with other products of the company}. The set of measures covering the price index is {changing the structure of usage fees, step-by-step pricing, provision of low-priced versions, premium strategy, integration with other products}. The countermeasure deriving unit 520 sequentially searches the factors covered by each of these countermeasures, and obtains the transposed index, that is, the column data of the table shown in FIG.
　これらのうち、対策導出部５２０は、被覆が最大となる対策である、自社他製品との統合を選ぶ。この対策、自社他製品との統合は、要因特定部１５０が出力した｛利便性、重要度、物価指数｝の３つの要因全てを被覆する。したがって、対策導出部５２０は、自社他製品との統合、を対策として出力する。 Among these, the countermeasure derivation unit 520 selects integration with other products of the company, which is the countermeasure that maximizes the covering. This countermeasure and integration with other products of the company cover all three factors {convenience, importance, price index} output by the factor specifying unit 150. Therefore, the countermeasure derivation unit 520 outputs integration with other products of the company as a countermeasure.
　なお、要因特定部１５０の出力が、｛認知度、利便性、重要度｝だとする。この場合、対策導出部５２０は、図１７の転置インデックスを計算後、自社他製品との統合を最初に選択する。次に、対策導出部５２０は、まだ被覆されていない要因である認知度を被覆する対策｛宣伝を行なう、クロス宣伝｝のいずれかを選択する。この時点で対策の集合が、入力された全要因を被覆するため、対策導出部５２０は、｛自社他製品との統合、宣伝を行なう｝か、｛自社他製品との統合、クロス宣伝｝のいずれかの対策集合を出力する。 Note that the output of the factor identification unit 150 is {recognition, convenience, importance}. In this case, the countermeasure deriving unit 520 first selects integration with other products of the company after calculating the inverted index of FIG. Next, the countermeasure deriving unit 520 selects one of the countermeasures {advertisement, cross advertisement} covering the degree of recognition that is not yet covered. Since the set of countermeasures covers all the input factors at this point, the countermeasure derivation unit 520 may select {integrate and promote with other products of the company} or {integrate with other products of the company and cross-advertisement}. Output one of the countermeasure sets.
　以上、実施形態を参照して本願発明を説明したが、本願発明は上記実施形態に限定されるものではない。本願発明の構成や詳細には、本願発明のスコープ内で当業者が理解し得る様々な変更をすることができる。なお、図面中の矢印の向きは、一例を示すものであり、ブロック間の信号の向きを限定するものではない。 The present invention has been described above with reference to the embodiments, but the present invention is not limited to the above embodiments. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. In addition, the direction of the arrow in a drawing shows an example and does not limit the direction of the signal between blocks.
　本発明は、計画上の進捗状況を判断し、取るべき対策の意思決定を支援するといった用途に適用できる。また、本発明は、管理している成果物に類似する成果物について、立てるべき目標を提案するといった用途にも適用可能である。 The present invention can be applied to uses such as judging progress in planning and supporting decision making of measures to be taken. The present invention can also be applied to uses such as proposing a goal to be set for a product similar to a managed product.
　この出願は、2014年10月23日に出願された日本出願特願2014-216362を基礎とする優先権を主張し、その開示の全てをここに取り込む。 This application claims priority based on Japanese Patent Application No. 2014-216362 filed on October 23, 2014, the entire disclosure of which is incorporated herein.
　１０１、５００、５５０、８０１　　知識管理装置
　１１０　　目標入力部
　１２０　　実績入力部
　１３０、８３０　　計画情報保管部
　１４０、８４０　　因果関係保管部
　１５０、８５０　　要因特定部
　２００　　計画情報
　２０１　　情報ノード
　２０２　　情報リンク
　２０３　　ノードラベル
　２０４　　ノード値
　３００　　因果関係情報
　３０１　　因果関係ノード
　３０２　　計画情報ノードリンク
　３０３　　要因ノード
　３０４　　要因リンク
　５１０、５６０　　対策保管部
　５２０、５７０　　対策導出部
　６００　　対策情報
　６１０　　要因ノード
　６１１　　要因・対策関係情報
　６２０　　対策ノード
　６３０　　対策間リンク
　７００　　コンピュータ装置
　７０１　　ＣＰＵ
　７０２　　記憶部
　７０３　　記憶装置
　７０４　　入力部
　７０５　　出力部
　７０６　　通信部
　７０７　　記録媒体
　９１０　　計画情報（目標）
　９２０　　計画情報（実績）
　９３０　　差分情報
　９４０　　主要差分情報 101, 500, 550, 801 Knowledge management device 110 Target input unit 120 Result input unit 130, 830 Plan information storage unit 140, 840 Causal relationship storage unit 150, 850 Factor identification unit 200 Plan information 201 Information node 202 Information link 203 Node label 204 Node value 300 Causal relationship information 301 Causal relationship node 302 Plan information node link 303 Factor node 304 Factor link 510, 560 Countermeasure storage unit 520, 570 Countermeasure derivation unit 600 Countermeasure information 610 Factor node 611 Factor / countermeasure relationship information 620 Countermeasure node 630 Link between countermeasures 700 Computer device 701 CPU
702 Storage unit 703 Storage device 704 Input unit 705 Output unit 706 Communication unit 707 Recording medium 910 Plan information (target)
920 Plan information (actual)
930 Difference information 940 Main difference information

Claims (10)

  1. 　ノードに関連付けて当該ノードの値であるノード値の目標を記憶する計画情報保管手段と、
    　ノードに関連付けて当該ノードのノード値を変化させる要因を記憶する因果関係保管手段と、
    　ノードのノード値の実績を取得して、実績が当該ノードに関連付けられた目標を達成していないと、当該ノードに関連付けられた要因を出力する要因特定手段と、を備える知識管理装置。     Plan information storage means for storing a node value target which is a value of the node in association with the node;
    A causal relationship storing means for storing a factor that changes the node value of the node in association with the node;
    A knowledge management device comprising factor identification means for acquiring a node value result of a node and outputting a factor associated with the node if the result does not achieve a goal associated with the node.
  2. 　前記計画情報保管手段は、ａ）ノード値が他のノード（以降、影響ノードと呼ぶ）のノード値の影響を受けるノードと、ｂ）当該影響ノードとを、関連付けるリンクを有するグラフを格納し、
    　前記要因特定手段は、ノードのノード値の実績を取得して、実績が当該ノードに関連付けられた目標を達成していないと、
    当該ノードに関連付けられた影響ノードに関連付けられた要因を出力する、請求項１の知識管理装置。     The plan information storage means stores a graph having a link in which a) a node value is affected by a node value of another node (hereinafter referred to as an influence node), and b) the influence node.
    The factor specifying means obtains a node value result of a node, and if the result does not achieve the target associated with the node,
    The knowledge management apparatus according to claim 1, wherein a factor associated with an affected node associated with the node is output.
  3. 　要因と対策を関連付けて格納する対策保管手段と、
    　前記要因特定処理手段が出力した要因に関連付けられている対策を出力する対策導出手段と、をさらに備える、請求項１乃至２の何れか１項の知識管理装置。     Measures storage means for storing factors and measures in association with each other;
    The knowledge management apparatus according to claim 1, further comprising: a countermeasure derivation unit that outputs a countermeasure associated with the factor output by the factor identification processing unit.
  4. 　前記対策導出手段は、ａ）対策のリストと、ｂ）当該対策のリストに格納された対策に関連付けられた要因のリストとを、
    　　前記要因のリストに追加される新たな要因の数が最大となるような対策を前記対策リストに追加することを、
    　　　前記要因リストに前記要因特定処理手段が出力したすべての要因が包含されるまで繰り返して
    　作成し、
    作成された前記対策のリストに含まれる対策を出力する、請求項３の知識管理装置。     The countermeasure deriving means includes a) a list of countermeasures, and b) a list of factors associated with the countermeasures stored in the countermeasure list.
    Adding a measure that maximizes the number of new factors added to the factor list to the measure list;
    Create repeatedly until all the factors output by the factor identification processing means are included in the factor list,
    The knowledge management device according to claim 3, wherein a countermeasure included in the prepared countermeasure list is output.
  5. 　前記対策導出手段は、ａ）対策のリストと、ｂ）当該対策のリストに格納された対策に関連付けられた要因のリストとを、
    　前記要因リストに前記要因特定処理手段が出力した要因集合、前記対策のリスト内の対策各々に関連付けられた要因集合との共通部が最大となるような対策を前記対策リストに追加することを、
    　　前記要因リストに前記要因特定処理手段が出力したすべての要因が包含されるまで繰り返して
    　作成し、
    作成された前記対策のリストに含まれる対策を出力する、請求項３の知識管理装置。     The countermeasure deriving means includes a) a list of countermeasures, and b) a list of factors associated with the countermeasures stored in the countermeasure list.
    Adding a countermeasure that maximizes the common part between the factor set output by the factor specifying processing means to the factor list and the factor set associated with each countermeasure in the countermeasure list;
    Create repeatedly until all the factors output by the factor identification processing means are included in the factor list,
    The knowledge management device according to claim 3, wherein a countermeasure included in the prepared countermeasure list is output.
  6. 　ノードに関連付けて当該ノードの値であるノード値の目標を記憶し、
    　ノードに関連付けて当該ノードのノード値を変化させる要因を記憶し、
    　ノードのノード値の実績を取得して、実績が当該ノードに関連付けられた目標を達成していないと、当該ノードに関連付けられた要因を出力する知識管理方法。     Store the node value target that is the value of the node in association with the node,
    Stores the factors that change the node value of the node in association with the node,
    A knowledge management method for acquiring a node value record of a node and outputting a factor associated with the node if the record does not achieve the target associated with the node.
  7. 　ａ）ノード値が他のノード（以降、影響ノードと呼ぶ）のノード値の影響を受けるノードと、ｂ）当該影響ノードとを、関連付けるリンクを有するグラフを記憶し、
    　ノードのノード値の実績を取得して、実績が当該ノードに関連付けられた目標を達成していないと、
    当該ノードに関連付けられた影響ノードに関連付けられた要因を出力する、請求項６の知識管理方法。     a) a node whose node value is affected by the node value of another node (hereinafter referred to as an influence node); and b) a graph having a link for associating the influence node.
    If you get a node value achievement for a node and the achievement does not meet the goal associated with that node,
    The knowledge management method according to claim 6, wherein a factor associated with an influence node associated with the node is output.
  8. 　要因と対策を関連付けて記憶し、
    　さらに、出力した要因に関連付けられている対策を出力する請求項６乃至７の何れか１項の知識管理方法。     Memorize the factors and measures,
    Furthermore, the knowledge management method of any one of Claim 6 thru | or 7 which outputs the countermeasure linked | related with the output factor.
  9. 　ａ）対策のリストとｂ）当該対策のリストに格納された対策に関連付けられた要因のリストとを、前記要因のリストに追加される新たな要因の数が最大となるような対策を前記対策リストに追加することを、前記要因リストに出力したすべての要因が包含されるまで繰り返して作成し、作成された前記対策のリストに含まれる対策を出力する、請求項８の知識管理方法。 a) a list of countermeasures and b) a list of factors associated with the countermeasures stored in the list of countermeasures, and a countermeasure that maximizes the number of new factors added to the list of factors. The knowledge management method according to claim 8, wherein adding to the list is repeated until all the factors output in the factor list are included, and measures included in the prepared list of measures are output.
  10. 　コンピュータに、
    　ノードに関連付けて当該ノードの値であるノード値の目標を記憶する処理と、
    　ノードに関連付けて当該ノードのノード値を変化させる要因を記憶する処理と、
    　ノードのノード値の実績を取得して、実績が当該ノードに関連付けられた目標を達成していないと、当該ノードに関連付けられた要因を出力する処理と、を実行させるプログラムを記録する記憶媒体。     On the computer,
    A process of storing a node value target which is a value of the node in association with the node;
    A process of storing factors that change the node value of the node in association with the node;
    A storage medium for recording a program that acquires a node value record of a node and executes a process of outputting a factor associated with the node if the record does not achieve the target associated with the node.
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