Data analysis processing apparatus, data analysis processing method, and data analysis processing program

Abstract

This data analysis processing device has a multi-dimensional database, a multi-dimensional database management unit, an online analysis processing operation execution unit, and a structure operation execution unit. The multi-dimensional database management unit: associates data representing temporal, spatial, and specific dimensions as well as characteristics that embody an event with an identifier of the event; and stores and manages said data in a multi-dimensional database as a multi-dimensional cube constructed for each subject, together with data that represents the structure of the event. The online analysis processing operation execution unit causes the multi-dimensional database management unit to refer to and aggregate the data constituting existing multi-dimensional cubes or to generate a new multi-dimensional cube, using an augment instructed from a client or using data that constitutes other multi-dimensional cubes. The structure operation execution unit causes the multi-dimensional database management unit to increase or decrease the data that constitutes multi-dimensional cube, using the data that represents the structure of the event.

Description

Technical Field

[0001] The present invention relates to a data analysis processing apparatus, a data analysis processing method, and a data analysis processing program.

Background Art

[0002] Regarding real-world events that change temporally and / or spatially due to generation and disappearance and / or state transitions, a data analysis processing apparatus is known that maps data representing the real-world events to a multidimensional cube and analyzes them by an online analysis processing operation. The data analysis processing apparatus uses, for example, a method disclosed in Non-Patent Document 1. That method performs the following Processes 1 and 2.

[0003] 1. A multidimensional cube is configured from data representing the characteristics of an event and data of dimensions that identify the data representing the characteristics.

[0004] 2. By performing an online analysis processing operation on the multidimensional cube, the data constituting the multidimensional cube is analyzed.

Prior Art Documents

Non-Patent Documents

[0005]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] Conventional data analysis processing apparatuses have limitations in the viewpoints for analyzing data that constitutes a multidimensional cube. For example, when configuring a multidimensional cube, conventional data analysis processing apparatuses use data representing the characteristics of an event and data of dimensions for identifying the data representing the characteristics, but do not use the identifier of the event that is the information source of the data or data representing the structure of the event.

[0007] Therefore, when analyzing data that constitutes a multidimensional cube, it is possible to analyze the data by focusing on the dimensions, but it is not possible to analyze the data by focusing on the structure of the event that is the information source of the data.

[0008] The present invention has been made in view of the above circumstances, and an object thereof is to provide a data analysis processing apparatus, a data analysis processing method, and a data analysis processing program capable of analyzing data by focusing on the structure of an event that is the information source of the data when analyzing data that constitutes a multidimensional cube.

Means for Solving the Problems

[0009] One aspect of the present invention is a data analysis processing apparatus that maps data embodying a real-world event that changes in at least one of time and space due to at least one of generation / extinction and state transition to a multidimensional cube and analyzes it by an online analysis processing operation. The data analysis processing apparatus associates, with an identifier of the event for identifying the event that is the information source of the data, data of a time dimension and data of a space dimension that embody temporal and spatial changes due to at least one of the generation / extinction and state transition as data embodying the event, data of a plurality of types of specific dimensions depending on a theme, and data representing a plurality of types of characteristics of the event depending on the theme identified by the data of the time dimension, the data of the space dimension, and the data of the specific dimensions. The structure between the two events is represented by the identifiers of the two events, the type of the structure between the two events, and the information on attributes. Along with the data representing the structure of the event, as the multi-dimensional cube constructed for each subject, it is stored in a multi-dimensional database and managed by a multi-dimensional database management unit. An online analysis processing operation execution unit uses the arguments instructed by the client or the data constituting other multi-dimensional cubes to cause the multi-dimensional database management unit to refer to / sum up the data constituting the existing multi-dimensional cube, or to generate a new multi-dimensional cube. A structure operation execution unit uses the data representing the structure of the event constituting the multi-dimensional cube to increase or decrease the data constituting the multi-dimensional cube.

[0010] One aspect of the present invention maps data embodying an event to a multi-dimensional cube for real-world events that change in at least one of time and space due to at least one of generation / extinction and state transition, and analyzes it by an online analysis processing operation. , executed by a computer It is a data analysis processing method. The data analysis processing method associates, with an identifier of the event for identifying the event, which is the information source of the data, data in a time dimension and data in a space dimension that embody temporal and spatial changes due to at least one of the generation / extinction and state transition as data embodying the event, data of a plurality of types of specific dimensions depending on the subject, and data representing a plurality of types of characteristics of the event depending on the subject identified by the data in the time dimension, the data in the space dimension, and the data of the specific dimensions. The structure between the two events is represented by the identifiers of the two events, the type of the structure between the two events, and the information on attributes. It includes accumulating and managing in the multi-dimensional cube constructed for each subject along with the data representing the structure of the event, using the arguments instructed by the client or the data constituting other multi-dimensional cubes to refer to / sum up the data constituting the existing multi-dimensional cube, or to generate a new multi-dimensional cube, and using the data representing the structure of the event constituting the multi-dimensional cube to increase or decrease the data constituting the multi-dimensional cube.

[0011] The data analysis processing program according to one aspect of the present invention causes a computer to execute the functions of each component of the above-described data analysis processing apparatus.

Advantages of the Invention

[0012] According to the present invention, there are provided a data analysis processing apparatus, a data analysis processing method, and a data analysis processing program capable of analyzing data by focusing on the structure of an event that is an information source of the data when analyzing data constituting a multidimensional cube.

Brief Description of the Drawings

[0013]

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Embodiment for Carrying Out the Invention

[0014] Hereinafter, embodiments according to the present invention will be described with reference to the drawings.

[0015] (Configuration) FIG. 1 is a block diagram showing an example of a configuration of a data analysis processing apparatus 10 according to an embodiment. The data analysis processing apparatus 10 includes an online analysis processing operation execution unit 12, a structure operation execution unit 14, a multidimensional database management unit 16, and a multidimensional database 18.

[0016] The data analysis processing apparatus 10 is an apparatus that maps data representing a real-world event that changes temporally and / or spatially due to generation / extinction and / or state transition to a multidimensional cube and analyzes it by an online analysis processing operation. Hereinafter, for convenience, the online analysis processing is referred to as OLAP (Online Analytical Processing). That is, the online analysis processing operation is referred to as an OLAP operation, and the online analysis processing operation execution unit 12 is referred to as an OLAP operation execution unit 12.

[0017] The multi-dimensional database management unit 16 associates data of a time dimension and data of a space dimension that represent temporal and spatial changes due to at least one of generation / extinction and state transition, which are data representing an event, data of a plurality of types of specific dimensions dependent on a subject, and data representing a plurality of types of characteristics of an event dependent on a subject identified by the data of the time dimension, the data of the space dimension, and the data of the specific dimension, with an identifier of the event for identifying the event that is the information source of these data, and accumulates them in the multi-dimensional database 18 as a multi-dimensional cube constructed for each subject together with data representing the structure of the event, and manages the multi-dimensional database 18.

[0018] FIG. 2 is a diagram showing examples of data of a time dimension, a space dimension, a specific dimension, and data representing characteristics of an event that constitute a multi-dimensional cube. The upper part (a) of FIG. 2 shows non-normalized tabular data, and the lower part (b) of FIG. 2 shows normalized tabular data. The tabular data each have, for each serial number, an identifier of the data, data of an identifier of the event, and values of related data.

[0019] FIGS. 3 to 6 are diagrams showing an example of data representing the structure of an event that constitutes a multi-dimensional cube and an example of a schematic diagram of the structure of the event. FIG. 7 is a diagram showing an example of a schematic diagram of the structure of the event.

[0020] In FIGS. 3 to 6, the data representing the structure of events is shown as tabular data. The tabular data is composed of a bidirectional linked list of identifiers (itself and the opposite) of two events representing the structure between the two events, and information on the type and attributes of the structure between the two events. The type information indicates the type of the structure, and the attribute information (attribute value) indicates the use of the structure, etc. The attribute value may include time information when the structure of the event is established. In this case, the attribute value can be either the unique value of the attribute or the data value in the time dimension. The attribute value may have a hierarchical structure. In this case, by specifying a certain attribute value, not only the specified attribute value itself but also the lower-level attribute values or the upper-level attribute values can be specified simultaneously. In FIGS. 3 to 6, in addition to the tabular data representing the structure of events, the hierarchical structure of the attribute values is also shown.

[0021] In FIGS. 3 to 7, the schematic diagram of the structure of events shows the structure between two events identified by identifiers. The structure between two events identified by identifiers is based on a bidirectional linked list of identifiers (itself and the opposite) of the two events. In FIGS. 3 to 7, the identifiers of the events are also shown. This identifier is associated with the data representing the structure of the events. In FIG. 7, the case name of the event, the type of the structure, and the attributes are also shown.

[0022] FIG. 3 is a diagram showing an example of tabular data representing the network structure of events and a schematic diagram of the structure of events. For example, the structure in FIG. 3 is a transportation network, a power transmission network / gas pipeline network / water supply network, a communication network, etc.

[0023] When the structure is a transportation network, for example, the event identifiers p1 and p2 are distribution bases / intersections, and the event identifier p3 is a road / railway / river / sea route / air route. When the structure is a power transmission network / gas pipeline network / water supply network, for example, the event identifiers p1 and p2 are power / gas / water distribution facilities, and the event identifier p3 is a utility tunnel. When the structure is a communication network, for example, the event identifiers p1 and p2 are communication facilities, and the event identifier p3 is a cable duct / telegraph pole.

[0024] The data (row) p1p3 of the network structure shows the structure of p3 as seen from p1. The type indicates a "dependence" relationship, and the attribute indicates a delivery network. The data (row) p3p1 of the network structure shows the structure of p1 as seen from p3. The type indicates a "dependence" relationship, and the attribute indicates a delivery network. Hereinafter, "dependence" will be simply referred to as "dependence relationship". The hierarchical structure of the attribute values of the delivery network has regular, reserve, and temporary levels under delivery.

[0025] Figure 4 is a diagram showing tabular data representing the network structure and hierarchical structure of events, and an example of a schematic diagram of the structure of an event. For example, the structure in Figure 4 is a hypothetical transportation network or the like.

[0026] In this case, for example, the event identifiers p1, p2 are delivery bases / intersections, the event identifier p3 is a road / railway / river / sea route / air route, the event identifiers p4, p5 are warehouses / signals, and the event identifier p6 is a road sign / river sign / light wave sign / radiowave sign.

[0027] The data (row) p1p3 of the network structure shows the structure of p3 as seen from p1. The type indicates a dependence relationship, and the attribute indicates a temporary delivery network. The data (row) p3p1 of the network structure shows the structure of p1 as seen from p3. The type indicates a dependence relationship, and the attribute indicates a temporary delivery network. The hierarchical structure of the attribute values of the network structure has regular, reserve, and temporary levels under delivery.

[0028] The hierarchical structure data (row) p3p6 shows the structure of p6 as seen from p3. The type indicates a has-a (aggregation relationship), and the attribute indicates that it is a temporary component. The hierarchical structure data (row) p6p3 shows the structure of p3 as seen from p6. The type indicates a part-of (composition relationship), and the attribute indicates that it is a temporary component. Hereinafter, "has-a (aggregation relationship)" will be simply referred to as "aggregation relationship", and "part-of (composition relationship)" will be simply referred to as "composition relationship". The hierarchical structure of the attribute values of the hierarchical structure has a permanent and a temporary at the lower level of the composition.

[0029] Figure 5 is a diagram showing tabular data representing the network structure and hierarchical structure of events and an example of a schematic diagram of the event structure. For example, the structure in Figure 5 is a permanent power transmission network / gas pipeline network / water supply network, communication network, etc. When the structure is a permanent power transmission network / gas pipeline network / water supply network, for example, the event identifiers p1, p2 are power / gas / water distribution facilities, the event identifier p3 is a utility tunnel, the event identifiers p7, p8 are power / gas / water equipment, and the event identifier p9 is a power line / gas pipe / water pipe. When the structure is a communication network, for example, the event identifiers p1, p2 are communication facilities, the event identifier p3 is a duct / telephone pole, the event identifiers p7, p8 are communication equipment, and the event identifier p9 is a communication cable.

[0030] The network structure data (row) p1p3 shows the structure of p3 as seen from p1. The type indicates a dependency relationship, and the attribute indicates that it is a regular distribution network. The network structure data (row) p3p1 shows the structure of p1 as seen from p3. The type indicates a dependency relationship, and the attribute indicates that it is a regular distribution network. The hierarchical structure of the attribute values of the network structure has a regular, a standby, and a temporary at the lower level of the distribution.

[0031] The hierarchical structure data (row) p3p9 shows the structure of p9 as seen from p3. The type indicates an aggregating relationship, and the attribute indicates a constant component. The hierarchical structure data (row) p9p3 shows the structure of p3 as seen from p9. The type indicates a composing relationship, and the attribute indicates a constant component. The hierarchical structure of the attribute values of the hierarchical structure has both constant and temporary components at the lower level of the composition.

[0032] Figure 6 is a diagram showing tabular data representing the network structure and hierarchical (abstraction) structure of events, and an example of a schematic diagram of the structure of an event. For example, the structure in Figure 6 is a permanent power transmission network / gas pipeline network / water supply network, communication network, etc. When the structure is a permanent power transmission network / gas pipeline network / water supply network, for example, the event identifiers p7, p8 are electrical / gas / water equipment, the event identifier p9 is an electric wire / gas pipe / water pipe, and the event identifier p10 is a requirements specification. When the structure is a permanent communication network, for example, the event identifiers p7, p8 are communication equipment, the event identifier p9 is a communication cable, and the event identifier p10 is a requirements specification.

[0033] The network structure data (row) p7p9 shows the structure of p9 as seen from p7. The type indicates a dependent relationship, and the attribute indicates a commonly used distribution network. The network structure data (row) p9p7 shows the structure of p7 as seen from p9. The type indicates a dependent relationship, and the attribute indicates a commonly used distribution network. The hierarchical structure of the attribute values of the network structure has commonly used, spare, and temporary components at the lower level of the distribution.

[0034] The data (row) p7p10 of the hierarchical (abstraction) structure shows the structure of p10 as seen from p7. The type indicates an Is-a (inheritance) relationship, and the attribute indicates the specification that p7 follows. The data (row) p10p7 of the hierarchical (abstraction) structure shows the structure of p7 as seen from p10. The type indicates an Is-a (inheritance) relationship, and the attribute indicates the product that follows p10. The hierarchical structure of the attribute values of the hierarchical (abstraction) structure has design under specification and product under design.

[0035] Figure 7 is a diagram showing an example of a schematic diagram representing the network structure of events and the hierarchical / hierarchical (abstraction) structure. For example, the structure of Figure 7 is a communication network with a communication protocol stack. In this case, for example, the event identifiers p7, p8 are L2 switches, the event identifiers p9, p13, 14 are communication cables, the event identifier p10 is a requirements specification document, the event identifiers p11, p12, p19 are routers, the event identifiers p15, p16 are L2 control boards, the event identifier p17 is an L2 routing control program, and the event identifier p18 is an L3 routing control program. Note that L2 and L3 respectively mean the second layer and the third layer.

[0036] The OLAP operation execution unit 12 receives the OLAP operation and arguments sent from the client 40, and instructs the multidimensional database management unit 16 to perform operations on the multidimensional data accordingly. Also, the OLAP operation execution unit 12 receives the operation result of the multidimensional data from the multidimensional database management unit 16 and sends the operation result to the client 40.

[0037] The structure operation execution unit 14 receives the structure operation and arguments sent from the client 40, and instructs the multidimensional database management unit 16 to perform operations on the multidimensional data accordingly. Also, the structure operation execution unit 14 receives the operation result of the multidimensional data from the multidimensional database management unit 16 and sends the operation result to the client 40.

[0038] The multidimensional database management unit 16 refers to / sums up the data that constitutes the existing multidimensional cube or generates a new multidimensional cube in response to the operation instructions from the OLAP operation execution unit 12, and returns the operation result to the OLAP operation execution unit 12. Further, the multidimensional database management unit 16 increases or decreases the data that constitutes the multidimensional cube by using the data representing the structure of the events that constitute the multidimensional cube in response to the operation instructions from the structure operation execution unit 14, and returns the operation result to the structure operation execution unit 14.

[0039] (Operation) Next, the processing operations of the data analysis processing apparatus 10 configured as described above will be described.

[0040] First, an overview of the operation of the data analysis processing apparatus 10 will be described. FIG. 8 is a sequence diagram for explaining an example of the operation of the data analysis processing apparatus 10.

[0041] 1. As shown by the dashed line in FIG. 8 and indicated as "ALT" and [OLAP operation], when the OLAP operation execution unit 12 receives an OLAP operation and arguments from the client 40, it instructs the multidimensional database management unit 16 to perform an operation on the multidimensional data accordingly.

[0042] 2. The multidimensional database management unit 16 refers to / sums up the data that constitutes the multidimensional cube or generates a new multidimensional cube in response to the instruction to operate on the multidimensional data.

[0043] 3. The multidimensional database management unit 16 returns the operation result to the OLAP operation execution unit 12.

[0044] 4. As shown by the dashed line in FIG. 8 and indicated as "LOOP", the OLAP operation execution unit 12 repeats the above instruction to the multidimensional database management unit 16 according to the content of the received OLAP operation and arguments.

[0045] 5. When the OLAP operation execution unit 12 can obtain the final result corresponding to the OLAP operation and arguments, it returns the operation result of the OLAP operation to the client 40.

[0046] 6. As shown by being enclosed by a dashed line and labeled "ALT" and "[Structure Operation]" in FIG. 8, when the structure operation execution unit 14 receives a structure operation and arguments from the client 40, it instructs the multidimensional database management unit 16 to perform an operation on the multidimensional data accordingly.

[0047] 7. The multidimensional database management unit 16 increases or decreases the data constituting the multidimensional cube by using the data representing the structure of the events constituting the multidimensional cube in response to an instruction for an operation on the multidimensional data.

[0048] 8. The multidimensional database management unit 16 returns the operation result to the structure operation execution unit 14.

[0049] 9. As shown by being enclosed by a dashed line and labeled "LOOP" in FIG. 8, the structure operation execution unit 14 repeats the above instruction to the multidimensional database management unit 16 according to the content of the received structure operation and arguments.

[0050] 10. When the structure operation execution unit 14 can obtain the final operation result corresponding to the content of the structure operation and arguments, it returns the operation result of the structure operation to the client 40.

[0051] (Operation Example 1) Next, a detailed example of the operation of the multidimensional database management unit 16 when increasing or decreasing the data constituting the multidimensional cube by using the data representing the structure of the events constituting the multidimensional cube will be described.

[0052] In this operation example, the multidimensional database management unit 16, in response to an instruction for an operation on the multidimensional data from the structure operation execution unit 14, uses the data of the source multidimensional cube by OLAP operation as the original data for both the source multidimensional cube and the destination multidimensional cube, and based on the type and attribute values of the data representing the structure of the events constituting the destination multidimensional cube, increases or decreases the data constituting the destination multidimensional cube.

[0053] FIG. 9 is a flowchart for explaining the details of the operation of the multidimensional database management unit 16 according to this operation example.

[0054] In step S11, the multidimensional database management unit 16 is waiting to receive an operation instruction for multidimensional data for a structure operation from the structure operation execution unit 14. That is, the multidimensional database management unit 16 maintains a waiting state until it receives an operation instruction for multidimensional data for a structure operation.

[0055] In step S12, when the multidimensional database management unit 16 receives an operation instruction for multidimensional data for a structure operation, it determines the type of the operation instruction.

[0056] As a result of the determination in step S12, if the operation instruction is an instruction to increase the data constituting the multidimensional cube at the generation destination, the multidimensional database management unit 16 executes the processes of the following steps S13a to S13c.

[0057] In step S13a, data whose type and attribute values satisfy the conditions is extracted from the data representing the structure of the event constituting the multidimensional cube at the generation destination.

[0058] In step S13b, the "identifier of the event that is the information source of the data to be added" is extracted. At this time, an identifier of any event may be selected.

[0059] In step S13c, data having the event with the extracted identifier as the information source is extracted from the multidimensional cube at the generation source and added to the multidimensional cube at the generation destination.

[0060] On the contrary, as a result of the determination in step S12, if the operation instruction is an instruction to decrease the data constituting the multidimensional cube at the generation destination, the multidimensional database management unit 16 executes the processes of the following steps S14a to S14c.

[0061] In step S14a, data whose type and attribute values satisfy the conditions is extracted from the data representing the structure of the events that constitute the multi-dimensional cube at the generation destination.

[0062] In step S14b, the "identifier of the event that is the information source of the data to be deleted" is extracted. At this time, the identifier of any event may be selected.

[0063] In step S14c, data having the event with the extracted identifier as the information source is deleted from the multi-dimensional cube at the generation destination.

[0064] In step S15, after the processing of steps S13a to S13c is completed or after the processing of steps S14a to S14c is completed, the multi-dimensional database management unit 16 returns the operation result to the structure operation execution unit 14.

[0065] The operation of the flowchart in FIG. 9 will be described on the premise of the state in FIG. 10. FIG. 10 shows an example of non-normalized tabular data as data representing the time dimension, space dimension, unique dimension, and characteristics that constitute the multi-dimensional cubes at the generation source and generation destination, tabular data as an example of data representing the structure of events, and an example of a schematic diagram of the structure of events. This example is, for example, a case where, by an OLAP operation, a multi-dimensional cube with the affected / failed equipment as the subject is generated from a multi-dimensional cube with all equipment as the subject by specifying the conditions of the data in the time dimension and space dimension. In FIG. 10, in the OLAP operation, the data and schematic diagram of the generation source are shown on the left side, and the data and schematic diagram of the generation destination are shown on the right side.

[0066] FIG. 11 is a schematic diagram for explaining an operation example when increasing data constituting a generated multidimensional cube on the premise of the state of FIG. 10. In step S13a, data (rows) whose type is dependence (dependent relationship) is extracted from the data representing the structure of the event constituting the generated multidimensional cube. In step S13b, an identifier of an event that exists in the identifier of the event (opposite) and does not exist in the identifier of the event (itself) is extracted by a set operation (difference). That is, an identifier of an event that is in a dependent relationship but is not an information source of the data constituting the generated multidimensional cube is extracted. This is the "identifier of the event that is the information source of the data to be added". In step S13c, data having the extracted identifier event as an information source is extracted from the source multidimensional cube and added to the generated multidimensional cube.

[0067] FIG. 12 is a diagram showing an example of data representing the structure of an event and a schematic diagram of the structure of the event when processed as described above. This example is a case where, for example, based on the survey results, facilities affected by the damaged / failed facilities, in other words, facilities indirectly damaged / failed are added (registered). In FIG. 12, the data and the schematic diagram before addition are shown on the left side, and the data and the schematic diagram after addition are shown on the right side.

[0068] FIG. 13 is a schematic diagram for explaining an operation example when reducing data constituting a multi-dimensional cube at the generation destination, on the premise of the state in the case of FIG. 11. In step S14a, data (rows) whose type is dependence (dependent relationship) is extracted from the data representing the structure of the events constituting the multi-dimensional cube at the generation destination. In sub-step S14b-1 of step S14b, by set operation (difference), event identifiers that exist in the event identifier (opposite) and do not exist in the event identifier (itself) are extracted. In sub-step S14b-2 of step S14b, the event identifier of the event paired with the extracted event identifier (itself) is extracted. That is, the event identifier that is not the information source of the data constituting the multi-dimensional cube at the generation destination is set as the event identifier (opposite), and the event identifier (itself) is extracted. This is the "event identifier that is the information source of the data to be deleted". In step S14c, data having the extracted identifier as the information source is deleted from the multi-dimensional cube at the generation destination.

[0069] FIG. 14 is a diagram showing an example of data representing the structure of an event and a schematic diagram of the structure of the event when processed as described above. This example is, for example, a case where, taking the restoration work as an opportunity, equipment affected by the equipment where a disaster / failure has occurred, in other words, equipment indirectly affected by the disaster / failure is deleted (registration is removed). In FIG. 14, the data and the schematic diagram before deletion are shown on the left side, and the data and the schematic diagram after deletion are shown on the right side.

[0070] (Operation Example 2) Subsequently, another example of the details of the operation of the multi-dimensional database management unit 16 when increasing or decreasing the data constituting the multi-dimensional cube using the data representing the structure of the events constituting the multi-dimensional cube will be described.

[0071] In this operation example, the multidimensional database management unit 16, in response to an instruction for operating multidimensional data from the structure operation execution unit 14, performs an increase process or a decrease process on the data constituting the destination multidimensional cube with the data of the source multidimensional cube as the original, for the source multidimensional cube and the destination multidimensional cube by OLAP operations. In the increase process, the multidimensional database management unit 16 increases the data constituting the destination multidimensional cube based on the data type and attribute values of the data representing the structure of the events constituting the destination multidimensional cube, and the values of the data representing the time dimension, space dimension, unique dimension, and characteristics constituting the source multidimensional cube. In the decrease process, the multidimensional database management unit 16 decreases the data constituting the destination multidimensional cube based on the data type and attribute values of the data representing the structure of the events constituting the destination multidimensional cube, and the values of the data representing the time dimension, space dimension, unique dimension, and characteristics constituting the destination multidimensional cube.

[0072] FIG. 15 is a flowchart for explaining the details of the operation of the multidimensional database management unit 16 according to this operation example.

[0073] In step S21, the multidimensional database management unit 16 waits for receiving an operation instruction for multidimensional data for structure operation from the structure operation execution unit 14. That is, the multidimensional database management unit 16 maintains a waiting state until it receives an operation instruction for multidimensional data for structure operation.

[0074] In step S22, when the multidimensional database management unit 16 receives an operation instruction for multidimensional data for structure operation, it determines the type of the operation instruction.

[0075] As a result of the determination in step S22, if the operation instruction is an instruction to increase the data constituting the destination multidimensional cube, the multidimensional database management unit 16 executes the processes of steps S23a to S23d below.

[0076] In step S23a, data whose type and attribute values satisfy the conditions are extracted from the data representing the structure of the events that make up the multi-dimensional cube of the generation destination.

[0077] In step S23b, "extract the identifier of the event that is the information source of the data to be added". At this time, the identifier of any event may be selected.

[0078] In step S23c, data with the event of the extracted identifier as the information source is extracted from the multi-dimensional cube of the generation source, and the data to be added is selected based on the values of the data representing the time dimension, space dimension, unique dimension, and characteristics.

[0079] In step S23d, the selected data is added to the multi-dimensional cube of the generation destination.

[0080] Conversely, if the operation instruction is an instruction to decrease the data constituting the multi-dimensional cube of the generation destination as a result of the determination in step S22, the multi-dimensional database management unit 16 executes the processes of the following steps S24a to S24d.

[0081] In step S24a, data (rows) whose type and attribute values satisfy the conditions are extracted from the data representing the structure of the events that make up the multi-dimensional cube of the generation destination.

[0082] In step S24b, "extract the identifier of the event that is the information source of the data to be deleted". At this time, the identifier of any event may be selected.

[0083] In step S24c, data with the event of the extracted identifier as the information source is extracted from the multi-dimensional cube of the generation destination, and the data to be deleted is selected based on the values of the data representing the time dimension, space dimension, unique dimension, and characteristics.

[0084] In step S24d, the selected data is deleted from the multi-dimensional cube of the generation destination.

[0085] In step S25, after the processing of steps S23a to S23d or after the processing of steps S24a to S24d, the multidimensional database management unit 16 returns the operation result to the structure operation execution unit 14.

[0086] The operations of the flowchart in FIG. 15 will be described on the premise of the state in FIG. 10 in the same manner as the operations of the flowchart in FIG. 9. Since the processes of steps S21, S22, S23a, S23b, S24a, S24b, and S25 in the flowchart of FIG. 15 are the same as the processes of steps S11, S12, S13a, S13b, S14a, S14b, and S15 in the flowchart of FIG. 9, respectively, the processes of steps S23c, S23d, S24c, and S24d in the flowchart of FIG. 15 will be described.

[0087] First, on the premise of the state in FIG. 10, when increasing the data constituting the multidimensional cube to be generated, from the state where the operations of steps S23a and S23b are completed and the "identifier of the event that is the information source of the data to be added" is extracted, in step S23c, the data to be added is selected, and in step S23d, an example of the operation of adding the selected data to the multidimensional cube to be generated will be described using a schematic diagram.

[0088] FIG. 16 is a schematic diagram for explaining an example of the operation when the data representing the time dimension, space dimension, specific dimension, and characteristics constituting the multidimensional cube is stored as tabular data normalized in the same manner as in FIG. 10. The upper part (a) of FIG. 16 shows tabular data representing the time dimension, space dimension, specific dimension, and characteristics, and the lower part (b) of FIG. 16 shows tabular data representing the structure of the event.

[0089] For the data representing the time dimension, space dimension, specific dimension, and characteristics shown in the upper part (a) of FIG. 16, the multidimensional database management unit 16 executes the processes of steps S23c and S23d as follows. In step S23c, data (rows) that satisfy the conditions are selected from the source table. The condition for the event identifier is that it is an element of the "event identifier that is the information source of the data to be added" that has already been extracted, and the condition for the data representing the time dimension, space dimension, specific dimension, and characteristics is that the data representing the characteristics is within the specified range (f31 or f91). In step S23d, the selected data (rows) are added to the destination table.

[0090] For the data representing the structure of the event shown in the lower part (b) of FIG. 16, the multidimensional database management unit 16 executes the processes of steps S23c and S23d as follows. In step S23c, the "event identifier that is the information source of the data to be added" is replaced with the set of event identifiers of the data (rows) selected above, and data (rows) representing the structure with the event of the identifier as the information source are selected from the source table. In step S23d, the selected data (rows) are added to the destination table. As a result, the structure is the same as that in FIG. 12.

[0091] FIG. 17 is a schematic diagram for explaining an operation example when data representing the time dimension, space dimension, specific dimension, and characteristics that constitute a multidimensional cube are stored as data in a normalized table format different from that in FIG. 10. The upper part (a) of FIG. 17 shows table format data representing the time dimension, space dimension, specific dimension, and characteristics, and the lower part (b) of FIG. 17 shows table format data representing the structure of the event.

[0092] For the data representing the time dimension, space dimension, specific dimension, and characteristics shown in the upper part (a) of FIG. 17, the multidimensional database management unit 16 executes the processes of steps S23c and S23d as follows. Here, step S23c has sub-steps S23c-1 to 3.

[0093] In sub-step S23c-1, denormalize the table of the generator and select data (rows) that satisfy the conditions. In sub-step S23c-2, create a table consisting of the identifier of the event and the identifiers of the data representing the time dimension, space dimension, unique dimension, and characteristics. The condition for the identifier of the event is that it is an element of the "identifier of the event that is the information source of the data to be added" that has been extracted, and the condition for the data representing the time dimension, space dimension, unique dimension, and characteristics is that the data representing the characteristics is within the specified range (f31 or f91).

[0094] Furthermore, in sub-step S23c-3, select data (rows) having the "identifiers of the data representing the time dimension, space dimension, unique dimension, and characteristics" of the table created above from the table of the normalized data representing the time dimension, space dimension, unique dimension, and characteristics of the generator. Note that instead of creating a table consisting of the identifier of the event and the identifiers of the data representing the time dimension, space dimension, unique dimension, and characteristics as described above, it is also possible to denormalize the table of the generator each time and select data (rows) that satisfy the conditions for each table of the data representing the time dimension, space dimension, unique dimension, and characteristics.

[0095] In step S23d, add the selected data (rows) to the table of the normalized data representing the time dimension, space dimension, unique dimension, and characteristics of the destination.

[0096] The operation example shown in the upper part (a) of FIG. 17 explains the table of the time dimension, but the same applies to the tables of the data representing the space dimension, unique dimension, and characteristics.

[0097] For the data representing the structure shown in the lower part (b) of FIG. 17, the operation is the same as when storing it as denormalized table-form data. After creating a table by the processes of sub-steps S23c-1 and S23c-2 above, in sub-step S23c-3, replace the "identifier of the event that is the information source of the data to be added" with the set of "identifiers of the event" of the created table, and select data (rows) representing the structure having the event of the identifier as the information source from the table of the generator. In step S23d, add the selected data (rows) to the table of the destination. As a result, the structure is the same as that in FIG. 12.

[0098] Next, on the premise of the state when it is as shown in FIGS. 16 and 17, when reducing the data constituting the generated multidimensional cube, from the state where the operations of steps S24a and S24b are completed and the "identifier of the event that is the information source of the data to be deleted" is extracted, in step S24c, the data to be deleted is selected, and in step S24d, an operation example of deleting the selected data from the generated multidimensional cube will be described using a schematic diagram.

[0099] FIG. 18 is a schematic diagram for explaining an operation example when data representing a time dimension, a space dimension, an eigen dimension, and characteristics constituting a multidimensional cube is stored as tabular data normalized in the same manner as in FIG. 16. The upper part (a) of FIG. 18 shows tabular data representing the time dimension, the space dimension, the eigen dimension, and the characteristics, and the lower part (b) of FIG. 18 shows tabular data representing the structure of the event.

[0100] For the data representing the time dimension, the space dimension, the eigen dimension, and the characteristics shown in the upper part (a) of FIG. 18, the multidimensional database management unit 16 executes the processes of steps S24c and S24d as follows. In step S24c, data (rows) that satisfy the conditions are selected from the generated table. The condition for the event identifier is that it is an element of the already extracted "identifier of the event that is the information source of the data to be deleted", and the condition for the data representing the time dimension, the space dimension, the eigen dimension, and the characteristics is that the data representing the characteristics is within the specified range (f31 or f91). In step S24d, the selected data (rows) are deleted from the generated table.

[0101] For the data representing the event structure shown in the lower part (b) of FIG. 18, the multidimensional database management unit 16 executes the processes of steps S24c and S24d as follows. In step S24c, the set of event identifiers of the data (rows) selected above is replaced with the "identifier of the event that is the information source of the data to be deleted", and from the destination table, data (rows) representing the structure with the event of the identifier as the information source are selected. In step S24d, the selected data (rows) are deleted from the destination table. As a result, the structure is the same as that in FIG. 14.

[0102] FIG. 19 is a schematic diagram for explaining an operation example when data representing a time dimension, a space dimension, a specific dimension, and characteristics that constitute a multidimensional cube are stored as table-form data normalized in the same manner as in FIG. 17. The upper part (a) of FIG. 19 shows table-form data representing the time dimension, the space dimension, the specific dimension, and the characteristics, and the lower part (b) of FIG. 19 shows table-form data representing the event structure.

[0103] For the data representing the time dimension, the space dimension, the specific dimension, and the characteristics shown in the upper part (a) of FIG. 19, the multidimensional database management unit 16 executes the processes of steps S24c and S24d as follows. Here, step S24c has sub-steps S24c-1 to 3.

[0104] In sub-step S24c-1, the destination table is denormalized to select data (rows) that satisfy the conditions. In sub-step S24c-2, a table is created from the event identifier and the identifiers of the data representing the time dimension, the space dimension, the specific dimension, and the characteristics. The condition for the event identifier is that it is an element of the "identifier of the event that is the information source of the data to be deleted" that has been extracted, and the condition for the data representing the time dimension, the space dimension, the specific dimension, and the characteristics is that the data representing the characteristics is within the specified range (f31 or f91).

[0105] Furthermore, in sub-step S24c-3, data (rows) having the "identifier of data representing the time dimension, space dimension, specific dimension, and characteristics" in the table created above are selected from the table of data representing the normalized time dimension, space dimension, specific dimension, and characteristics at the generation destination. Note that instead of creating a table consisting of the event identifier and the identifier of data representing the time dimension, space dimension, specific dimension, and characteristics as described above, it is also possible to non-normalize the source table each time and select data (rows) that satisfy the conditions for each table of data representing the time dimension, space dimension, specific dimension, and characteristics.

[0106] In step S24d, the selected data (rows) are deleted from the table of data representing the normalized time dimension, space dimension, specific dimension, and characteristics at the generation destination.

[0107] The operation example shown in the upper part (a) of FIG. 19 describes the table of the time dimension, but the same applies to the tables of data representing the space dimension, specific dimension, and characteristics.

[0108] For the data representing the structure shown in the lower part (b) of FIG. 19, the operation is the same as when it is stored as non-normalized table format data. After creating the table by the processing of the above sub-steps S24c-1 and S24c-2, in sub-step S24c-3, the "identifier of the event that is the information source of the deletion data" is replaced with the set of "event identifiers" in the created table, and data (rows) representing the structure with the event of the identifier as the information source are selected from the table at the generation destination. In step S24d, the selected data (rows) are deleted from the table at the generation destination. As a result, the structure is the same as that in FIG. 14.

[0109] FIG. 20 is a block diagram showing an example of the hardware configuration of the data analysis processing apparatus 10 according to the embodiment. As shown in FIG. 20, the data analysis processing apparatus 10 includes a processor 22, a memory 24, a storage 26, and an interface unit 28. That is, the data analysis processing apparatus 10 is configured by a computer and is configured by, for example, a personal computer or a server computer.

[0110] The processor 22 is composed of arithmetic units such as a Central Processing Unit (CPU) or a Micro Processing Unit (MPU), for example.

[0111] The memory 24 is a main storage device and has, for example, a RAM (Random Access Memory) and a ROM (Read Only Memory). The ROM stores programs and information necessary for basic processing of the processor 22. The RAM temporarily stores programs and information necessary for the processing executed by the processor 22.

[0112] The storage 26 is an auxiliary storage device and is composed of non-volatile storage media such as a HDD (Hard Disk Drive) or an SSD (Solid State Drive), for example. The storage 26 stores the multidimensional database 18 and the data analysis processing program 32. The data analysis processing program 32 is a program that causes the processor 22 to execute the functions of the OLAP operation execution unit 12, the structure operation execution unit 14, and the multidimensional database management unit 16. The source of the data analysis processing program 32 is, for example, an optical disk or a network.

[0113] The processor 22 operates as the OLAP operation execution unit 12, the structure operation execution unit 14, and the multidimensional database management unit 16 by reading the data analysis processing program 32 from the storage 26 into the memory 24 and executing it.

[0114] The OLAP operation execution unit 12, the structure operation execution unit 14, and the multidimensional database management unit 16 may be composed of various other elements instead of being composed of the processor 22 and the memory 24.

[0115] For example, the interface unit 28 is connected to the network 50. Thereby, the data analysis processing device 10 can receive an operation instruction from the client 40 via the network 50 and provide a processing result to the client 40.

[0116] (Effect) As described above, the data analysis processing apparatus 10 associates data representing real-world events with event identifiers for identifying the events that are the information sources of those data, and accumulates and manages them in a multidimensional cube constructed for each subject together with data representing the structure of the events. Specifically, the data analysis processing apparatus 10, for the source multidimensional cube and the destination multidimensional cube in an OLAP operation, based on the data of the source multidimensional cube as the original and the type and attribute values of the data representing the structure of the events that make up the destination multidimensional cube, increases or decreases the data that makes up the destination multidimensional cube.

[0117] In some cases, the data analysis processing apparatus 10, in addition to the type and attribute values of the data representing the structure of the events that make up the destination multidimensional cube, also bases on the values of the data representing the time dimension, space dimension, unique dimension, and characteristics that make up the source multidimensional cube, and increases the data that makes up the destination multidimensional cube.

[0118] Also, the data analysis processing apparatus 10, in addition to the type and attribute values of the data representing the structure of the events that make up the destination multidimensional cube, also bases on the values of the data representing the time dimension, space dimension, unique dimension, and characteristics that make up the destination multidimensional cube, and decreases the data that makes up the destination multidimensional cube.

[0119] In this way, by focusing on the structure of the events that are the information sources of the data, it is possible to increase or decrease the data that makes up the multidimensional cube to be analyzed. As a result, when analyzing the data that makes up the multidimensional cube, it becomes possible to analyze the data while focusing on the structure of the events that are the information sources of the data.

[0120] Note that the present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the gist thereof at the implementation stage. Also, the respective embodiments may be implemented in appropriate combination, and in that case, the combined effects can be obtained. Furthermore, the above embodiments include various inventions, and various inventions can be extracted by combinations selected from a plurality of disclosed constituent elements. For example, even if some constituent elements are deleted from all the constituent elements shown in the embodiments, if the problem can be solved and the effects can be obtained, the configuration from which these constituent elements are deleted can be extracted as an invention.

Explanation of Reference Numerals

[0121] 10…Data analysis processing device 12…OLAP operation execution unit 14…Structure operation execution unit 16…Multidimensional database management unit 18…Multidimensional database 22…Processor 24…Memory 26…Storage 28…Interface unit 32…Data analysis processing program 40…Client 50…Network

Claims

1. A data analysis processing apparatus that maps data representing a real-world event that changes in at least one of time and space due to at least one of generation / elimination and state transition to a multi-dimensional cube and analyzes it by an online analysis processing operation, Time-dimensional data and space-dimensional data that represent temporal and spatial changes due to at least one of the generation / elimination and state transition as data representing the event, data of a plurality of types of specific dimensions depending on a theme, and data representing a plurality of types of characteristics of the event depending on the theme identified by the time-dimensional data, the space-dimensional data, and the specific-dimensional data are associated with an identifier of the event for identifying the event that is the information source of these data, and the structure between two of the events is represented by two identifiers of the events, the type of the structure between the two events, and information on attributes, together with data representing the structure of the event, as the multi-dimensional cube constructed for each theme, is stored in a multi-dimensional database and a multi-dimensional database management unit that manages the multi-dimensional database, An online analysis processing operation execution unit that causes the multi-dimensional database management unit to refer to / sum up data constituting the existing multi-dimensional cube or generate a new multi-dimensional cube by using an argument instructed from a client or data constituting another multi-dimensional cube, A structure operation execution unit that causes the multi-dimensional database management unit to increase or decrease data constituting the multi-dimensional cube by using data representing the structure of the event constituting the multi-dimensional cube, A data analysis processing apparatus comprising the above.

2. The structure operation execution unit, with respect to the multi-dimensional cube of the generation source and the multi-dimensional cube of the generation destination in the online analysis processing operation, based on the data of the multi-dimensional cube of the generation source as the original and the type and attribute values of data representing the structure of the event constituting the multi-dimensional cube of the generation destination, causes the multi-dimensional database management unit to increase or decrease data constituting the multi-dimensional cube of the generation destination, The data analysis processing apparatus according to Claim 1.

3. The structure operation execution unit, with respect to the multi-dimensional cube of the generation source and the multi-dimensional cube of the generation destination in the online analysis processing operation, based on the data of the multi-dimensional cube of the generation source as the original, Based on the type and attribute values of the data representing the structure of the events that make up the multidimensional cube of the generation destination, and the values of the data representing the time dimension, space dimension, unique dimension, and characteristics that make up the multidimensional cube of the generation source, increase the data that makes up the multidimensional cube of the generation destination in the multidimensional database management unit, or Based on the type and attribute values of the data representing the structure of the events that make up the multidimensional cube of the generation destination, and the values of the data representing the time dimension, space dimension, unique dimension, and characteristics that make up the multidimensional cube of the generation destination, decrease the data that makes up the multidimensional cube of the generation destination in the multidimensional database management unit. The data analysis processing apparatus according to claim 1.

4. A data analysis processing method for mapping data representing an event to a multidimensional cube and analyzing it by an online analysis processing operation for a real-world event that changes in at least one of time and space due to at least one of generation / elimination and state transition, Associate the time dimension data and space dimension data that embody the temporal and spatial changes due to at least one of the generation / elimination and state transition, as data representing the event, with the data of a plurality of types of unique dimensions depending on the subject, and the data representing a plurality of types of characteristics of the event depending on the subject identified by the time dimension data, the space dimension data, and the unique dimension data, with the identifier of the event for identifying the event that is the information source of these data, and represent the structure between two said events with the identifiers of the two said events and the information on the type and attributes of the structure between the two said events. Accumulate and manage in the multidimensional cube constructed for each subject together with the data representing the structure of the event. Refer to / sum up the data that makes up the existing multidimensional cube, or generate a new multidimensional cube, using the arguments instructed by the client or the data that makes up other multidimensional cubes. Increase or decrease the data that makes up the multidimensional cube using the data representing the structure of the event that makes up the multidimensional cube. A data analysis processing method executed by a computer, comprising:

5. A data analysis processing program that causes a computer to execute the functions of each component of the data analysis processing apparatus according to any one of claims 1 to 3.

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