WO2022018899A1 - System for extracting subtree from kpi tree - Google Patents

System for extracting subtree from kpi tree Download PDF

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Publication number
WO2022018899A1
WO2022018899A1 PCT/JP2021/007735 JP2021007735W WO2022018899A1 WO 2022018899 A1 WO2022018899 A1 WO 2022018899A1 JP 2021007735 W JP2021007735 W JP 2021007735W WO 2022018899 A1 WO2022018899 A1 WO 2022018899A1
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node
tree
kpi
information
information node
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PCT/JP2021/007735
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French (fr)
Japanese (ja)
Inventor
僚介 奥野
忠輔 中川
識史 尾田
保人 住吉
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株式会社日立製作所
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Publication of WO2022018899A1 publication Critical patent/WO2022018899A1/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

Definitions

  • the present invention relates to a technique for extracting a partial tree from a KPI (Key Performance Indicator) tree.
  • the KPI tree decomposes one KGI (Key Goal Indicator) into one or more elements (hereinafter, nodes), and if there are two or more elements, defines an operation between the elements, and each element. Is also a tree that can be obtained by decomposing it in the same way. By inputting a numerical value to the node corresponding to the child, the value of the node corresponding to the parent can be calculated. By doing this recursively, the KGI value of the vertex node can be calculated.
  • KGI Key Goal Indicator
  • the KPI tree uses KPI as an index to measure how well a certain process of a business is achieved, and KGI as an index to measure how much the company's final goal is achieved. It is often used as a method for quantitatively evaluating business value.
  • KPI is often a non-financial index that can be observed in the planned operating time of a certain machine or in the field
  • KGI is measured from a management perspective such as profit and ROA (Return On Assets).
  • ROA Return On Assets
  • a financial indicator Often a financial indicator.
  • the ability and experience of the creator is required to logically structure the relationship between KGI and KPI. Therefore, creators who have little experience in creating KPI trees need to receive some support.
  • Patent Document 1 is a technique for supporting the creation of a KPI tree.
  • Patent Document 1 describes a system and a method for grasping a change in the value of a node connected to KGI and KGI even when there is a constraint condition for fixing the value of a certain node.
  • Patent Document 1 describes a technique for converting a KPI tree structure into a new KPI tree structure in which a designated node is used as a leaf node.
  • Patent Document 1 discloses a technique for automatically rewriting a tree structure starting from a certain node, which makes it easy to grasp the numerical change of a node in the KPI tree.
  • Patent Document 1 discloses a technique capable of extracting a partial tree suitable for reuse from a KPI tree.
  • One aspect of the present invention is a system for extracting a partial tree from a KPI tree including an information node indicating information and an operation node indicating an operation, wherein one or more arithmetic devices and one or more storage devices are used.
  • the one or more storage devices include the first KPI tree, and the one or more arithmetic devices are a first lower information node and a higher information node of the first lower information node in the first KPI tree.
  • the first partial tree including the first upper information node is extracted, and the change of the first upper information node due to the change of the first lower information node is calculated based on the calculation node included in the first partial tree. Unnecessary nodes are deleted from the first subtree.
  • a partial tree suitable for reuse can be extracted from the KPI tree.
  • KPI tree component extraction It is a figure which shows the processing flow example of the KPI tree component extraction. It is a figure explaining the flow of KPI tree component creation by the KPI tree creation support method. This is an example of KPI tree parts created by the KPI tree creation support method. It is a figure explaining the connection method of the created KPI tree component.
  • This system may be a physical computer system (one or more physical computers) or a system built on a computer resource group (multiple computer resources) such as a cloud platform.
  • a computer system or computational resource group includes one or more interface devices (including, for example, communication devices and input / output devices), one or more storage devices (including, for example, memory (main storage) and auxiliary storage devices), and one or more operations. Includes equipment.
  • the function When the function is realized by executing the program including the instruction code by the arithmetic unit, the function is at least the arithmetic unit because the defined processing is appropriately performed by using the storage device and / or the interface device and the like. It may be a part.
  • the process described with the function as the subject may be a process performed by an arithmetic unit or a system having the arithmetic unit.
  • the program may be installed from the program source.
  • the program source may be, for example, a program distribution computer or a computer-readable storage medium (eg, a computer-readable non-transient storage medium).
  • the description of each function is an example, and a plurality of functions may be combined into one function, or one function may be divided into a plurality of functions.
  • the support device extracts a partial tree from the past KPI tree and provides it to the user.
  • the support device extracts a partial tree from the past KPI tree and provides it to the user.
  • the KPI tree includes information nodes, arithmetic nodes, and links connecting the nodes.
  • the arithmetic node is a node that performs a predetermined arithmetic and can represent an arbitrary function.
  • An information node is a node that shows specific information without performing an operation.
  • the support device may delete nodes that are not necessary for calculating the change of the specific upper node due to the change of the specific lower information node. As a result, the partial tree to be extracted can be simplified and the suitability as a component can be improved.
  • FIG. 1 is a diagram showing a hardware configuration example of the KPI tree creation support system 10 of the present embodiment.
  • the KPI tree creation support system 10 includes a main storage device 101, an auxiliary storage device 102, an input device 103, an output device 104, an arithmetic unit 105, and a communication device 106. These components are communicably connected to each other by bus 107.
  • the main storage device 101 can be composed of a volatile storage element such as a RAM (Random Access Memory).
  • the auxiliary storage device 102 can be composed of a non-volatile storage element such as an SSD (Solid State Drive) or a hard disk drive.
  • the input device 103 is a keyboard, mouse, touch panel, etc. that accepts an input operation from the user, and the output device 104 displays the processing result to the user.
  • the arithmetic unit 105 for example, a CPU (Central Processing Unit), executes a program loaded from the auxiliary storage device 102 or an external device into the main storage device 101, performs integrated control of the device itself, and performs various determinations, calculations, and controls. Perform processing.
  • the communication device 106 is a device for connecting to a network and exchanging data.
  • the KPI tree creation support system 10 can be composed of one computer or a plurality of computers communicating with each other, including one or more storage devices and one or more arithmetic units.
  • FIG. 2 shows an example of a functional configuration implemented by the KPI tree creation support system 10 by executing a program.
  • the KPI tree creation support system 10 of the present embodiment includes a display unit 210, an input operation unit 220, an information processing unit 230, and a past KPI tree database 240.
  • the information processing unit 230 includes a creation KPI tree storage unit 231, a past KPI tree processing unit 234, a node search unit 238, and a subgoal candidate search unit 239.
  • the display unit 210 receives from the created KPI tree storage unit 231 and the subgoal candidate search unit 239, which will be described later, and displays the KPI tree on the output device 104.
  • FIG. 3 shows an example of a completed KPI tree displayed on the output device 104.
  • the display unit 210 displays the KPI tree 3 in which the KGI 301 is arranged in the vertex node and the nodes 302 to 313 connected to the vertex node are arranged.
  • a node whose name is described by an operation such as "-" such as a node 302
  • an operation node a node whose name is described by information rather than an operation such as a node 303
  • an information node a node whose name is described by information rather than an operation such as a node 303
  • the calculation node is a node that performs the operation indicated by the name
  • the information node is a node that indicates the information specified by the name.
  • the name of the information node is an example of an information identifier.
  • a link In FIG. 3, what is represented by an arrow is called a link.
  • the link has a direction, and of the two nodes connected by the link, the node at the start point is called the child node, and the node at the end is called the parent node.
  • node 301 is the parent node of node 302 at link 414.
  • a node that can be both a parent node and a child node, such as node 303 is called an intermediate node
  • a node that does not have a child node, such as node 307 and node 308, is called a leaf node.
  • the leaf node and the KGI node are information nodes
  • the parent node and child node of the information node are arithmetic nodes.
  • the parent node and child node of the arithmetic node are information nodes.
  • a node shown by a dotted line like node 311 is called a synchronization node.
  • a sync node refers to a node other than that node and shows the same information.
  • node 311 refers to the same information as node 305 and displays the same name "C”.
  • the synchronization node is a leaf node.
  • a tree that uses only the nodes and links that make up a certain KPI tree is called a partial tree of the KPI tree.
  • the KPI tree 31 composed of the nodes 301, 302, 303, 313 and the links 314, 315, 316 is a partial tree of the KPI tree 3.
  • the top side of the KPI tree 3 is called the upper side, and the end side of the KPI tree is called the lower side. Further, the fact that the relationship between the two nodes is a higher node and a lower node means that the lower node is a descendant node of the upper node.
  • the elements decomposed from left to right with the KGI 301 as the apex are arranged, but the layout of the components of the KPI tree is arbitrary. The components may be arranged, for example, from top to bottom.
  • the input operation unit 220 provides information to the node search unit 238 and the partial KPI tree extraction unit 235 according to the operation received via the input device 103.
  • the specific content of the information provided will be described later.
  • the creation KPI tree storage unit 231 of the information processing unit 230 stores the data of the KPI tree created by the user.
  • the creation KPI tree storage unit 231 includes a creation KPI tree node data storage unit 232 and a creation KPI tree link data storage unit 233.
  • the created KPI tree node data storage unit 232 stores the node data of the KPI tree created by the user.
  • the created KPI tree link data storage unit 233 stores the link data of the KPI tree created by the user.
  • the past KPI tree processing unit 234 includes a partial KPI tree extraction unit 235, a node data temporary storage unit 236, and a link data temporary storage unit 237.
  • the partial KPI tree extraction unit 235 extracts a partial tree that can be used as a KPI tree component from the past KPI tree stored in the past KPI tree database 240. In the extraction of the partial tree, the partial KPI tree extraction unit 235 prunes unnecessary nodes and links. The specific processing flow will be described later.
  • the node data temporary storage unit 236 stores the node data of the target KPI tree to be processed by the partial KPI tree extraction unit 235.
  • the link data temporary storage unit 237 stores the link data of the target KPI tree to be processed by the partial KPI tree extraction unit 235.
  • the node search unit 238 obtains node data from the input operation unit 220 and searches the past node data storage unit 241 for the node indicated by the node data.
  • the sub-goal candidate search unit 239 searches for sub-goal candidates based on the matching node data in the past node data storage unit 241 acquired by the node search unit 238 and the link data of the past link data storage unit 242. Details of the subgoals will be described later.
  • the past KPI tree database 240 includes a past node data storage unit 241 and a past link data storage unit 242.
  • the past node data storage unit 241 stores the data related to the node among the data of the KPI tree created in the past.
  • the past link data storage unit 242 stores the data related to the link among the data of the KPI tree created in the past.
  • Each functional unit can be realized by the arithmetic unit 105, the main storage device 101 and / or the auxiliary storage device 102.
  • the storage unit can be mounted by the main storage device 101 and / or the auxiliary storage device 102, and other functional units can be mounted by the arithmetic unit 105.
  • FIG. 4 shows an example of a node data table structure used in the created KPI tree node data storage unit 232, node data temporary storage unit 236, and past node data storage unit 241. These storage units store information on the nodes of the KPI tree, and store tables having the same configuration in the present embodiment.
  • the table structure illustrated in FIG. 4 includes a tree ID column 401, a node ID column 402, a category column 403, a text column 404, a synchronization source column 405, a synchronization destination column 406, an attribute column 407, and a check column 408.
  • Each record shows information about one node in the KPI tree.
  • the tree ID column 401 indicates an ID that identifies the KPI tree.
  • the node ID column 402 indicates an ID that identifies the nodes that make up the tree identified by the tree ID column 401.
  • the category column 403 indicates whether the node is the arithmetic node "Op" or the information node "Info”.
  • the text field 404 indicates a specific name of the node.
  • the synchronization source column 405 indicates the node ID of the referenced node when a certain node is a synchronization node.
  • the synchronization destination column 406 indicates the node ID of the reference source when a certain node is referenced by a certain synchronization node.
  • the attribute column 407 indicates whether or not a node is a subgoal.
  • the check column 408 is used for the partial KPI tree extraction unit 235, as will be described later.
  • the subgoal registered in the attribute field 407 is a node determined to be important as an intermediate node of the KPI tree, and is input by the creator when storing the data of the KPI tree, for example. For example, a finance node calculated from a node that cannot be measured by monetary amount may be defined as a subgoal.
  • the specific usage of the check 408 will be described later together with the processing flow of the past KPI tree processing unit 334.
  • the record 409 is the data related to the node 301 of the KPI tree 3 shown in FIG.
  • Record 410 is data about node 311 of the KPI tree 3.
  • the record 410 stores data in the synchronization source column 405, and the node 311 is a synchronization node.
  • the text information of the node ID 5 is quoted in the text field 404 of the record 410.
  • the output device 104 displays the node having the node ID 11 as “C”.
  • FIG. 5 shows an example of the link data table structure used in the created KPI tree link data storage unit 233, the link data temporary storage unit 237, and the past link data storage unit 247.
  • the table structure example illustrated in FIG. 5 includes a tree ID column 501, a link ID column 502, a parent node ID column 503, a child node ID column 504, and a check column 505.
  • the tree ID column 501 indicates an ID that identifies the KPI tree.
  • the link ID column 502 indicates an ID that identifies the link of the KPI tree identified by the tree ID column 501.
  • the parent node ID column 503 indicates an ID that identifies the parent node among the two nodes connected by the link.
  • the child node ID column 504 indicates an ID that identifies the child node among the two nodes connected by the link.
  • the check box 505 is used for the partial KPI tree extraction unit 235, as will be described later. The specific usage of the check 408 will be described later together with the processing flow of the past KPI tree processing unit 334.
  • the record 506 is the data related to the link 314 of the KPI tree 3 shown in FIG.
  • FIG. 6 shows an example of the processing flow of the KPI tree creation support method.
  • the user inputs KPI information and KGI information using the input device 103.
  • the user can also enter subordinate nodes including KGI child nodes and links between the nodes.
  • FIG. 8A which will be described later, shows an example of user input.
  • the KPI tree creation support system 10 can obtain the KPI tree creation condition desired by the user.
  • the input operation unit 220 acquires information about the calculation method of KGI, KPI and KGI, and classifies it into node data and link data.
  • the input operation unit 220 stores the node data in the creation KPI tree node data storage unit 232 and stores the link data in the creation KPI tree link data storage unit 233. (S1).
  • the KPI tree creation support system 10 supports the user's KPI tree creation by repeating creating parts from the KPI tree created in the past and presenting them to the user (S2).
  • FIG. 7 shows a more detailed flow of step S2.
  • the display unit 210 outputs the data stored in the created KPI tree storage unit 231 to the output device 104 (S201).
  • FIG. 8A shows an example of an image generated by the display unit 210 from the user input acquired in step S1 and displayed by the output device 104.
  • FIG. 8A shows an image of user input by the display unit 210, but the user may input user data by the same GUI.
  • the user can then select, among the input nodes, an information node having no parent node other than the KGI node by using the input device 103.
  • nodes 802 and 803 can be selected.
  • the input operation unit 220 receives the ID of the selected node (S202).
  • the input operation unit 220 acquires the information of the selected node from the creation KPI tree node data storage unit 232 and passes it to the node search unit 238.
  • the following description exemplifies the behavior when the node 802 is selected by the user.
  • the node search unit 238 that has received the information of the node selected from the input operation unit 220 searches the past node data storage unit 241 for a node having a name that matches the name of the selected node (text field 404) (S203). ).
  • the user inputs the parent node and link of the selected node and creates a KPI tree (S215). Specifically, the input operation unit 220 acquires the information input by the user via the input device 103 and stores it in the creation KPI tree storage unit 231.
  • the node search unit 238 acquires a tree ID from the field of the tree ID column 401 of the record of that node, and is a subgoal candidate. It is transmitted to the search unit 239.
  • the node search unit 238 sets the IDs indicated by the tree ID column 401 and the node ID column 402 of the past node data storage unit 241. Send the list.
  • a node having the same name as the selected node may exist in a different past KPI tree, and a plurality of nodes having the same name as the selected node may exist in one past KPI tree.
  • the sub-goal candidate search unit 239 that received the tree ID searches for a node with a sub-goal attribute (closest sub-goal node) that can be reached from the selected node with the fewest links in the past KPI tree. There are no other subgoal nodes between the selected node and the subgoal node obtained by the search. In this way, by selecting the subgoal node closest to the selected node among the subgoal nodes with the names specified in advance, the partial tree more suitable for the part can be extracted. As described with reference to FIG. 4, the past node data storage unit 241 indicates a node having a subgoal attribute in the attribute column 407. Depending on the design, other subgoal nodes may be selected.
  • the subgoal candidate search unit 239 further calculates the similarity KS between the past KPI tree and the information of the created KPI tree input by the user in step S1.
  • the similarity KS can be calculated as follows.
  • Ni is a character string in the text field 404 of the i-th leaf node of the KPI tree input in step S1.
  • k is the number of leaf nodes of the KPI tree input in step S1.
  • mj is a character string in the text field 404 of the jth node of the past KPI tree.
  • l is the number of nodes in the past KPI tree.
  • Distance refers to a function that calculates the distance between strings, such as the minimum edit distance. It should be noted that the value obtained by distance is a real number, and the larger the numerical value, the greater the distance between the character strings. For example, in the input example of FIG. 8A, n1 is "sales" and k is 3.
  • the sub-goal candidate search unit 239 ranks the sub-goals in descending order of similarity KS, and lists the selected node ID, the sub-goal ID, and the tree ID and rank set of the past KPI tree including the sub-goal.
  • step S203 when the node search unit 238 finds a plurality of nodes having the same name as the selected node but different node IDs from the same past KPI tree, the subgoal candidate search unit 239 finds the subgoal of the past KPI tree and the selected node. Further rank in the pair.
  • the subgoal candidate search unit 239 determines the rank based on the number of links required to reach the subgoal node and the selected node. The smaller the number of required links, the higher the rank.
  • the subgoal candidate search unit 239 may delete the pair having a large number of links.
  • the sub-goal candidate search unit 239 sends the created sub-goal candidate list to the display unit 210 (S205).
  • the display unit 210 receives the subgoal candidate list and outputs the information of the subgoal candidate list to the output device 104 (S206).
  • FIG. 8B shows an example of displaying information in the subgoal candidate list.
  • Listing 85 shows the names of the subgoal nodes, along with the names of the KGIs (“customer satisfaction”), in order according to the rank of the subgoal nodes determined in step S205. Subgoal nodes with the same name in different KPI trees are identified by the number immediately following the name, and different nodes with the same name in the same KPI node are identified by the number after the hyphen.
  • the user selects one or more subgoal candidates from the list 85 and inputs them using the input device 103.
  • the input operation unit 220 receives the input information and sends it to the past KPI tree processing unit 234 together with the subgoal candidate list (S207). In this way, the display unit 210 presents the rank of the subgoal candidate and the user, and the input operation unit 220 accepts the selection from the user. This makes it possible to select a subgoal candidate that is more appropriate for the user.
  • the past KPI tree processing unit 234 that received the subgoal candidate information acquires the node data and the link data having the KPI tree ID including the subgoal node selected by the user from the past KPI tree database 240, and temporarily converts the node data into node data.
  • the link data is stored in the storage unit 236 in the link data temporary storage unit 237 (S208).
  • step S205 a subgoal list including "(subgoal node name, KPI tree ID): (production amount, 7), (manufacturing cost, 2), (fixed asset, 9)" based on the input as shown in FIG. 8A. Is created. Further, it is assumed that the subgoal "manufacturing cost" is selected in step S207.
  • the past KPI tree processing unit 234 stores the node data and the link data of the past KPI tree having the KPI tree ID of 2.
  • the partial KPI tree extraction unit 235 uses the node data temporary storage unit 236 and the link data temporary storage unit 237 to form a calculation node from the partial tree including the route from the node selected by the user in step S202 to the subgoal node.
  • the necessary part is extracted by utilizing the property of the synchronization node (S209).
  • FIG. 9 shows an example of the extraction processing flow in step S209. Further, FIG. 10 is a diagram for explaining the operation of the processing flow example shown in FIG. FIG. 10 shows a lower portion extracted from the past KPI tree stored in the node data temporary storage unit 236 and the link data temporary storage unit 237 in step S208.
  • Nodes 1001 to 1014 are nodes with node IDs 1001 to 1014 in the past KPI tree having a tree ID of 2. Further, node 1013 is a synchronization node and refers to node 1006. Also, list 1015 is a list used for extraction. This list 1015 is possessed by the partial KPI tree extraction unit 235. It is assumed that the node of "manufacturing capacity" is selected in step S202 and the subgoal node of "manufacturing cost" is specified in step S207.
  • the partial KPI tree extraction unit 235 creates a list including the selected nodes (S901).
  • the node ID of the selected node is 1008, the partial KPI tree extraction unit 235 includes 1008 in the list 1015 (FIG. 10).
  • the partial KPI tree extraction unit 235 pays attention to the first node of the list 1015.
  • the partial KPI tree extraction unit 235 pays attention to the node 1008 (S903), inputs 1 to the check field 408 of the node of interest in the node data temporary storage unit 236, and inputs 1 to the parent node in the link data temporary storage unit 237. Enter 1 in the check field 505 of the link to (S904).
  • the partial KPI tree extraction unit 235 adds the synchronization node of the reference source to the list 1015 (S906). For example, if node 1006 in FIG. 10 is of interest, the partial KPI tree extraction unit 235 adds to list 1015 a synchronization node ID 1013 that references node 1006.
  • the partial KPI tree extraction unit 235 newly pays attention to the parent node of the node of interest and displays the check box 408 of the parent node. Enter 1 (S907).
  • this attention node is an arithmetic node.
  • the partial KPI tree extraction unit 235 checks the child node of that node and the check column 408 of the link to the child node. Enter 1 in 505 (S909). For example, when the node 1007 in FIG. 10 is attracting attention, the partial KPI tree extraction unit 235 inputs 1 to the check boxes 408 and 505 of the link to the node 1005 and the node 1005.
  • step S909 When the process of step S909 is completed or the node of interest is not "x" or " ⁇ " (S908: NO), the partial KPI tree extraction unit 235 inputs 1 in the check field 505 of the link to the parent node of the node of interest. Then, the parent node of the attention node is set as a new attention node (S910).
  • this attention node is an information node. If the node of interest is not a subgoal node (S9111: NO), the partial KPI tree extraction unit 235 returns to step S904 and repeats steps S904 to S910 until the subgoal node becomes a node of interest.
  • the partial KPI tree extraction unit 235 deletes the first element of the list (S912). For example, when the content of the list is "1008, 1013", the partial KPI tree extraction unit 235 deletes the first element 1008 and sets the list to "1013". If the list has no elements after step S9812 (S902: YES), the partial KPI tree extraction unit 235 inputs 1 in the check box 408 of the subgoal node (S913). In the example of FIG. 10, 1 is input to the check field 408 of the node 1001 of the manufacturing cost.
  • the partial KPI tree extraction unit 235 deletes the node and link records in which the check column 408 or 505 does not contain 1 from the records of the node data temporary storage unit 236 and the link data temporary storage unit 237 (S914). Further, the partial KPI tree extraction unit 235 deletes a node having neither a child node nor a parent node (S915).
  • the node 1014 and the link connecting the node 1014 and the node 1002 are deleted.
  • FIG. 11 shows a partial KPI tree after step 208 for the KPI tree shown in FIG. This partial KPI tree becomes the KPI tree component 11.
  • the operation node of the past KPI tree is either "x", " ⁇ ", "+” or "-".
  • the link to the child node and the child node of the arithmetic node of "x" or " ⁇ ” is left, and the link to the child node and the child node of the arithmetic node of "+” or "-" is deleted.
  • the KPI tree can include operation nodes that perform arbitrary operations, and depending on the operation method, multiple inputs are independent or interdependent with respect to the effect on the output. It is possible to determine whether or not it is.
  • the synchronization node when the reference node of the synchronization node is left (not deleted) in the route from the selected node to the subgoal node and the synchronization node has a route to the subgoal node, the synchronization node is partially treeed. Include in. This makes it possible to construct a partial tree that can more accurately calculate the change of the subgoal node due to the change of the selected node. It is not necessary to leave the synchronization node.
  • the partial KPI tree extraction unit 235 sends the data of the created partial tree of the past KPI tree, that is, the data stored in the node data temporary storage unit 236 and the link data temporary storage unit 237 to the display unit 210.
  • the display unit 210 displays the received partial tree information (S210).
  • the input operation unit 220 receives a user's choice as to whether to use the displayed partial tree as a component (S211).
  • the partial KPI tree extraction unit 235 stores the contents of the node data temporary storage unit 236 and the link data temporary storage unit 237 in the creation KPI tree storage unit 231 (S213). Further, if there is a node in the leaf node of the created KPI tree that matches the subgoal node of the part, the partial KPI tree extraction unit 235 has the partial tree created in step S209 and the created KPI already in the created KPI tree storage unit 231. Join the trees.
  • the partial KPI tree extraction unit 235 joins two trees so that the vertex node of the partial tree, which is a subgoal node, and the leaf node of the created KPI tree overlap. Specifically, the partial KPI tree extraction unit 235 deletes the link from the partial tree to the subgoal node and its child node, and creates a link between the child node and the corresponding leaf node of the created KPI tree.
  • FIG. 12 shows an example of joining a partial tree and a created KPI tree. It is assumed that there is a created KPI tree including a node 801 and a tree component 11 as shown in FIG.
  • the partial KPI tree extraction unit 235 joins the trees.
  • the partial KPI tree extraction unit 235 deletes the node 1101 and the link 1115, and newly creates a link 1201 between the node 805 and the node 1102.
  • the display unit 210 may output the created KPI tree in which the tree parts are combined to the output device 104 as shown in FIG.
  • step S215 is executed. After that, if all the nodes of the created KPI tree except the KGI node have a parent node (S214: YES), step S2 is terminated. If the determination result in step S214 is NO, the flow returns to step S201.
  • the KPI tree creation support system 10 uses the tree IDs of all the node data and the link data of the created KPI tree storage unit 231 as the tree IDs in the past KPI tree database. Change to a value that does not exist in the past KPI tree database 240 (S3). With the above, the KPI tree creation support system is terminated.
  • a KPI tree useful for using the parts of the KPI tree to be created is searched from the records of the past KPI trees, the tree is made into parts, and the tree is combined with the KGI and the tree connected to the KGI. You can easily divert past records.
  • the present invention is not limited to the above-described embodiment, but includes various modifications.
  • the above-described embodiment has been described in detail in order to explain the present invention in an easy-to-understand manner, and is not necessarily limited to the one including all the configurations described.
  • it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment and it is also possible to add the configuration of another embodiment to the configuration of one embodiment.
  • each of the above configurations, functions, processing units, etc. may be realized by hardware, for example, by designing a part or all of them with an integrated circuit. Further, each of the above configurations, functions, and the like may be realized by software by the arithmetic unit interpreting and executing a program that realizes each function. Information such as programs, tables, and files that realize each function can be placed in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), or a recording medium such as an IC card or an SD card.
  • SSD Solid State Drive
  • control lines and information lines indicate what is considered necessary for explanation, and not all control lines and information lines are shown in the product. In practice, it can be considered that almost all configurations are interconnected.

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Abstract

Disclosed is a system for extracting a subtree from a KPI tree including an information node indicating information and a computation node indicating computation. The system includes one or more arithmetic devices and one or more storage devices. The one or more storage devices store a first KPI tree therein. The one or more arithmetic devices extract, in the first KPI tree, a first subtree including a first lower-level information node and a first higher-level information node that is a higher-level information node of the first lower-level information node, and on the basis of a computation node included in the first subtree, delete, from the first subtree, a node unnecessary for calculation of a change in the first higher-level information node caused by a change in the first lower-level information node.

Description

ＫＰＩツリーから部分ツリーを抽出するシステムA system that extracts partial trees from KPI trees 参照による取り込みCapture by reference
　本出願は、２０２０年７月２１日に出願された日本出願である特願２０２０－１２４５４５の優先権を主張し、その内容を参照することにより、本出願に取り込む。 This application claims the priority of Japanese Patent Application No. 2020-124545, which is a Japanese application filed on July 21, 2020, and incorporates it into this application by referring to its contents.
　本発明は、ＫＰＩ(Ｋｅｙ　Ｐｅｒｆｏｒｍａｎｃｅ　Ｉｎｄｉｃａｔｏｒ:重要業績評価指標)ツリーから部分ツリーを抽出する技術に関する。 The present invention relates to a technique for extracting a partial tree from a KPI (Key Performance Indicator) tree.
　ＫＰＩツリーは、一つのＫＧＩ（Ｋｅｙ　Ｇｏａｌ　Ｉｎｄｉｃａｔｏｒ：重要目標達成指標）を一つ以上の要素（以下、ノード）に分解し、要素が二つ以上ある場合は要素間に演算を定義し、各要素も同様に分解することでえら得るツリーである。子にあたるノードに数値を入力すると、親にあたるノードの値を算出可能である。これを再帰的に行うことで頂点ノードのＫＧＩの数値を算出できる。 The KPI tree decomposes one KGI (Key Goal Indicator) into one or more elements (hereinafter, nodes), and if there are two or more elements, defines an operation between the elements, and each element. Is also a tree that can be obtained by decomposing it in the same way. By inputting a numerical value to the node corresponding to the child, the value of the node corresponding to the parent can be calculated. By doing this recursively, the KGI value of the vertex node can be calculated.
　ＫＰＩツリーは事業のあるプロセスがどの程度達成されているかを測る指標をＫＰＩ、企業の最終目標がどの程度達成されているかを測る指標をＫＧＩとして、その関係性を構造化し、数値を代入して事業価値を定量評価する手法として使われることが多い。 The KPI tree uses KPI as an index to measure how well a certain process of a business is achieved, and KGI as an index to measure how much the company's final goal is achieved. It is often used as a method for quantitatively evaluating business value.
　しかしながら、ＫＰＩはある機械の計画稼働時間や、現場で観測できる非財務的指標であることが多いのに対し、ＫＧＩは利益やＲＯＡ(Ｒｅｔｕｒｎ　Ｏｎ　Ａｓｓｅｔｓ：総資産利益率)など経営的視点で測られる財務指標であることが多い。ＫＧＩとＫＰＩの関係を論理的に構造化するためには作成者の能力と経験が必要である。そのため、ＫＰＩツリー作成経験の少ない作成者は何らかの支援を受ける必要がある。 However, while KPI is often a non-financial index that can be observed in the planned operating time of a certain machine or in the field, KGI is measured from a management perspective such as profit and ROA (Return On Assets). Often a financial indicator. The ability and experience of the creator is required to logically structure the relationship between KGI and KPI. Therefore, creators who have little experience in creating KPI trees need to receive some support.
　ＫＰＩツリーの作成を支援する技術として、例えば、特許文献１がある。特許文献１は、あるノードの値を固定する制約条件があった際にもＫＧＩとＫＧＩにつながるノードの値の変化を把握するためのシステムおよび手法を記載している。特許文献１は、ＫＰＩツリー構造を、指定されたノードを葉ノードにした新たなＫＰＩツリー構造に変換する技術が記載されている。 For example, Patent Document 1 is a technique for supporting the creation of a KPI tree. Patent Document 1 describes a system and a method for grasping a change in the value of a node connected to KGI and KGI even when there is a constraint condition for fixing the value of a certain node. Patent Document 1 describes a technique for converting a KPI tree structure into a new KPI tree structure in which a designated node is used as a leaf node.
特開２０１９－２００６３１号公報Japanese Unexamined Patent Publication No. 2019-200631
　特許文献１はあるノードを起点にツリー構造を自動的に書き換える技術を開示し、ＫＰＩツリーのノードの数値変化の把握が容易になる。しかし、この技術で生成できるＫＰＩツリーをもとに別の業界や事業のＫＰＩツリーを作成する際に、書き換え前のＫＰＩツリー情報のほとんどが残っており、ツリーを部分的かつ汎用的に使用することに適していない。したがって、ＫＰＩツリーから再利用に適した部分ツリーを抽出できる技術が望まれる。 Patent Document 1 discloses a technique for automatically rewriting a tree structure starting from a certain node, which makes it easy to grasp the numerical change of a node in the KPI tree. However, when creating a KPI tree for another industry or business based on the KPI tree that can be generated by this technology, most of the KPI tree information before rewriting remains, and the tree is used partially and universally. Not suitable for that. Therefore, a technique capable of extracting a partial tree suitable for reuse from a KPI tree is desired.
　本発明の一態様は、情報を示す情報ノードと演算を示す演算ノードとを含むＫＰＩツリーから、部分ツリーを抽出するシステムであって、１以上の演算装置と、１以上の記憶装置と、を含み、前記１以上の記憶装置は、第１ＫＰＩツリーを格納し、前記１以上の演算装置は、前記第１ＫＰＩツリーにおいて、第１下位情報ノードと、前記第１下位情報ノードの上位情報ノードである第１上位情報ノードとを含む、第１部分ツリーを抽出し、前記第１部分ツリーに含まれる演算ノードに基づき、前記第１下位情報ノードの変化による前記第１上位情報ノードの変化の算出に不要なノードを、前記第１部分ツリーから削除する。 One aspect of the present invention is a system for extracting a partial tree from a KPI tree including an information node indicating information and an operation node indicating an operation, wherein one or more arithmetic devices and one or more storage devices are used. The one or more storage devices include the first KPI tree, and the one or more arithmetic devices are a first lower information node and a higher information node of the first lower information node in the first KPI tree. The first partial tree including the first upper information node is extracted, and the change of the first upper information node due to the change of the first lower information node is calculated based on the calculation node included in the first partial tree. Unnecessary nodes are deleted from the first subtree.
　本開示の一態様によれば、ＫＰＩツリーから再利用に適した部分ツリーを抽出できる。 According to one aspect of the present disclosure, a partial tree suitable for reuse can be extracted from the KPI tree.
ＫＰＩツリー作成支援システムのハードウェア構成例を示す図である。It is a figure which shows the hardware configuration example of the KPI tree creation support system. ＫＰＩツリー作成支援システムの機能構成例を示す図である。It is a figure which shows the functional structure example of the KPI tree creation support system. ＫＰＩツリー作成支援システムで作成されるＫＰＩツリー例を示す図である。It is a figure which shows the example of the KPI tree created by the KPI tree creation support system. ノードデータテーブルのデータ構造例を示す図である。It is a figure which shows the data structure example of a node data table. リンクデータテーブルのデータ構造例を示す図である。It is a figure which shows the data structure example of the link data table. ＫＰＩツリー作成支援システムの処理フロー例を示す図である。It is a figure which shows the processing flow example of the KPI tree creation support system. ＫＰＩツリー部品作成・利用の処理フロー例を示す図である。It is a figure which shows the processing flow example of KPI tree component creation and use. ＫＰＩツリー表示部が表示する画面例を示す図である。It is a figure which shows the screen example which the KPI tree display part displays. サブゴール候補リストの情報の表示例を示す。An example of displaying information in the subgoal candidate list is shown. ＫＰＩツリー部品抽出の処理フロー例を示す図である。It is a figure which shows the processing flow example of the KPI tree component extraction. ＫＰＩツリー作成支援方法でＫＰＩツリー部品作成のフローを説明する図である。It is a figure explaining the flow of KPI tree component creation by the KPI tree creation support method. ＫＰＩツリー作成支援方法で作成されるＫＰＩツリー部品の一例である。This is an example of KPI tree parts created by the KPI tree creation support method. 作成されたＫＰＩツリー部品の接続方法を説明する図である。It is a figure explaining the connection method of the created KPI tree component.
　以下、本発明の実施形態について図面を用いて詳細に説明する。便宜上その必要があるときは、複数のセクションまたは実施例に分割して説明するが、特に明示した場合を除き、それらは互いに無関係なものではなく、一方は他方の一部または全部の変形例、詳細、補足説明等の関係にある。また、以下において、要素の数等（個数、数値、量、範囲等を含む）に言及する場合、特に明示した場合及び原理的に明らかに特定の数に限定される場合等を除き、その特定の数に限定されるものではなく、特定の数以上でも以下でもよい。 Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. When necessary for convenience, the description will be divided into multiple sections or examples, but unless otherwise specified, they are not unrelated to each other, one of which is a partial or whole variant of the other. There is a relationship of details, supplementary explanations, etc. In addition, in the following, when the number of elements, etc. (including the number, numerical value, quantity, range, etc.) is referred to, the specification is specified except when explicitly stated or when the number is clearly limited to a specific number in principle. The number is not limited to, and may be more than or less than a specific number.
　本システムは、物理的な計算機システム（一つ以上の物理的な計算機）でもよいし、クラウド基盤のような計算リソース群（複数の計算リソース）上に構築されたシステムでもよい。計算機システムあるいは計算リソース群は、１以上のインタフェース装置（例えば通信装置及び入出力装置を含む）、１以上の記憶装置（例えば、メモリ（主記憶）及び補助記憶装置を含む）及び１以上の演算装置を含む。 This system may be a physical computer system (one or more physical computers) or a system built on a computer resource group (multiple computer resources) such as a cloud platform. A computer system or computational resource group includes one or more interface devices (including, for example, communication devices and input / output devices), one or more storage devices (including, for example, memory (main storage) and auxiliary storage devices), and one or more operations. Includes equipment.
　命令コードを含むプログラムが演算装置によって実行されることで機能が実現される場合、定められた処理が、適宜に記憶装置及び／またはインタフェース装置等を用いながら行われるため、機能は演算装置の少なくとも一部とされてもよい。機能を主語として説明された処理は、演算装置あるいはその演算装置を有するシステムが行う処理としてもよい。プログラムは、プログラムソースからインストールされてもよい。プログラムソースは、例えば、プログラム配布計算機または計算機が読み取り可能な記憶媒体（例えば計算機読み取り可能な非一過性記憶媒体）であってもよい。各機能の説明は一例であり、複数の機能が一つの機能にまとめられたり、一つの機能が複数の機能に分割されたりしてもよい。 When the function is realized by executing the program including the instruction code by the arithmetic unit, the function is at least the arithmetic unit because the defined processing is appropriately performed by using the storage device and / or the interface device and the like. It may be a part. The process described with the function as the subject may be a process performed by an arithmetic unit or a system having the arithmetic unit. The program may be installed from the program source. The program source may be, for example, a program distribution computer or a computer-readable storage medium (eg, a computer-readable non-transient storage medium). The description of each function is an example, and a plurality of functions may be combined into one function, or one function may be divided into a plurality of functions.
　以下において、ユーザがＫＰＩツリーを作成することを支援する支援装置の例を説明する。支援装置は、過去のＫＰＩツリーから部分ツリーを抽出し、ユーザに提供する。過去に作成したＫＰＩツリーを自動的に部品化して再利用可能とすることで、ＫＰＩツリー作成経験の少ない者でも容易にＫＰＩツリーを作成できる。 Below, an example of a support device that assists the user in creating a KPI tree will be described. The support device extracts a partial tree from the past KPI tree and provides it to the user. By automatically converting the KPI tree created in the past into parts and making it reusable, even a person with little experience in creating a KPI tree can easily create a KPI tree.
　ＫＰＩツリーは、情報ノード、演算ノード及びノード間をつなぐリンクを含む。演算ノードは、所定の演算を行うノードであり、任意関数を表すことができる。情報ノードは演算を行うことなく、特定の情報を示すノードである。支援装置は、部分ツリーの抽出において、特定の下位の情報ノードの変化による特定の上位ノードの変化の計算に不要なノードを削除してもよい。これにより、抽出する部分ツリーを簡略化して部品としての適性を高めることができる。 The KPI tree includes information nodes, arithmetic nodes, and links connecting the nodes. The arithmetic node is a node that performs a predetermined arithmetic and can represent an arbitrary function. An information node is a node that shows specific information without performing an operation. In extracting the subtree, the support device may delete nodes that are not necessary for calculating the change of the specific upper node due to the change of the specific lower information node. As a result, the partial tree to be extracted can be simplified and the suitability as a component can be improved.
　図１は、本実施形態のＫＰＩツリー作成支援システム１０のハードウェア構成例を示す図である。ＫＰＩツリー作成支援システム１０は、主記憶装置１０１、補助記憶装置１０２、入力装置１０３、出力装置１０４、演算装置１０５、及び通信装置１０６を含む。これら構成要素は、バス１０７で相互に通信可能に接続されている。 FIG. 1 is a diagram showing a hardware configuration example of the KPI tree creation support system 10 of the present embodiment. The KPI tree creation support system 10 includes a main storage device 101, an auxiliary storage device 102, an input device 103, an output device 104, an arithmetic unit 105, and a communication device 106. These components are communicably connected to each other by bus 107.
　主記憶装置１０１は、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）など揮発性記憶素子で構成することができる。補助記憶装置１０２は、ＳＳＤ（Ｓｏｌｉｄ　Ｓｔａｔｅ　Ｄｒｉｖｅ）やハードディスクドライブなど不揮発性記憶素子で構成することができる。入力装置１０３は、ユーザからの入力動作を受け付けるキーボード、マウス、タッチパネル等であり、出力装置１０４は、ユーザに対して処理結果を表示する。 The main storage device 101 can be composed of a volatile storage element such as a RAM (Random Access Memory). The auxiliary storage device 102 can be composed of a non-volatile storage element such as an SSD (Solid State Drive) or a hard disk drive. The input device 103 is a keyboard, mouse, touch panel, etc. that accepts an input operation from the user, and the output device 104 displays the processing result to the user.
　演算装置１０５、例えば、ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）であり、補助記憶装置１０２や外部装置から主記憶装置１０１にロードしたプログラムを実行し、装置自体の統括制御を行うとともに、各種判定、演算および制御処理を行う。通信装置１０６は、ネットワークに接続しデータをやり取りするための装置である。 The arithmetic unit 105, for example, a CPU (Central Processing Unit), executes a program loaded from the auxiliary storage device 102 or an external device into the main storage device 101, performs integrated control of the device itself, and performs various determinations, calculations, and controls. Perform processing. The communication device 106 is a device for connecting to a network and exchanging data.
　ＫＰＩツリー作成支援システム１０の上記構成要素の一部は省略されてもよく、例えば、ＫＰＩツリー作成支援システム１０がスタンドアロンマシンとして稼働する場合、通信装置１０６は省略してもよい。ＫＰＩツリー作成支援システム１０は、１以上の記憶装置及び１以上の演算装置を含む、一つの計算機又は互いに通信を行う複数の計算機で構成することができる。 Some of the above components of the KPI tree creation support system 10 may be omitted. For example, when the KPI tree creation support system 10 operates as a stand-alone machine, the communication device 106 may be omitted. The KPI tree creation support system 10 can be composed of one computer or a plurality of computers communicating with each other, including one or more storage devices and one or more arithmetic units.
　図２は、ＫＰＩツリー作成支援システム１０がプログラムを実行することで実装する機能構成の一例を示す。本実施形態のＫＰＩツリー作成支援システム１０は、表示部２１０、入力操作部２２０、情報処理部２３０、及び過去ＫＰＩツリーデータベース２４０、を含む。情報処理部２３０は、作成ＫＰＩツリー格納部２３１、過去ＫＰＩツリー処理部２３４、ノード検索部２３８、及びサブゴール候補検索部２３９を含む。 FIG. 2 shows an example of a functional configuration implemented by the KPI tree creation support system 10 by executing a program. The KPI tree creation support system 10 of the present embodiment includes a display unit 210, an input operation unit 220, an information processing unit 230, and a past KPI tree database 240. The information processing unit 230 includes a creation KPI tree storage unit 231, a past KPI tree processing unit 234, a node search unit 238, and a subgoal candidate search unit 239.
　表示部２１０は、後述する作成ＫＰＩツリー格納部２３１及びサブゴール候補検索部２３９等からを受け取り、出力装置１０４にＫＰＩツリーを表示する。図３は、出力装置１０４に表示される完成されたＫＰＩツリーの一例を示す。 The display unit 210 receives from the created KPI tree storage unit 231 and the subgoal candidate search unit 239, which will be described later, and displays the KPI tree on the output device 104. FIG. 3 shows an example of a completed KPI tree displayed on the output device 104.
　表示部２１０は、頂点ノードにＫＧＩ３０１が配置され、その頂点ノードにつながるノード３０２～３１３が配置されたＫＰＩツリー３を表示する。ここで、ノード３０２のように名称が「－」のような演算で記載されているものを演算ノード、ノード３０３のように名称が演算でなく情報で記載されいてるノードを情報ノードと呼ぶこととする。演算ノードは、名称が示す演算を行うノードであり、情報ノードは、名称が特定する情報を示すノードである。情報ノードの名称は情報識別子の例である。 The display unit 210 displays the KPI tree 3 in which the KGI 301 is arranged in the vertex node and the nodes 302 to 313 connected to the vertex node are arranged. Here, a node whose name is described by an operation such as "-" such as a node 302 is called an operation node, and a node whose name is described by information rather than an operation such as a node 303 is called an information node. do. The calculation node is a node that performs the operation indicated by the name, and the information node is a node that indicates the information specified by the name. The name of the information node is an example of an information identifier.
　図３において、矢印で表されているものをリンクと呼ぶ。リンクは方向を持っており、リンクで接続される二つのノードのうち、始点にあるノードを子ノード、先にあるノードを親ノードという。例えば、ノード３０１はリンク４１４においてノード３０２の親ノードであると。さらに、ノード３０３のように、親ノードにも子ノードにもなるノードを中間ノード、ノード３０７やノード３０８のように子ノードを持たないノードを葉ノードという。葉ノードおよびＫＧＩノードは情報ノードであり、情報ノードの親ノードおよび子ノードは演算ノードである。演算ノードの親ノード及び子ノードは、情報ノードである。 In FIG. 3, what is represented by an arrow is called a link. The link has a direction, and of the two nodes connected by the link, the node at the start point is called the child node, and the node at the end is called the parent node. For example, node 301 is the parent node of node 302 at link 414. Further, a node that can be both a parent node and a child node, such as node 303, is called an intermediate node, and a node that does not have a child node, such as node 307 and node 308, is called a leaf node. The leaf node and the KGI node are information nodes, and the parent node and child node of the information node are arithmetic nodes. The parent node and child node of the arithmetic node are information nodes.
　また、ノード３１１のように点線で示されるノードを同期ノードという。同期ノードは、そのノード以外のあるノードを参照し、同じ情報を示す。例えば、ノード３１１はノード３０５と同じ情報を参照しており、同じ名称「Ｃ」を表示している。同期ノードは葉ノードである。 Also, a node shown by a dotted line like node 311 is called a synchronization node. A sync node refers to a node other than that node and shows the same information. For example, node 311 refers to the same information as node 305 and displays the same name "C". The synchronization node is a leaf node.
　あるＫＰＩツリーを構成するノード及びリンクのみを用いたツリーをＫＰＩツリーの部分ツリーと呼ぶこととする。例えばノード３０１、３０２、３０３、３１３とリンク３１４、３１５、３１６で構成されたＫＰＩツリー３１は、ＫＰＩツリー３の部分ツリーである。 A tree that uses only the nodes and links that make up a certain KPI tree is called a partial tree of the KPI tree. For example, the KPI tree 31 composed of the nodes 301, 302, 303, 313 and the links 314, 315, 316 is a partial tree of the KPI tree 3.
　ＫＰＩツリー３の頂点側を上位、ＫＰＩツリーの末端側を下位と呼ぶこととする。また、二つのノードの関係が上位ノードと下位ノードであることは、その下位ノードはその上位ノードの子孫ノードである。図３は、ＫＧＩ３０１を頂点として左から右に分解した要素を配置しているが、ＫＰＩツリーの構成要素のレイアウトは任意である。構成要素は、例えば、上から下に配置してもよい。 The top side of the KPI tree 3 is called the upper side, and the end side of the KPI tree is called the lower side. Further, the fact that the relationship between the two nodes is a higher node and a lower node means that the lower node is a descendant node of the upper node. In FIG. 3, the elements decomposed from left to right with the KGI 301 as the apex are arranged, but the layout of the components of the KPI tree is arbitrary. The components may be arranged, for example, from top to bottom.
　図２に戻って、入力操作部２２０は入力装置１０３を介して受けた操作に従って、ノード検索部２３８及び部分ＫＰＩツリー抽出部２３５に情報を提供する。具体的な情報の提供内容は後述する。
Returning to FIG. 2, the input operation unit 220 provides information to the node search unit 238 and the partial KPI tree extraction unit 235 according to the operation received via the input device 103. The specific content of the information provided will be described later.
　情報処理部２３０の作成ＫＰＩツリー格納部２３１は、ユーザが作成しているＫＰＩツリーのデータを格納する。作成ＫＰＩツリー格納部２３１は、作成ＫＰＩツリーノードデータ格納部２３２及び作成ＫＰＩツリーリンクデータ格納部２３３を含む。作成ＫＰＩツリーノードデータ格納部２３２は、ユーザが作成しているＫＰＩツリーのノードデータを格納している。作成ＫＰＩツリーリンクデータ格納部２３３は、ユーザが作成しているＫＰＩツリーのリンクデータを格納している。 The creation KPI tree storage unit 231 of the information processing unit 230 stores the data of the KPI tree created by the user. The creation KPI tree storage unit 231 includes a creation KPI tree node data storage unit 232 and a creation KPI tree link data storage unit 233. The created KPI tree node data storage unit 232 stores the node data of the KPI tree created by the user. The created KPI tree link data storage unit 233 stores the link data of the KPI tree created by the user.
　過去ＫＰＩツリー処理部２３４は、部分ＫＰＩツリー抽出部２３５、ノードデータ一時格納部２３６、リンクデータ一時格納部２３７、を含む。部分ＫＰＩツリー抽出部２３５は、過去ＫＰＩツリーデータベース２４０に格納されている過去ＫＰＩツリーから、ＫＰＩツリー部品として利用できる部分ツリーを抽出すする。部分ツリーの抽出において、部分ＫＰＩツリー抽出部２３５は、不必要なノードおよびリンクを剪定する。具体的な処理フローは後述する。 The past KPI tree processing unit 234 includes a partial KPI tree extraction unit 235, a node data temporary storage unit 236, and a link data temporary storage unit 237. The partial KPI tree extraction unit 235 extracts a partial tree that can be used as a KPI tree component from the past KPI tree stored in the past KPI tree database 240. In the extraction of the partial tree, the partial KPI tree extraction unit 235 prunes unnecessary nodes and links. The specific processing flow will be described later.
　ノードデータ一時格納部２３６は、部分ＫＰＩツリー抽出部２３５で処理する対象のＫＰＩツリーのノードデータを格納する。リンクデータ一時格納部２３７は、部分ＫＰＩツリー抽出部２３５で処理する対象のＫＰＩツリーのリンクデータを格納する。 The node data temporary storage unit 236 stores the node data of the target KPI tree to be processed by the partial KPI tree extraction unit 235. The link data temporary storage unit 237 stores the link data of the target KPI tree to be processed by the partial KPI tree extraction unit 235.
　ノード検索部２３８は、入力操作部２２０からノードデータを得て、過去ノードデータ格納部２４１でそのノードデータが示すノードを検索する。サブゴール候補検索部２３９は、ノード検索部２３８が取得した過去ノードデータ格納部２４１内の適合ノードデータと過去リンクデータ格納部２４２のリンクデータを基にサブゴール候補を検索する。サブゴールの詳細は後述する。 The node search unit 238 obtains node data from the input operation unit 220 and searches the past node data storage unit 241 for the node indicated by the node data. The sub-goal candidate search unit 239 searches for sub-goal candidates based on the matching node data in the past node data storage unit 241 acquired by the node search unit 238 and the link data of the past link data storage unit 242. Details of the subgoals will be described later.
　過去ＫＰＩツリーデータベース２４０は、過去ノードデータ格納部２４１、過去リンクデータ格納部２４２、を含む。過去ノードデータ格納部２４１は、過去に作成されたＫＰＩツリーのデータのうち、ノードに関するデータを格納する。過去リンクデータ格納部２４２は、過去に作成されたＫＰＩツリーのデータのうち、リンクに関するデータを格納する。 The past KPI tree database 240 includes a past node data storage unit 241 and a past link data storage unit 242. The past node data storage unit 241 stores the data related to the node among the data of the KPI tree created in the past. The past link data storage unit 242 stores the data related to the link among the data of the KPI tree created in the past.
　各機能部は、演算装置１０５、主記憶装置１０１及び／又は補助記憶装置１０２により実現できる。格納部は、主記憶装置１０１及び／又は補助記憶装置１０２により実装し、他の機能部を演算装置１０５により実装することができる。 Each functional unit can be realized by the arithmetic unit 105, the main storage device 101 and / or the auxiliary storage device 102. The storage unit can be mounted by the main storage device 101 and / or the auxiliary storage device 102, and other functional units can be mounted by the arithmetic unit 105.
　本実施形態のＫＰＩツリー作成支援システム１０の格納部が格納している情報について説明する。図４は作成ＫＰＩツリーノードデータ格納部２３２、ノードデータ一時格納部２３６、過去ノードデータ格納部２４１で使用されるノードデータテーブル構造の例を示している。これら格納部は、ＫＰＩツリーのノードの情報を格納し、本実施形態において、同一構成のテーブルを格納している。 The information stored in the storage unit of the KPI tree creation support system 10 of the present embodiment will be described. FIG. 4 shows an example of a node data table structure used in the created KPI tree node data storage unit 232, node data temporary storage unit 236, and past node data storage unit 241. These storage units store information on the nodes of the KPI tree, and store tables having the same configuration in the present embodiment.
　図４に例示するテーブル構造は、ツリーＩＤ欄４０１、ノードＩＤ欄４０２、カテゴリ欄４０３、テキスト欄４０４、同期元欄４０５、同期先欄４０６、属性欄４０７、及びチェック欄４０８を含む。各レコードは、ＫＰＩツリー内の一つのノードの情報を示す。 The table structure illustrated in FIG. 4 includes a tree ID column 401, a node ID column 402, a category column 403, a text column 404, a synchronization source column 405, a synchronization destination column 406, an attribute column 407, and a check column 408. Each record shows information about one node in the KPI tree.
　ツリーＩＤ欄４０１は、ＫＰＩツリーを識別するＩＤを示す。ノードＩＤ欄４０２は、ツリーＩＤ欄４０１が同定するツリーを構成するノードを識別するＩＤを示す。カテゴリ欄４０３は、ノードが演算ノード「Ｏｐ」か情報ノード「Ｉｎｆｏ」かを示す。テキスト欄４０４は、ノードの具体的な名称を示す。同期元欄４０５は、あるノードが同期ノードである場合、参照先のノードのノードＩＤを示す。同期先欄４０６は、あるノードがある同期ノードの参照を受けている場合、参照元のノードＩＤを示す。 The tree ID column 401 indicates an ID that identifies the KPI tree. The node ID column 402 indicates an ID that identifies the nodes that make up the tree identified by the tree ID column 401. The category column 403 indicates whether the node is the arithmetic node "Op" or the information node "Info". The text field 404 indicates a specific name of the node. The synchronization source column 405 indicates the node ID of the referenced node when a certain node is a synchronization node. The synchronization destination column 406 indicates the node ID of the reference source when a certain node is referenced by a certain synchronization node.
　属性欄４０７は、あるノードがサブゴールであるか否かを示す。チェック欄４０８は、後述するように、部分ＫＰＩツリー抽出部２３５に使用される。属性欄４０７に登録されるサブゴールは、ＫＰＩツリーの中間ノードとして重要と判断されているノードであり、例えば、ＫＰＩツリーのデータの格納時に、その作成者によって入力される。例えば、金額で測れないノードから計算される財務ノードが、サブゴールと定義されてもよい。サブゴール属性が過去ＫＰＩツリーのデータで設定されていることで、部品として有用な部分ツリーを抽出可能となる。チェック４０８の具体的な使用方法は、過去ＫＰＩツリー処理部３３４の処理フローとともに後述する。 The attribute column 407 indicates whether or not a node is a subgoal. The check column 408 is used for the partial KPI tree extraction unit 235, as will be described later. The subgoal registered in the attribute field 407 is a node determined to be important as an intermediate node of the KPI tree, and is input by the creator when storing the data of the KPI tree, for example. For example, a finance node calculated from a node that cannot be measured by monetary amount may be defined as a subgoal. By setting the subgoal attribute with the data of the past KPI tree, it is possible to extract a partial tree useful as a part. The specific usage of the check 408 will be described later together with the processing flow of the past KPI tree processing unit 334.
　例えば、図４のテーブルが、図３に示すＫＰＩツリーのノードの情報を示す場合、レコード４０９は、図３に示したＫＰＩツリー３のノード３０１に関するデータである。レコード４１０は、ＫＰＩツリー３のノード３１１に関するデータである。レコード４１０は、同期元欄４０５にデータを格納しており、ノード３１１は、同期ノードである。ノードＩＤ５のテキスト情報が、レコード４１０のテキスト欄４０４の部分で引用されている。この場合、出力装置１０４（表示部２１０）は、ノードＩＤが１１のノードを「Ｃ」と表示する。 For example, when the table of FIG. 4 shows the information of the node of the KPI tree shown in FIG. 3, the record 409 is the data related to the node 301 of the KPI tree 3 shown in FIG. Record 410 is data about node 311 of the KPI tree 3. The record 410 stores data in the synchronization source column 405, and the node 311 is a synchronization node. The text information of the node ID 5 is quoted in the text field 404 of the record 410. In this case, the output device 104 (display unit 210) displays the node having the node ID 11 as “C”.
　図５は作成ＫＰＩツリーリンクデータ格納部２３３、リンクデータ一時格納部２３７、及び過去リンクデータ格納部２４７で使用されるリンクデータテーブル構造の例を示している。 FIG. 5 shows an example of the link data table structure used in the created KPI tree link data storage unit 233, the link data temporary storage unit 237, and the past link data storage unit 247.
　図５に例示するテーブル構造例は、ツリーＩＤ欄５０１、リンクＩＤ欄５０２、親ノードＩＤ欄５０３、子ノードＩＤ欄５０４、及びチェック欄５０５を含む。ツリーＩＤ欄５０１は、ＫＰＩツリーを識別するＩＤを示す。リンクＩＤ欄５０２は、ツリーＩＤ欄５０１が同定するＫＰＩツリーのリンクを識別するＩＤを示す。 The table structure example illustrated in FIG. 5 includes a tree ID column 501, a link ID column 502, a parent node ID column 503, a child node ID column 504, and a check column 505. The tree ID column 501 indicates an ID that identifies the KPI tree. The link ID column 502 indicates an ID that identifies the link of the KPI tree identified by the tree ID column 501.
　親ノードＩＤ欄５０３は、リンクがつなぐ二つのノードのうち親ノードを識別するＩＤを示す。子ノードＩＤ欄５０４は、リンクがつなぐ二つのノードのうち子ノードを識別するＩＤを示す。チェック欄５０５は、後述するように、部分ＫＰＩツリー抽出部２３５に使用される。チェック４０８の具体的な使用方法は、過去ＫＰＩツリー処理部３３４の処理フローとともに後述する。 The parent node ID column 503 indicates an ID that identifies the parent node among the two nodes connected by the link. The child node ID column 504 indicates an ID that identifies the child node among the two nodes connected by the link. The check box 505 is used for the partial KPI tree extraction unit 235, as will be described later. The specific usage of the check 408 will be described later together with the processing flow of the past KPI tree processing unit 334.
　例えば、図５のテーブルが、図３に示すＫＰＩツリーのリンクの情報を示す場合、レコード５０６は、図３に示したＫＰＩツリー３のリンク３１４に関するデータである。 For example, when the table of FIG. 5 shows the information of the link of the KPI tree shown in FIG. 3, the record 506 is the data related to the link 314 of the KPI tree 3 shown in FIG.
　以下、本実施形態におけるＫＰＩツリー作成支援方法の実施手順を、図を参照して説明する。以下で説明するＫＰＩツリー作成支援方法に対応する各種動作は、ＫＰＩツリー作成支援システム１０が実行するプログラムによって実現される。そして、これらのプログラムは、以下に説明される各種の動作を行うためのコードから構成される。 Hereinafter, the implementation procedure of the KPI tree creation support method in the present embodiment will be described with reference to the figure. Various operations corresponding to the KPI tree creation support method described below are realized by the program executed by the KPI tree creation support system 10. Then, these programs are composed of codes for performing various operations described below.
　図６は、ＫＰＩツリー作成支援方法の処理フロー例を示す。まず、ユーザは入力装置１０３を用いて、ＫＰＩの情報とＫＧＩの情報を入力する。ユーザは、さらに、ＫＧＩの子ノードを含む下位ノード及びノード間のリンクも入力することもできる。後述する図８Ａは、ユーザ入力の例を示す。ユーザ入力により、ＫＰＩツリー作成支援システム１０は、ユーザが望むＫＰＩツリー作成条件を得ることができる。 FIG. 6 shows an example of the processing flow of the KPI tree creation support method. First, the user inputs KPI information and KGI information using the input device 103. The user can also enter subordinate nodes including KGI child nodes and links between the nodes. FIG. 8A, which will be described later, shows an example of user input. By user input, the KPI tree creation support system 10 can obtain the KPI tree creation condition desired by the user.
　上述のように、入力操作部２２０は、ＫＧＩ、ＫＰＩ及びＫＧＩの計算方法についての情報を取得し、それをノードデータ及びリンクデータに分類する。入力操作部２２０は、ノードデータを作成ＫＰＩツリーノードデータ格納部２３２に格納し、リンクデータを作成ＫＰＩツリーリンクデータ格納部２３３に格納する。（Ｓ１）。 As described above, the input operation unit 220 acquires information about the calculation method of KGI, KPI and KGI, and classifies it into node data and link data. The input operation unit 220 stores the node data in the creation KPI tree node data storage unit 232 and stores the link data in the creation KPI tree link data storage unit 233. (S1).
　次に、ＫＰＩツリー作成支援システム１０は、過去に作成されたＫＰＩツリーから部品を作成してユーザに提示することを繰り返すことで、ユーザのＫＰＩツリー作成を支援する（Ｓ２）。 Next, the KPI tree creation support system 10 supports the user's KPI tree creation by repeating creating parts from the KPI tree created in the past and presenting them to the user (S2).
　図７はステップＳ２のより詳細なフローを示す。まず、表示部２１０は、作成ＫＰＩツリー格納部２３１に格納されたデータを出力装置１０４に出力する（Ｓ２０１）。図８Ａは、表示部２１０が、ステップＳ１で取得されたユーザ入力から生成して出力装置１０４で表示する画像例を示す。図８Ａは、表示部２１０によるユーザ入力の画像を示すが、ユーザは、同様のＧＵＩによって、ユーザデータを入力してもよい。 FIG. 7 shows a more detailed flow of step S2. First, the display unit 210 outputs the data stored in the created KPI tree storage unit 231 to the output device 104 (S201). FIG. 8A shows an example of an image generated by the display unit 210 from the user input acquired in step S1 and displayed by the output device 104. FIG. 8A shows an image of user input by the display unit 210, but the user may input user data by the same GUI.
　ここでは、ＫＧＩとして「利益」、ＫＰＩとして「製造能力」と「計画時間」が入力されている。さらに、ＫＧＩの子ノードとして「利益＝売上－費用」を意味する情報が、さらに下位のノードとして「費用＝製造原価＋販管費」を意味する情報が入力されている。 Here, "profit" is input as KGI, and "manufacturing capacity" and "planned time" are input as KPI. Further, information meaning "profit = sales-expense" is input as a child node of KGI, and information meaning "cost = manufacturing cost + SG & A cost" is input as a lower node.
　図７に戻って、次に、ユーザは、入力されたノードのうち、ＫＧＩノード以外の親ノードを持たない情報ノードを、入力装置１０３を用いて選択することができる。例えば、図８Ａにおいてはノード８０２及び８０３が選択可能である。入力操作部２２０は、選択されたノードのＩＤを受け取る（Ｓ２０２）。入力操作部２２０は、選択されたノードの情報を作成ＫＰＩツリーノードデータ格納部２３２から取得し、ノード検索部２３８に渡す。以下の説明ではユーザにノード８０２が選択された場合のふるまいを例示する。 Returning to FIG. 7, the user can then select, among the input nodes, an information node having no parent node other than the KGI node by using the input device 103. For example, in FIG. 8A, nodes 802 and 803 can be selected. The input operation unit 220 receives the ID of the selected node (S202). The input operation unit 220 acquires the information of the selected node from the creation KPI tree node data storage unit 232 and passes it to the node search unit 238. The following description exemplifies the behavior when the node 802 is selected by the user.
　入力操作部２２０から選択されたノードの情報を受けたノード検索部２３８は、過去ノードデータ格納部２４１において、選択されたノードの名称（テキスト欄４０４）と一致する名称のノードを検索する（Ｓ２０３）。 The node search unit 238 that has received the information of the node selected from the input operation unit 220 searches the past node data storage unit 241 for a node having a name that matches the name of the selected node (text field 404) (S203). ).
　選択されたノードと同名のノードが過去ノードに存在しない場合（Ｓ２０４：ＮＯ）、ユーザが選択ノードの親ノード及びリンクを入力し、ＫＰＩツリーを作成する（Ｓ２１５）。具体的には、入力操作部２２０は、ユーザによって入力装置１０３を介して入力された情報を取得し、作成ＫＰＩツリー格納部２３１に格納する。 If a node with the same name as the selected node does not exist in the past node (S204: NO), the user inputs the parent node and link of the selected node and creates a KPI tree (S215). Specifically, the input operation unit 220 acquires the information input by the user via the input device 103 and stores it in the creation KPI tree storage unit 231.
　過去ノードデータ格納部２４１に、選択ノードと同名のノードが存在する場合（Ｓ２０４：ＹＥＳ）、ノード検索部２３８は、そのノードのレコードのツリーＩＤ欄４０１のフィールドからツリーＩＤを取得し、サブゴール候補検索部２３９に送信する。過去ノードデータ格納部２４１が選択ノードと同名の複数のノードを格納している場合、ノード検索部２３８は、過去ノードデータ格納部２４１のツリーＩＤ欄４０１及びノードＩＤ欄４０２が示すＩＤのセットのリストを、送信する。選択ノードと同名のノードは、異なる過去ＫＰＩツリーに存在することがあり、又、選択ノードと同名の複数ノードが、一つの過去ＫＰＩツリーに存在し得る。 When a node having the same name as the selected node exists in the past node data storage unit 241 (S204: YES), the node search unit 238 acquires a tree ID from the field of the tree ID column 401 of the record of that node, and is a subgoal candidate. It is transmitted to the search unit 239. When the past node data storage unit 241 stores a plurality of nodes having the same name as the selected node, the node search unit 238 sets the IDs indicated by the tree ID column 401 and the node ID column 402 of the past node data storage unit 241. Send the list. A node having the same name as the selected node may exist in a different past KPI tree, and a plurality of nodes having the same name as the selected node may exist in one past KPI tree.
　ツリーＩＤを受けたサブゴール候補検索部２３９は、過去ＫＰＩツリーにおいて、選択されたノードから最も少ないリンクでたどり着けるサブゴール属性のノード（最も近いサブゴールノード）を検索する。選択されたノードと検索により得られたサブゴールノードとの間には、他のサブゴールノードは存在しない。このように、予め指定された名称のサブゴールノードにおいて選択ノードに最も近いサブゴールノードを選択することで、より部品に適した部分ツリーを抽出できる。図４を参照して説明したように、過去ノードデータ格納部２４１は、属性欄４０７において、サブゴール属性を有するノードを示す。なお、設計によっては、他のサブゴールノードが選択されてもよい。 The sub-goal candidate search unit 239 that received the tree ID searches for a node with a sub-goal attribute (closest sub-goal node) that can be reached from the selected node with the fewest links in the past KPI tree. There are no other subgoal nodes between the selected node and the subgoal node obtained by the search. In this way, by selecting the subgoal node closest to the selected node among the subgoal nodes with the names specified in advance, the partial tree more suitable for the part can be extracted. As described with reference to FIG. 4, the past node data storage unit 241 indicates a node having a subgoal attribute in the attribute column 407. Depending on the design, other subgoal nodes may be selected.
　サブゴール候補検索部２３９は、さらに、過去ＫＰＩツリーとステップＳ１でユーザにより入力された作成しているＫＰＩツリーの情報との類似度ＫＳを計算する。例えば、類似度ＫＳは以下のように計算できる。 The subgoal candidate search unit 239 further calculates the similarity KS between the past KPI tree and the information of the created KPI tree input by the user in step S1. For example, the similarity KS can be calculated as follows.
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
　ｎｉはステップＳ１で入力されたＫＰＩツリーのｉ番目の葉ノードのテキスト欄４０４にある文字列である。ｋはステップＳ１で入力されたＫＰＩツリーの葉ノードの数である。ｍｊは過去ＫＰＩツリーのｊ番目のノードのテキスト欄４０４にある文字列である。ｌは、過去ＫＰＩツリーのノード数である。ｄｉｓｔａｎｃｅは、最小編集距離のような、文字列と文字列の距離を計算する関数を指す。なお、ｄｉｓｔａｎｃｅで求められる値は実数であり、数値が大きければ大きいほど、文字列同士の距離があるものとする。例えば、図８Ａの入力例において、ｎ１は「売上」、ｋは３である。 Ni is a character string in the text field 404 of the i-th leaf node of the KPI tree input in step S1. k is the number of leaf nodes of the KPI tree input in step S1. mj is a character string in the text field 404 of the jth node of the past KPI tree. l is the number of nodes in the past KPI tree. Distance refers to a function that calculates the distance between strings, such as the minimum edit distance. It should be noted that the value obtained by distance is a real number, and the larger the numerical value, the greater the distance between the character strings. For example, in the input example of FIG. 8A, n1 is "sales" and k is 3.
　サブゴール候補検索部２３９は、類似度ＫＳが高い順にサブゴールをランク付けし、選択ノードＩＤ、サブゴールＩＤ、そのサブゴールが含まれる過去ＫＰＩツリーのツリーＩＤ及びランクのセットをリスト化する。 The sub-goal candidate search unit 239 ranks the sub-goals in descending order of similarity KS, and lists the selected node ID, the sub-goal ID, and the tree ID and rank set of the past KPI tree including the sub-goal.
　ステップＳ２０３において、ノード検索部２３８が同じ過去ＫＰＩツリーから、選択ノードと同名でノードＩＤの異なる複数のノードを見つけている場合、サブゴール候補検索部２３９は、その過去ＫＰＩツリーのサブゴールと選択ノードのペアの中でさらにランク付けを行う。 In step S203, when the node search unit 238 finds a plurality of nodes having the same name as the selected node but different node IDs from the same past KPI tree, the subgoal candidate search unit 239 finds the subgoal of the past KPI tree and the selected node. Further rank in the pair.
　具体的には、サブゴール候補検索部２３９は、サブゴールノードと選択ノードまでに必要なリンク数に基づいてランクを決定する。必要リンク数が小さいほどランクが高くなる。異なるＩＤの選択ノードが同一ＩＤのサブゴールノードとペアを構成する場合、サブゴール候補検索部２３９は、リンク数が多いペアを削除してもよい。サブゴール候補検索部２３９は、作成されたサブゴール候補リストを表示部２１０に送る（Ｓ２０５）。 Specifically, the subgoal candidate search unit 239 determines the rank based on the number of links required to reach the subgoal node and the selected node. The smaller the number of required links, the higher the rank. When the selection nodes having different IDs form a pair with the subgoal node having the same ID, the subgoal candidate search unit 239 may delete the pair having a large number of links. The sub-goal candidate search unit 239 sends the created sub-goal candidate list to the display unit 210 (S205).
　表示部２１０は、サブゴール候補リストを受け取り、出力装置１０４にサブゴール候補リストの情報を出力する（Ｓ２０６）。図８Ｂは、サブゴール候補リストの情報の表示例を示す。リスト８５は、ＫＧＩの名称（「顧客満足」）と共に、ステップＳ２０５で決定されたサブゴールノードのランクに従った順序で、サブゴールノードの名称を示す。異なるＫＰＩツリーの同一名称のサブゴールノードは、名称の直後の数字で識別され、同一ＫＰＩノードの同一名称の異なるノードは、ハイフンの後の数字で識別されている。 The display unit 210 receives the subgoal candidate list and outputs the information of the subgoal candidate list to the output device 104 (S206). FIG. 8B shows an example of displaying information in the subgoal candidate list. Listing 85 shows the names of the subgoal nodes, along with the names of the KGIs (“customer satisfaction”), in order according to the rank of the subgoal nodes determined in step S205. Subgoal nodes with the same name in different KPI trees are identified by the number immediately following the name, and different nodes with the same name in the same KPI node are identified by the number after the hyphen.
　ユーザはリスト８５から一つ以上のサブゴール候補を選択し、入力装置１０３を用いて入力する。入力操作部２２０は、入力された情報を受け取り、サブゴール候補リストと共に過去ＫＰＩツリー処理部２３４に送る（Ｓ２０７）。このように、表示部２１０は、サブゴール候補のランクとユーザに提示すると共に、入力操作部２２０はユーザからの選択を受け付ける。これにより、ユーザにとってより適切なサブゴール候補を選択できる。 The user selects one or more subgoal candidates from the list 85 and inputs them using the input device 103. The input operation unit 220 receives the input information and sends it to the past KPI tree processing unit 234 together with the subgoal candidate list (S207). In this way, the display unit 210 presents the rank of the subgoal candidate and the user, and the input operation unit 220 accepts the selection from the user. This makes it possible to select a subgoal candidate that is more appropriate for the user.
　サブゴール候補情報を受け取った過去ＫＰＩツリー処理部２３４は、過去ＫＰＩツリーデータベース２４０から、ユーザに選択されたサブゴールノードを含むＫＰＩツリーＩＤをもつノードデータとリンクデータを取得し、ノードデータをノードデータ一時格納部２３６に、リンクデータをリンクデータ一時格納部２３７に格納する（Ｓ２０８）。 The past KPI tree processing unit 234 that received the subgoal candidate information acquires the node data and the link data having the KPI tree ID including the subgoal node selected by the user from the past KPI tree database 240, and temporarily converts the node data into node data. The link data is stored in the storage unit 236 in the link data temporary storage unit 237 (S208).
　例えば、ステップＳ２０５で図８Ａのような入力を基に「（サブゴールノード名称、ＫＰＩツリーＩＤ）：（生産量、７）、（製造原価、２）、（固定資産、９）」を含むサブゴールリストが作成されているとする。また、ステップＳ２０７で「製造原価」というサブゴールが選択されたとする。過去ＫＰＩツリー処理部２３４は、ＫＰＩツリーＩＤが２の過去ＫＰＩツリーのノードデータとリンクのデータとを格納する。 For example, in step S205, a subgoal list including "(subgoal node name, KPI tree ID): (production amount, 7), (manufacturing cost, 2), (fixed asset, 9)" based on the input as shown in FIG. 8A. Is created. Further, it is assumed that the subgoal "manufacturing cost" is selected in step S207. The past KPI tree processing unit 234 stores the node data and the link data of the past KPI tree having the KPI tree ID of 2.
　その後、部分ＫＰＩツリー抽出部２３５は、ノードデータ一時格納部２３６と、リンクデータ一時格納部２３７から、ステップＳ２０２でユーザに選択されたノードからサブゴールノードまでの経路を含む部分ツリーから、演算ノードと同期ノードの性質を利用して必要な部分を抽出する（Ｓ２０９）。 After that, the partial KPI tree extraction unit 235 uses the node data temporary storage unit 236 and the link data temporary storage unit 237 to form a calculation node from the partial tree including the route from the node selected by the user in step S202 to the subgoal node. The necessary part is extracted by utilizing the property of the synchronization node (S209).
　図９はステップＳ２０９での抽出の処理フローの例を示す。また、図１０は図９に示す処理フロー例の動作を説明するための図である。図１０はステップＳ２０８で、ノードデータ一時格納部２３６とリンクデータ一時格納部２３７に格納された過去ＫＰＩツリーから抜粋した下位部分を示す。 FIG. 9 shows an example of the extraction processing flow in step S209. Further, FIG. 10 is a diagram for explaining the operation of the processing flow example shown in FIG. FIG. 10 shows a lower portion extracted from the past KPI tree stored in the node data temporary storage unit 236 and the link data temporary storage unit 237 in step S208.
　ノード１００１～１０１４はツリーＩＤが２の過去ＫＰＩツリーにおけるノードＩＤ１００１～１０１４のノードである。さらに、ノード１０１３は同期ノードであり、ノード１００６を参照している。また、リスト１０１５は抽出のために使用されるリストである。このリスト１０１５は、部分ＫＰＩツリー抽出部２３５が持っている。ステップＳ２０２で、「製造能力」のノードが選択され、ステップＳ２０７で「製造原価」のサブゴールノードが指定されたとする。 Nodes 1001 to 1014 are nodes with node IDs 1001 to 1014 in the past KPI tree having a tree ID of 2. Further, node 1013 is a synchronization node and refers to node 1006. Also, list 1015 is a list used for extraction. This list 1015 is possessed by the partial KPI tree extraction unit 235. It is assumed that the node of "manufacturing capacity" is selected in step S202 and the subgoal node of "manufacturing cost" is specified in step S207.
　まず、部分ＫＰＩツリー抽出部２３５は、選択されたノードが含まれるリストを作成する（Ｓ９０１）。ここでは、選択されたノードのノードＩＤが１００８であるため、部分ＫＰＩツリー抽出部２３５は、リスト１０１５に１００８を含める（図１０）。 First, the partial KPI tree extraction unit 235 creates a list including the selected nodes (S901). Here, since the node ID of the selected node is 1008, the partial KPI tree extraction unit 235 includes 1008 in the list 1015 (FIG. 10).
　作成されたリスト１０１５が空でなければ（Ｓ９０２：ＮＯ）、部分ＫＰＩツリー抽出部２３５は、リスト１０１５の最初のノードに注目する。この例では、部分ＫＰＩツリー抽出部２３５はノード１００８に注目し（Ｓ９０３）、ノードデータ一時格納部２３６において注目されたノードのチェック欄４０８に１を入力し、リンクデータ一時格納部２３７において親ノードへのリンクのチェック欄５０５に１を入力する（Ｓ９０４）。 If the created list 1015 is not empty (S902: NO), the partial KPI tree extraction unit 235 pays attention to the first node of the list 1015. In this example, the partial KPI tree extraction unit 235 pays attention to the node 1008 (S903), inputs 1 to the check field 408 of the node of interest in the node data temporary storage unit 236, and inputs 1 to the parent node in the link data temporary storage unit 237. Enter 1 in the check field 505 of the link to (S904).
　さらに、この注目ノードが同期ノードの参照先ノードの場合（Ｓ９０５：ＹＥＳ）、部分ＫＰＩツリー抽出部２３５は、は参照元の同期ノードをリスト１０１５に追加する（Ｓ９０６）。例えば、図１０のノード１００６が注目されている場合、部分ＫＰＩツリー抽出部２３５は、リスト１０１５に、ノード１００６を参照している同期ノードＩＤ１０１３を追加する。注目ノードが同期ノードの参照先ノードでない場合（Ｓ９０５：ＮＯ）またはステップＳ９０６を終えた場合、部分ＫＰＩツリー抽出部２３５は、注目ノードの親ノードに新たに注目し、親ノードのチェック欄４０８に１を入力する（Ｓ９０７）。 Further, when this attention node is the reference node of the synchronization node (S905: YES), the partial KPI tree extraction unit 235 adds the synchronization node of the reference source to the list 1015 (S906). For example, if node 1006 in FIG. 10 is of interest, the partial KPI tree extraction unit 235 adds to list 1015 a synchronization node ID 1013 that references node 1006. When the node of interest is not the referenced node of the synchronization node (S905: NO) or when step S906 is completed, the partial KPI tree extraction unit 235 newly pays attention to the parent node of the node of interest and displays the check box 408 of the parent node. Enter 1 (S907).
　情報ノードの親ノードは演算ノードであるため、この注目ノードは演算ノードである。本例では、注目ノードが「×」または「÷」の演算ノードである場合（Ｓ９０８：ＹＥＳ）、部分ＫＰＩツリー抽出部２３５は、そのノードの子ノードと子ノードへのリンクのチェック欄４０８、５０５に１を入力する（Ｓ９０９）。例えば、図１０のノード１００７が注目されている場合、部分ＫＰＩツリー抽出部２３５は、ノード１００５及びノード１００５へのリンクのチェック欄４０８、５０５に１を入力する。 Since the parent node of the information node is an arithmetic node, this attention node is an arithmetic node. In this example, when the node of interest is an arithmetic node of "x" or "÷" (S908: YES), the partial KPI tree extraction unit 235 checks the child node of that node and the check column 408 of the link to the child node. Enter 1 in 505 (S909). For example, when the node 1007 in FIG. 10 is attracting attention, the partial KPI tree extraction unit 235 inputs 1 to the check boxes 408 and 505 of the link to the node 1005 and the node 1005.
　ステップＳ９０９の処理を終えた場合または注目ノードが「×」「÷」でない場合（Ｓ９０８：ＮＯ）、部分ＫＰＩツリー抽出部２３５は、注目ノードの親ノードへのリンクのチェック欄５０５に１を入力し、注目ノードの親ノードを新たな注目ノードとする（Ｓ９１０）。 When the process of step S909 is completed or the node of interest is not "x" or "÷" (S908: NO), the partial KPI tree extraction unit 235 inputs 1 in the check field 505 of the link to the parent node of the node of interest. Then, the parent node of the attention node is set as a new attention node (S910).
　演算ノードの親ノードは情報ノードであるため、この注目ノードは情報ノードである。注目ノードがサブゴールノードでなければ（Ｓ９１１：ＮＯ）、部分ＫＰＩツリー抽出部２３５はステップＳ９０４に戻り、サブゴールノードが注目ノードになるまで、ステップＳ９０４からＳ９１０までを繰り返す。 Since the parent node of the arithmetic node is an information node, this attention node is an information node. If the node of interest is not a subgoal node (S9111: NO), the partial KPI tree extraction unit 235 returns to step S904 and repeats steps S904 to S910 until the subgoal node becomes a node of interest.
　一方、注目ノードがサブゴールノードである場合（Ｓ９１１：ＹＥＳ）、部分ＫＰＩツリー抽出部２３５は、リストの最初の要素を削除する（Ｓ９１２）。たとえば、リストの内容が「１００８、１０１３」の場合、部分ＫＰＩツリー抽出部２３５は、最初の要素１００８を削除し、リストを「１０１３」とする。ステップＳ９８１２の後にリストが要素を持たなければ（Ｓ９０２：ＹＥＳ）、部分ＫＰＩツリー抽出部２３５は、サブゴールノードのチェック欄４０８に１を入力する（Ｓ９１３）。図１０の例では、製造原価のノード１００１のチェック欄４０８に１を入力する。 On the other hand, when the node of interest is a subgoal node (S911: YES), the partial KPI tree extraction unit 235 deletes the first element of the list (S912). For example, when the content of the list is "1008, 1013", the partial KPI tree extraction unit 235 deletes the first element 1008 and sets the list to "1013". If the list has no elements after step S9812 (S902: YES), the partial KPI tree extraction unit 235 inputs 1 in the check box 408 of the subgoal node (S913). In the example of FIG. 10, 1 is input to the check field 408 of the node 1001 of the manufacturing cost.
　部分ＫＰＩツリー抽出部２３５は、ノードデータ一時格納部２３６とリンクデータ一時格納部２３７のレコードから、チェック欄４０８又は５０５に１が入っていないノード及びリンクのレコードを削除する（Ｓ９１４）。さらに、部分ＫＰＩツリー抽出部２３５は、子ノードも親ノードも持たないノードを削除する（Ｓ９１５）。図１０でステップＳ２０８の処理を行うと、ノード１０１４及び、ノード１０１４とノード１００２とをつなぐリンクが削除される。図１１は、図１０に示すＫＰＩツリーに対するステップ２０８の後の、部分ＫＰＩツリーを示す。この部分ＫＰＩツリーが、ＫＰＩツリー部品１１となる。 The partial KPI tree extraction unit 235 deletes the node and link records in which the check column 408 or 505 does not contain 1 from the records of the node data temporary storage unit 236 and the link data temporary storage unit 237 (S914). Further, the partial KPI tree extraction unit 235 deletes a node having neither a child node nor a parent node (S915). When the process of step S208 is performed in FIG. 10, the node 1014 and the link connecting the node 1014 and the node 1002 are deleted. FIG. 11 shows a partial KPI tree after step 208 for the KPI tree shown in FIG. This partial KPI tree becomes the KPI tree component 11.
　図１０を参照して説明した例において、過去ＫＰＩツリーの演算ノードは、「×」、「÷」、「＋」又は「－」のいずれかである。上記例は、「×」又は「÷」の演算ノードの子ノードと子ノードへのリンクを残し、「＋」又は「－」の演算ノードの子ノードと子ノードへのリンクを削除する。 In the example described with reference to FIG. 10, the operation node of the past KPI tree is either "x", "÷", "+" or "-". In the above example, the link to the child node and the child node of the arithmetic node of "x" or "÷" is left, and the link to the child node and the child node of the arithmetic node of "+" or "-" is deleted.
　これにより、当該演算ノードについて、選択ノードの変化に応じたサブゴールノードの変化の計算に不要なノードを削除し、必要なノードを残すことができ、より部品に適した部分ツリーを得ることができる。上記例の演算の種類は四つであるが、ＫＰＩツリーは任意の演算を行う演算ノードを含むことができ、演算方法に基づいて、出力に対する影響について複数の入力が独立であるか相互依存しているか判定可能である。 As a result, for the arithmetic node, nodes unnecessary for calculating the change of the subgoal node according to the change of the selected node can be deleted, necessary nodes can be left, and a partial tree more suitable for the component can be obtained. .. Although there are four types of operations in the above example, the KPI tree can include operation nodes that perform arbitrary operations, and depending on the operation method, multiple inputs are independent or interdependent with respect to the effect on the output. It is possible to determine whether or not it is.
　また、上記例は、選択ノードからサブゴールノードへの経路内に同期ノードの参照先ノードが残され（削除されず）、その同期ノードがサブゴールノードへの経路を有する場合、その同期ノードを部分ツリーに含める。これにより、より正確に選択ノードの変化によるサブゴールノードの変化を計算できる部分ツリーを構成することができる。なお、同期ノードを残さなくてもよい。 Further, in the above example, when the reference node of the synchronization node is left (not deleted) in the route from the selected node to the subgoal node and the synchronization node has a route to the subgoal node, the synchronization node is partially treeed. Include in. This makes it possible to construct a partial tree that can more accurately calculate the change of the subgoal node due to the change of the selected node. It is not necessary to leave the synchronization node.
　図７に戻って、処理フローの説明をする。部分ＫＰＩツリー抽出部２３５は作成した過去ＫＰＩツリーの部分ツリーのデータ、すなわちノードデータ一時格納部２３６とリンクデータ一時格納部２３７に格納されているデータを、表示部２１０に送る。表示部２１０は受け取った部分ツリーの情報を表示する（Ｓ２１０）。 Returning to FIG. 7, the processing flow will be explained. The partial KPI tree extraction unit 235 sends the data of the created partial tree of the past KPI tree, that is, the data stored in the node data temporary storage unit 236 and the link data temporary storage unit 237 to the display unit 210. The display unit 210 displays the received partial tree information (S210).
　入力操作部２２０は、ユーザから、表示された部分ツリーを部品として利用するか選択を受ける（Ｓ２１１）。部分ツリーを使用する場合（Ｓ２１２：ＹＥＳ）、部分ＫＰＩツリー抽出部２３５は、ノードデータ一時格納部２３６とリンクデータ一時格納部２３７の内容を作成ＫＰＩツリー格納部２３１に格納する（Ｓ２１３）。さらに、作成ＫＰＩツリーの葉ノードの中に部品のサブゴールノードと一致するノードがある場合、部分ＫＰＩツリー抽出部２３５は、ステップＳ２０９で作成した部分ツリーと作成ＫＰＩツリー格納部２３１に既にある作成ＫＰＩツリーの結合を行う。 The input operation unit 220 receives a user's choice as to whether to use the displayed partial tree as a component (S211). When the partial tree is used (S212: YES), the partial KPI tree extraction unit 235 stores the contents of the node data temporary storage unit 236 and the link data temporary storage unit 237 in the creation KPI tree storage unit 231 (S213). Further, if there is a node in the leaf node of the created KPI tree that matches the subgoal node of the part, the partial KPI tree extraction unit 235 has the partial tree created in step S209 and the created KPI already in the created KPI tree storage unit 231. Join the trees.
　部分ＫＰＩツリー抽出部２３５は、サブゴールノードである部分ツリーの頂点ノードと、作成ＫＰＩツリーの葉ノードが重なるように、二つのツリーを結合する。具体的には、部分ＫＰＩツリー抽出部２３５は、部分ツリーからサブゴールノードとその子ノードへのリンクを削除し、当該子ノードと作成ＫＰＩツリーの該当する葉ノードとのリンクを作成する。図１２は、部分ツリーと作成ＫＰＩツリーの結合の例を示す。図１２のようにノード８０１を含む作成ＫＰＩツリーとツリー部品１１があったとする。 The partial KPI tree extraction unit 235 joins two trees so that the vertex node of the partial tree, which is a subgoal node, and the leaf node of the created KPI tree overlap. Specifically, the partial KPI tree extraction unit 235 deletes the link from the partial tree to the subgoal node and its child node, and creates a link between the child node and the corresponding leaf node of the created KPI tree. FIG. 12 shows an example of joining a partial tree and a created KPI tree. It is assumed that there is a created KPI tree including a node 801 and a tree component 11 as shown in FIG.
　ノード８０５とノード１００１は一致しているため、部分ＫＰＩツリー抽出部２３５は、ツリーの結合を行う。部分ＫＰＩツリー抽出部２３５は、ノード１１０１とリンク１１１５を削除し、新たにノード８０５とノード１１０２とのリンク１２０１を作成する。表示部２１０は、ツリー部品を結合した作成ＫＰＩツリーを、図１２に示すように、出力装置１０４に出力してもよい。 Since the node 805 and the node 1001 match, the partial KPI tree extraction unit 235 joins the trees. The partial KPI tree extraction unit 235 deletes the node 1101 and the link 1115, and newly creates a link 1201 between the node 805 and the node 1102. The display unit 210 may output the created KPI tree in which the tree parts are combined to the output device 104 as shown in FIG.
　図７に戻り、ユーザが部分ツリーを使用しない場合（Ｓ２１２：ＮＯ）、Ｓ２１５のステップが実行される。こののち作成ＫＰＩツリーのノードにおいてＫＧＩノードを除くすべてのノードに親ノードがあれば（Ｓ２１４：ＹＥＳ）、ステップＳ２を終了する。ステップＳ２１４の判定結果がＮＯである場合、フローはステップＳ２０１に戻る。 Returning to FIG. 7, when the user does not use the partial tree (S212: NO), the step of S215 is executed. After that, if all the nodes of the created KPI tree except the KGI node have a parent node (S214: YES), step S2 is terminated. If the determination result in step S214 is NO, the flow returns to step S201.
　図６に戻り、ステップＳ２でＫＰＩツリーの作成を終えると、ＫＰＩツリー作成支援システム１０は、作成ＫＰＩツリー格納部２３１のすべてのノードデータとリンクデータのツリーＩＤを過去ＫＰＩツリーデータベース内のツリーＩＤにはない数値に変更し、過去ＫＰＩツリーデータベース２４０に格納する（Ｓ３）。以上により、ＫＰＩツリー作成支援システムを終了する。 Returning to FIG. 6, when the creation of the KPI tree is completed in step S2, the KPI tree creation support system 10 uses the tree IDs of all the node data and the link data of the created KPI tree storage unit 231 as the tree IDs in the past KPI tree database. Change to a value that does not exist in the past KPI tree database 240 (S3). With the above, the KPI tree creation support system is terminated.
　上述のように、本実施形態によれば、過去のＫＰＩツリーの記録から作成したいＫＰＩツリーの部品利用に有用なＫＰＩツリーを検索し、ツリーを部品化し、ＫＧＩ及びＫＧIに連なるツリーに結合することで過去の記録を容易に流用することができる。 As described above, according to the present embodiment, a KPI tree useful for using the parts of the KPI tree to be created is searched from the records of the past KPI trees, the tree is made into parts, and the tree is combined with the KGI and the tree connected to the KGI. You can easily divert past records.
　なお、本発明は上記した実施例に限定されるものではなく、様々な変形例が含まれる。例えば、上記した実施例は本発明を分かりやすく説明するために詳細に説明したものであり、必ずしも説明したすべての構成を備えるものに限定されるものではない。また、ある実施例の構成の一部を他の実施例の構成に置き換えることが可能であり、また、ある実施例の構成に他の実施例の構成を加えることも可能である。また、各実施例の構成の一部について、他の構成の追加・削除・置換をすることが可能である。 The present invention is not limited to the above-described embodiment, but includes various modifications. For example, the above-described embodiment has been described in detail in order to explain the present invention in an easy-to-understand manner, and is not necessarily limited to the one including all the configurations described. Further, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Further, it is possible to add / delete / replace a part of the configuration of each embodiment with another configuration.
　また、上記の各構成・機能・処理部等は、それらの一部又は全部を、例えば集積回路で設計する等によりハードウェアで実現してもよい。また、上記の各構成、機能等は、演算装置がそれぞれの機能を実現するプログラムを解釈し、実行することによりソフトウェアで実現してもよい。各機能を実現するプログラム、テーブル、ファイル等の情報は、メモリや、ハードディスク、ＳＳＤ（Ｓｏｌｉｄ　Ｓｔａｔｅ　Ｄｒｉｖｅ）等の記録装置、または、ＩＣカード、ＳＤカード等の記録媒体に置くことができる。 Further, each of the above configurations, functions, processing units, etc. may be realized by hardware, for example, by designing a part or all of them with an integrated circuit. Further, each of the above configurations, functions, and the like may be realized by software by the arithmetic unit interpreting and executing a program that realizes each function. Information such as programs, tables, and files that realize each function can be placed in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), or a recording medium such as an IC card or an SD card.
　また、制御線や情報線は説明上必要と考えられるものを示しており、製品上必ずしもすべての制御線や情報線を示しているとは限らない。実際には殆どすべての構成が相互に接続されていると考えてもよい。 In addition, the control lines and information lines indicate what is considered necessary for explanation, and not all control lines and information lines are shown in the product. In practice, it can be considered that almost all configurations are interconnected.

Claims (8)

  1. 　情報を示す情報ノードと演算を示す演算ノードとを含むＫＰＩツリーから、部分ツリーを抽出するシステムであって、
    　１以上の演算装置と、
    　１以上の記憶装置と、を含み、
    　前記１以上の記憶装置は、第１ＫＰＩツリーを格納し、
    　前記１以上の演算装置は、
    　前記第１ＫＰＩツリーにおいて、第１下位情報ノードと、前記第１下位情報ノードの上位情報ノードである第１上位情報ノードとを含む、第１部分ツリーを抽出し、
    　前記第１部分ツリーに含まれる演算ノードに基づき、前記第１下位情報ノードの変化による前記第１上位情報ノードの変化の算出に不要なノードを、前記第１部分ツリーから削除する、システム。     A system that extracts a partial tree from a KPI tree that includes an information node that indicates information and an operation node that indicates operations.
    One or more arithmetic units and
    Including one or more storage devices,
    The one or more storage devices store the first KPI tree and
    The one or more arithmetic units are
    In the first KPI tree, the first subtree including the first lower information node and the first upper information node which is the upper information node of the first lower information node is extracted.
    A system that deletes from the first subtree a node that is unnecessary for calculating the change of the first upper information node due to the change of the first lower information node based on the arithmetic node included in the first subtree.
  2. 　請求項１に記載のシステムであって、
    　前記１以上の演算装置は、
    　前記第１下位情報ノードから前記第１上位情報ノードまでの経路において、前記第１下位情報ノードの変化による前記第１上位情報ノードの変化の算出に必要な情報ノードを特定し、
    　前記第１ＫＰＩツリーにおいて前記第１上位情報ノードへの経路を有し、前記必要な情報ノードを参照するノードを、前記部分ツリーに含める、システム。     The system according to claim 1.
    The one or more arithmetic units are
    In the route from the first lower information node to the first upper information node, the information node necessary for calculating the change of the first upper information node due to the change of the first lower information node is specified.
    A system in which a node having a route to the first higher-level information node in the first KPI tree and referencing the required information node is included in the subtree.
  3. 　請求項１に記載のシステムであって、
    　前記１以上の演算装置は、
    　指定された１以上の情報識別子を取得し、
    　前記第１ＫＰＩツリーにおける前記第１下位情報ノードの上位情報ノードにおいて、前記１以上の情報識別子の一つが同定する情報を示し、前記第１下位情報ノードに最も近い上位情報ノードを、前記第１上位情報ノードとして選択する、システム。     The system according to claim 1.
    The one or more arithmetic units are
    Acquires one or more specified information identifiers and
    In the upper information node of the first lower information node in the first KPI tree, the information identified by one of the one or more information identifiers is shown, and the upper information node closest to the first lower information node is referred to as the first upper information node. The system you choose as the information node.
  4. 　請求項３に記載のシステムであって、
    　前記１以上の演算装置は、
    　前記第１ＫＰＩツリーから、第２下位情報ノードと、前記１以上の情報識別子の一つが同定する情報を示し、前記第２下位情報ノードに最も近い上位情報ノードである第２上位情報ノードと、を抽出し、
    　前記第１下位情報ノードと前記第１上位情報ノードとの間の距離及び前記第２下位情報ノードと前記第２上位情報ノードとの間の距離に基づいて、前記第１上位情報ノード及び前記第２上位情報ノードのランク付けを行う、システム。     The system according to claim 3.
    The one or more arithmetic units are
    From the first KPI tree, the second lower information node and the second upper information node, which is the upper information node closest to the second lower information node, showing the information identified by one of the one or more information identifiers, and the like. Extract and
    The first higher information node and the first higher information node based on the distance between the first lower information node and the first upper information node and the distance between the second lower information node and the second upper information node. 2 A system that ranks higher-level information nodes.
  5. 　請求項１に記載のシステムであって、
    　前記１以上の記憶装置は、前記第１ＫＰＩツリーを含む複数のＫＰＩツリーと、ユーザの作成ＫＰＩツリーと、を格納し、
    　前記１以上の演算装置は、
    　前記複数のＫＰＩツリーの第２ＫＰＩツリーにおいて、第３下位情報ノードと、前記第３下位情報ノードの上位情報ノードである第３上位情報ノードとを抽出し、
    　前記作成ＫＰＩツリーと前記第１ＫＰＩツリー及び前記第２ＫＰＩツリーそれぞれとの間の類似度に基づいて、前記第１上位情報ノード及び前記第３上位情報ノードのランク付けを行う、システム。     The system according to claim 1.
    The one or more storage devices store a plurality of KPI trees including the first KPI tree and a user-created KPI tree.
    The one or more arithmetic units are
    In the second KPI tree of the plurality of KPI trees, the third lower information node and the third upper information node, which is the upper information node of the third lower information node, are extracted.
    A system that ranks the first upper information node and the third upper information node based on the degree of similarity between the created KPI tree and each of the first KPI tree and the second KPI tree.
  6. 　請求項５に記載のシステムであって、
    　前記１以上の演算装置は、前記第１部分ツリーの情報をユーザに提示する、システム。     The system according to claim 5.
    The one or more arithmetic units are a system that presents the information of the first subtree to the user.
  7. 　請求項１に記載のシステムであって、
    　前記１以上の記憶装置は、ユーザの作成ＫＰＩツリーを格納し、
    　前記第１部分ツリーの頂点ノードが示す情報が、前記作成ＫＰＩツリーの葉ノードが示す情報と一致する場合、前記１以上の演算装置は、前記頂点ノードが前記葉ノードと重なるように、前記第１部分ツリーを前記作成ＫＰＩツリーに結合する、システム。     The system according to claim 1.
    The one or more storage devices store the user-created KPI tree.
    When the information indicated by the vertex node of the first partial tree matches the information indicated by the leaf node of the created KPI tree, the one or more arithmetic units may use the first or more arithmetic units so that the vertex node overlaps with the leaf node. A system that joins a partial tree to the created KPI tree.
  8. 　システムが、情報ノードと演算ノードとを含むＫＰＩツリーから部分ツリーを抽出する方法であって、前記システムは、第１ＫＰＩツリーを格納し、前記方法は、
    　前記システムが、前記第１ＫＰＩツリーにおいて、第１下位情報ノード及び第１上位情報ノードを含む、第１部分ツリーを抽出し、
    　前記システムが、前記部分ツリーに含まれる演算ノードに基づき、前記第１下位情報ノードの変化による前記第１上位情報ノードの変化の算出に無関係なノードを、抽出した前記第１部分ツリーから削除する、ことを含む方法。     A method in which a system extracts a partial tree from a KPI tree containing an information node and an arithmetic node, wherein the system stores a first KPI tree, and the method is:
    The system extracts a first subtree containing a first lower information node and a first upper information node in the first KPI tree.
    Based on the arithmetic node included in the partial tree, the system deletes the node unrelated to the calculation of the change of the first upper information node due to the change of the first lower information node from the extracted first subtree. , How to include that.
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