WO2015173854A1 - 情報処理システムおよび情報処理方法 - Google Patents
情報処理システムおよび情報処理方法 Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/30—Creation or generation of source code
- G06F8/35—Creation or generation of source code model driven
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
Definitions
- the present invention relates to an information processing system and an information processing method for executing graph processing.
- the above-mentioned distributed data is composed of environmental sensing data such as temperature and humidity, log data related to machines such as automobiles, and log data related to humans and organizations such as mail and SNS.
- the processing content of such distributed data is optimally arranged with clustering processing that classifies the data and adds labels and indexes, machine learning processing, and elements (people, things, information, etc.) that constitute society.
- Control processing The processing results regarding the distributed data obtained by these processes are expanded to distributed users and control targets.
- the user or the control target object determines, for example, the moving means and the moving direction and the control parameter according to the processing result.
- an object of the present invention is to provide a technique that enables efficient calculation of data that cannot be collected in one place on a large scale or that is updated every moment.
- the information processing system of the present invention that solves the above problem is a model of a graph structure that is composed of a plurality of vertices corresponding to an analysis target event and edges connecting the corresponding vertices according to the relationship between the corresponding events.
- a plurality of computers corresponding to each of the vertices and connected to each other so as to be able to exchange data, and an attribute representing one or more states for the event of the vertices stored in each of the computers
- a display device that displays the number of identifier data held for each vertex in the storage device as a spatial distribution map based on the distribution of the vertices.
- the information processing system includes a management computer, a computer installed on each shelf in the warehouse, and a portable computer possessed by each worker who collects the luggage placed on the shelf and can access the computer.
- Each of the computers holds information on the number of types of identifier data corresponding to a predetermined event related to the luggage arranged on the shelf or the corresponding shelf, and access from each portable terminal Receiving the identifier data held by the mobile terminal, transmitting the identifier data determined based on the past fluctuation state of the identifier data already held by the computer to the mobile terminal, and the identifier with the mobile terminal
- the difference argument of the data transmission / reception result is used to update the number of the identifier data of the plurality of types held by the computer.
- the identifier data is received, and the identifier data held by itself is updated by the received identifier data.
- Each computer holds the identifier data after a predetermined time has elapsed since the update of the number of the identifier data.
- An instruction to move the shelf in which the shelf is installed to the placement destination associated with the largest number of the plurality of types of identifier data is output to the management computer.
- the information processing system of the present invention includes a management computer and a computer associated with each data in the data center, and each of the computers includes a plurality of types of identifier data corresponding to a predetermined event related to the data. Is received from the program that uses each of the data, receives the identifier data held by the corresponding program, and is determined based on the past fluctuation status of the identifier data already held by the computer. And the number of the plural types of identifier data held by the computer is updated with a difference argument of a result of transmission / reception of the identifier data with the program. Receives the identifier data from the computer and uses the received identifier data to identify the information held by itself.
- the child data is updated, and each computer is associated with the largest number of the plurality of types of identifier data held by itself after a predetermined time has elapsed since the update of the number of identifier data.
- An instruction to move the data associated with itself to the arranged location is output to the management computer.
- the information processing system includes a plurality of terminals that transmit and receive messages via a network and a management computer, wherein each terminal holds information regarding the number of types of identifier data and transmits the messages.
- each terminal holds information regarding the number of types of identifier data and transmits the messages.
- a message to which the identifier data determined based on the past fluctuation state of the number of identifier data already held is transmitted to another terminal among the plurality of terminals, and the transmitted
- the number of the plurality of types of identifier data held by itself is updated, and when receiving the message, by adding the number of identifier data added to the received message Update the number of the plurality of types of identifier data held by itself, and update the number of the identifier data
- the management computer receives the number of the plurality of types of identifier data from each terminal.
- the information processing method of the present invention uses the graph structure constituted by a plurality of vertices corresponding to the analysis target event and edges connecting the corresponding vertices according to the relationship between the corresponding events as a model.
- a plurality of computers corresponding to each vertex, connected to each other so as to be able to exchange data corresponding to the edge, and holding identifier data having an attribute representing one or more states with respect to the event of the vertex are connected by the edge.
- the transition probability of the identifier data between the computers is calculated by a predetermined algorithm based on the number of identifier data held between the computers corresponding to the adjacent vertices, and the calculation result is Accordingly, the number of identifier data held by each computer is updated, and the number of identifier data held for each vertex is displayed as a spatial distribution map based on the distribution of the vertices. To display at the location, and wherein the
- FIG. 3 is a diagram illustrating an example of a graph structure model to be analyzed in the first embodiment.
- 3 is a conceptual diagram of a graph structure model to be analyzed in Example 1.
- FIG. 1 is a network configuration diagram including an information processing system in Embodiment 1.
- FIG. 6 is a flowchart illustrating a procedure example 1 in the information processing method according to the first embodiment.
- FIG. 6 is a flowchart illustrating a procedure example 2 in the information processing method according to the first embodiment.
- FIG. 6 is a flowchart illustrating a procedure example 3 in the information processing method according to the first embodiment.
- FIG. 10 is a flowchart illustrating a procedure example 4 in the information processing method according to the first exemplary embodiment.
- FIG. 6 is a diagram illustrating a more specific calculation model example in Embodiment 1.
- FIG. FIG. 6 is a diagram illustrating a configuration example of a computer in an information processing system according to a second embodiment. It is a figure which shows graph structure data in case there exists a weight in the edge of the calculation model in Example 2.
- FIG. It is a figure which shows the relationship table
- FIG. It is a figure which shows the example of transition time of the information element in Example 2.
- FIG. It is a figure which shows the acquisition conceptual example 1 of the graph structure data according to the real social activity in Example 3.
- FIG. 10 is a diagram illustrating a configuration example of an information processing system according to a third embodiment.
- FIG. 10 is a flowchart illustrating a procedure example 1 in the information processing method according to the third embodiment.
- FIG. 10 is a flowchart illustrating a procedure example 2 in the information processing method according to the third embodiment.
- FIG. 10 is a flowchart illustrating a procedure example 3 in the information processing method according to the third embodiment.
- FIG. 10 is a flowchart illustrating a procedure example 4 in the information processing method according to the third embodiment.
- FIG. 10 is a diagram illustrating a configuration example of an information processing system according to a fourth embodiment.
- FIG. 10 is a diagram illustrating a configuration example of an information processing system according to a fourth embodiment.
- FIG. 10 is a diagram illustrating an example of an access chart to process data blocks according to the fourth embodiment.
- FIG. 10 is a diagram illustrating an example of a process relationship in the fourth embodiment.
- FIG. 10 is a diagram illustrating an outline of an information processing system according to a fourth embodiment.
- FIG. 10 is a diagram illustrating an example of an access chart of a process in the fourth embodiment.
- FIG. 10 is a diagram illustrating an example of processing on an information element storage area corresponding to a data block according to a fourth embodiment. It is a figure which shows the example of a time change of the number of the information elements which the storage area of each information element in Example 4 preserve
- FIG. 10 is a diagram illustrating a conceptual example of a pickup work in the fifth embodiment. It is a figure which shows the example of the mobile terminal which the computer with which the shelf in Example 5 is equipped, and an operator hold
- FIG. 10 is a diagram illustrating a hardware configuration example of a computer and a mobile terminal according to a fifth embodiment. It is a figure which shows the flow concept of the information processing method in Example 5.
- FIG. FIG. 20 is a diagram illustrating a table describing a relationship between a computer provided in each shelf and a destination in Example 5.
- FIG. 10 is a diagram illustrating an example of a flowchart of a computer according to a fifth embodiment.
- FIG. 10 is a diagram illustrating an example of a flowchart regarding calculation result acquisition processing according to the fifth embodiment.
- FIG. 10 is a diagram illustrating a conceptual example in Example 6. It is a figure which shows the exchange example of the information element in Example 6. FIG. It is a figure which shows the example of a concept which specifies the affiliation community of each user from the information child number of each vertex in Example 6.
- FIG. 10 is a diagram illustrating a conceptual example in Example 7. It is a figure which shows Example 1 of the transition probability table
- FIG. 1 shows a conceptual diagram of a graph structure model (hereinafter, calculation model 1) to be analyzed in the information processing system.
- the calculation model 10 performs calculation by diffusing information elements, which are information units, with respect to a graph structure composed of vertices 110 to 114 and edges 120 connecting these vertices. It is a calculation model with the number of information elements as a solution.
- the calculation object is a classification problem.
- an information element that is a unit of information
- the information element is data having a state variable.
- the number of states 2 (that is, the data capacity is 1 bit), and the state u and the state v, respectively.
- FIG. 1 illustrates an information element 130 in a state u and an information element 131 in a state v.
- Each information element diffuses along side 12 (information element diffusion 140). The calculation result can be obtained from the number of information elements on each vertex. From the table 150 (see FIG.
- the vertex A vertex 110
- the vertex A vertex 110
- the number of information elements ( State v) is maximized.
- the maximum information element state corresponds to the classification result, that is, if the state u is the classification result A and the state v is the classification result B, the vertex A becomes the classification result B.
- FIG. 3 is a diagram illustrating a network configuration example including the information processing system 100 according to the present embodiment.
- the information processing system 100 shown in FIG. 3 includes one or more computers 220-1 to 220-4, and these computers are connected by a network 1.
- the computer 220 will be referred to unless the computer is particularly distinguished.
- the computer 220 includes a CPU 221, a main storage device 222 configured by a volatile storage device such as a RAM, a storage 223 configured by an appropriate nonvolatile storage device such as a hard disk drive, an input / output device 224 such as a keyboard, a mouse, and a display, A network I / F 225 is included.
- the CPU 221 executes a program 226 held in the main storage device 222 to implement a necessary function, performs overall control of the computer itself, and performs various determinations, calculations, and control processes. Therefore, the function corresponding to the information processing method of the present embodiment corresponds to a function implemented by executing the program 226 on the computer 220 described above.
- FIG. 4 is a flowchart showing a procedure example in the information processing method of the present embodiment.
- each of the computers 220-1 to 220-4 stores the data related to the corresponding vertex in the graph structure model 10, which is divided for each computer and stored in its own storage 223.
- data stored in the storage 223 by the computer 220-1 is stored in a predetermined data area 230-1 in the storage 223, as exemplified in FIG.
- a graph structure model 10 similar to that in FIG. 1 is assumed, and the same symbols are given.
- the vertex 110 that is the vertex A, the vertex 111 that is the vertex B, and the connection destination information of each vertex that is, the vertex 110 that is the vertex A is the vertex 113 that is the vertex D
- the vertex 111 that is the vertex B is The information connected to the vertex 110 that is the vertex A, the vertex 112 that is the vertex C, and the vertex 113 that is the vertex D is stored.
- each computer 220 executes a certain number of loop processes (steps 313-1 to 313-2).
- Each computer 220 executes loop processing (steps 314-3 to 314-2) for all the vertices acquired in step 311 in the loop processing, and receives information element reception processing (step 315).
- a transmission process (step 316) is executed.
- each computer 220 executes a calculation result acquisition process (step 317) and ends this flow.
- each computer 220 determines whether an information element has been received from another vertex after the start of the flow (step 411). If this determination statement is true (step 411: Y), the computer 220 updates the number of information elements holding data in the storage 223 (step 412), and if the above determination is false (step 411: N ), This flow ends.
- FIG. 6 shows a flowchart of the transmission process (step 316).
- Each computer 220 acquires the number of information elements on vertices (referred to as adjacent vertices) connected by edges to the vertices selected in the above loop processing (step 314) for the vertices after the start of the flow. (Step 512). Note that the number of information elements obtained here may be the number of information elements acquired in the past.
- the computer 220 executes loop processing (steps 513-1 to 513-2) for the information element on the vertex selected in step 314 described above.
- the computer 220 calculates a transition probability for the information element selected in step 314 described above in the loop (step 514).
- a formula for calculating the transition probability is shown below as Formula 1.
- Ndj is the degree of vertex j.
- the left side P indicates the transition probability
- Pu indicates the transition probability of the information element in the state u.
- Nu and Nv represent the number of information elements.
- Nu is the number of information elements in state u
- Nv is the number of information elements in state v.
- ⁇ in the third term and the fourth term is calculated for devices in the vicinity range.
- Nui and Nvj are the number of information elements in each state of the device j within the vicinity range.
- F and g are functions, and ⁇ and ⁇ are positive constants.
- the computer 220 may calculate the transition probability by prediction based on the update history of past information elements.
- the computer 220 in such a prediction method uses a test pattern as a correct answer data in advance and inputs a test history update history and the number of vertices as input data. It is assumed that the model learned in this way is used for calculating the transition probability. By using this model, the computer 220 can replace the number of information elements at adjacent vertices with the update history of past information elements.
- step 515 the computer 220 compares the transition probability calculated in step 514 described above with a predetermined threshold.
- a predetermined threshold for example, when the threshold value is 0.5 and 0.5 ⁇ transition probability ⁇ 1 (step 515: Y), that is, the computer 220 that has recognized that the probability is high advances the processing to step 516.
- the computer 220 recognizes that the transition probability is low, and proceeds to the end (step 520).
- step 516 the computer 220 selects one vertex from the adjacent vertices for which the number of information elements has been obtained in step 512.
- the selection method here may be random or round robin.
- step 517 the computer 220 transmits the data (state) of the information element from the network I / F 125 to the adjacent vertex (corresponding computer) selected in step 516 described above.
- step 518 the computer 220 updates the number of information elements at the vertex (executes the number of information elements ⁇ 1 because the information element is transmitted).
- step 317) in the flow of FIG. 4 is shown in FIG.
- the computer 220 compares the number of information elements of each state of each vertex held by the storage 223 in Step 611 and selects the state having the maximum number of information elements. For example, when the number of states is 2 (u and v), the number of information elements in the state u on a certain vertex is 1, and the number of information elements in the state v is 2, the computer 220 selects the state v.
- step 612 the computer 220 displays the result corresponding to the state selected in step 611 described above on a predetermined display device or input / output device 224 on the network 1.
- the result corresponding to the state v is the classification result B
- the vertex belongs to the classification B (the state u is the community A).
- the other vertices are known in the same way.
- the calculation result does not depend on the calculation order of the information elements.
- the vertex loop (step 314-1) and the information child loop (513-1) on the vertex are free in the calculation order of the vertex and information child,
- the calculation result does not change when a certain vertex A is processed and then the vertex B is processed, and when the vertex B is processed and then the vertex A is processed. That is, the calculation order is free.
- FIG. 8 shows an outline of the calculation order when the calculation model 10 and the information processing system 100 of FIG. 3 are assumed.
- FIG. 8 shows two flows having different calculation orders, which are described as processing order 1 and processing order 2, respectively. Further, the movement of data (information element) due to transmission / reception between vertices shows only movement related to the vertex D.
- the information element moves between the vertices.
- the transmission process of the same period as the vertex D of the vertex A and the transmission process of the same period as the vertex D of the vertex E are performed. Since the transmission processing of the same period has not been executed yet, in the reception processing of the vertex D, the movement 710 of the same period of information from the vertex A, the movement 712 of the previous period of information from the vertex B, and the vertex An information element move 711 of the same period from E occurs.
- FIG. 9 A more specific example is shown in FIG.
- the example shown in FIG. 9 is a calculation model having 4096 vertices.
- the sides are not shown because the figure becomes complicated.
- the vertices are dense near the center of the circle, and the density of the vertices decreases toward the outer periphery.
- the distance between the vertices near the outer periphery of the circle is larger than the gap near the center of the circle. For this reason, when the classification based on the distance threshold, which is one of the conventional statistical methods, is performed, the wavy gap cannot be recognized and cannot be correctly classified.
- the method of the present invention performs classification in an autonomous and distributed manner, a gap in a high-density region near the center and a low-density gap near the outer periphery can be correctly recognized.
- the number of states of the information element is 2, and in initialization of the number of information elements, the information group (state u) is stored in the vertex group 810-1 and the information field (state is stored in the vertex group 810-2). Assume that 4096 ⁇ 8 are assigned to v). Further, at each vertex in FIG. 9, the density (color tone density from white to gray to black) is changed depending on whether the number of information elements is large in the state u and the state v is large.
- the configuration example of the computer 910 in the information processing system in this case is as shown in FIG.
- the computer 910 shown here corresponds to each of the computers 220-1 to 220-4 shown in FIG. 3, and modules having the same function are given the same symbols.
- the computer 910 according to the second embodiment newly includes a delay device 911 with respect to the computers 220-1 to 220-4.
- graph structure data 920 when the sides of the calculation model have weights is shown in FIG.
- the graph structure data 920 includes a vertex A950, a vertex B951, and a vertex C952, a weight 960 having a value of 1 for the side between the vertices AB, and a value of 10 for the side between the vertices AC. Assume that a weight 961 exists.
- the computer 910 delays the transition of the information element by the delay unit 911 according to the weight 960 described above.
- FIG. 12 shows a table 940 that defines the relationship between the edge weight and the delay time.
- the relationship between the weight and the delay time may be given in advance in Table 940 as shown in FIG. 12, or may be calculated by the computer 910 using a predetermined mathematical formula.
- Example 3 Next, an example of an information processing system having a function of automatically acquiring in accordance with real-world activities in the step 311 in the flow of FIG.
- the graph structure data which is a calculation model, is not calculated on the information processing system, and real-world activities are used as they are.
- FIG. 14 A specific example is shown in FIG. In FIG. 14, for example, when a human conversation is assumed as a real-world activity, the graph structure data as a calculation model is obtained from a log of a person's conversation (who and who, how many times, how long, and a conversation). Structured.
- the computer 220 that obtains the recorded data has the graph structure data 1010 expressing the frequency and time of the conversation.
- a side 1013 is generated between the vertex A (1011) corresponding to the person A and the vertex B (1012) corresponding to the person B.
- Computer 220 thus performs structuring 1030.
- real-world activities are not limited to human conversation, but also activities between objects (for example, communication between machines such as robots, automobiles, traffic lights, etc.), activities between persons and objects, and objects through persons.
- Activities indirect communication between places and facilities via people who visit multiple facilities and shelves
- SNS user interaction messages communication, e-mail, etc.
- the computer 220 structures the activity in the real world into graph structure data, and then analyzes it in the same manner as the processing exemplified in the first embodiment.
- FIG. 15 shows a state 1050 in which information elements are exchanged in association with activities in the real world. If the person A and person B described above can hold an information child and can update the information child during a conversation between the person A and the person B, the computer 220 executes a calculation 1070 using real-world activities to calculate the information child. As an analysis result 1060 as a result, the number of information elements held in the real world, that is, person A and person B is obtained. In Examples 3 to 6, specific configurations corresponding to this concept will be described.
- FIG. 16 shows a configuration example of the information processing system 100 in the third embodiment.
- the information processing system 100 illustrated here includes a device group 3001 and a device 3020 held or mounted at the apex.
- the device group 3001 is a group of people in the real world, and each device is a smart device held by a person or the like.
- the device group is not limited to a person but may be a mobile object such as a car, a smartphone attached to a machine, a device such as an embedded computer and a program attached to data.
- the problem to be analyzed is a vertex group classification problem. When each vertex is a person, for example, it is applied to community detection of a certain group.
- the computer device 3020 includes a CPU 3021, a main storage device 3022 that holds a program 3026, a storage 3023, an input / output device 3024, and a network I / F 3025.
- the device 3020 has a neighborhood range 3030 that is recognized as communicable. In the example of FIG. 16, in the device 3010 associated with a person or a device, a circle having a radius with a predetermined value centered on the device 3010 is the neighborhood range 3030.
- Such a proximity range 3030 can be calculated from the physical distance between devices, but the reach of radio waves transmitted by the network I / F 3025, the frequency of communication between devices (that is, between vertices having devices), for example, mail
- the number of exchanges may be in a range above a certain threshold.
- the device 3020 can communicate within the vicinity range 3030 using a network I / F 3025 or the like.
- the vertex 3010 has a function of communicating with another device 3011 existing in the vicinity range 3030. Since the devices existing in the neighborhood range 3030 correspond to the vertices connected at the sides shown in the first embodiment, the actual social activities themselves become the sides. That is, the graph structure data of the first embodiment is not necessary.
- the device 3020 is called a vertex.
- FIG. 17 is a flowchart illustrating a processing procedure in the information processing method according to the third embodiment.
- the information element which is a unit of information is the same as that defined in the first embodiment.
- each device 3020 first initializes the number of information elements of the corresponding vertex (step 3111).
- step 3115 the device 3020 activates a calculation result acquisition process.
- the device 3020 may execute the reception process (step 3112), the transmission process (step 3113), and the calculation result acquisition process (step 3114) in parallel.
- FIG. 18 shows a flowchart of the process.
- the device 3020 determines whether or not a predetermined time has elapsed in step 3211 after the process starts. If the determination statement is true (step 3211: Y), the device 3020 moves the process to step 3212.
- step 3212 the device 3020 determines whether an information element has been received from another device. If the determination statement is true (step 3212: Y), the number of information elements of the device itself is updated in step 3213.
- the process ends with an end interrupt or the like. Further, the process may be triggered by communication instead of elapse of a fixed time.
- step 3113 The main difference between the transmission process in the third embodiment and the transmission process in the first embodiment is the addition of a process after a certain period of time.
- FIG. 19 shows a flowchart of the process.
- the device 3020 determines in step 3311 whether a predetermined time has elapsed. If this determination statement is true (step 3311: Y), the device 3020 moves the process to step 3312. In step 3312, the device 3020 communicates with the vertex existing in the neighborhood range 3030 described above, and acquires the number of information elements held by the vertex.
- Subsequent steps 3313-1 to 3313-2 are loop processing for each information element of the own vertex.
- the device 3020 calculates the transition probability of the self information element. This step is the same as the processing in step 514 of the first embodiment.
- step 3315 the device 3020 compares the transition probability calculated in step 3314 described above with a predetermined threshold value. If the result is true (step 3315: Y), the process proceeds to step 3316. For example, when the threshold value is 0.5 and 0 ⁇ transition probability ⁇ 0.5, the device 3020 advances the process to step 3311. On the other hand, if 0.5 ⁇ transition probability ⁇ 1, the process advances to step 3316. .
- step 3316 the device 3020 selects one vertex from the vertices in the neighborhood range 330.
- the selection method may be random or order.
- step 3317 the device 3020 transmits the data (status) of the self-identifier from the network I / F 3025 to the vertex (device) selected in step 3316 described above.
- step 3318 the device 3020 updates the number of information elements of its own vertex (since the information element has been transmitted, the number of information elements-1 is executed). Also, the parameters of these processes (for example, coefficients of transition probability calculation formulas, threshold values, selection methods, and the like) may be different at the vertices.
- FIG. 20 shows a flowchart of calculation result acquisition processing.
- the device 3020 determines whether there is a result acquisition request from the input / output device 3024 in step 3411 after the calculation result acquisition processing is started. If this determination is true (step 3411: Y), the device 3020 executes step 3412 and step 3413. This step is the same as step 611 and step 612 in the first embodiment, and a description thereof will be omitted.
- the classification problem for the vertices can be solved as in the first embodiment. Since the vertex in the third embodiment is a device distributed in the real world, that is, the classification problem for the device distributed in the real world can be efficiently solved without generating graph structure data. --- Example 4 --- Next, a fourth embodiment is shown as an example of an information processing system corresponding to a calculation model in which each vertex in the above-described third embodiment is data and an edge between the vertices is an access continuity between the data. The fourth embodiment provides a method for efficiently arranging data necessary for each process in a computer when a plurality of processes are processed on a plurality of computers.
- the computer 120-1 and the computer 120-2 are connected via the network 1, the process 120 is processed by the computer 120-1, and the data area of the computer 120-1 Assume that data blocks 1, 2, and 3 are stored in 130-1. Further, it is assumed that the process 2 is processed by the computer 120-2, and the data blocks 4, 5, and 6 are stored in the data area 130-2 of the computer 120-2.
- the above data block stores data necessary for the above processes 1 and 2.
- FIG. 22 shows an example of an access chart 4010 to each data block of each process 1 and 2.
- data blocks adjacent in the time direction in the time section T are related. Specifically, when the process 1 accesses the data block 1 and subsequently accesses the data block 2, the relationship between the data block 1 and the data block 2 is incremented by “+1”.
- FIG. 23 shows an example of the relationship 4020 calculated by accumulating the relationships of the processes.
- “2” of the value (4211) of the data block 1 in the row and the data block 2 in the column is continuously transferred to the data block 2 after the access to the data block 1 in the time interval T described above. This indicates that the number of accesses is “2”.
- the relationship table 4021 in FIG. 23 is represented as a graph, a relationship graph 4023 is obtained.
- the graph 4023 does not represent the side of the number of times “0”.
- the data blocks 1, 2, and 6 are originally the data area of the processing computer of the process 1, that is, the data area (130-1), and the data blocks 3, 4, and 5 are the data area of the processing computer of the process 2, that is, the data This indicates that it is preferably stored in the area (130-2).
- FIG. 24 shows a schematic diagram of an information processing system 4100 according to the fourth embodiment.
- the information processing system 4100 is a detailed diagram of the data area configuration of the information processing system 4000 of FIG.
- the information processing system 4100 has information child storage areas 1 to 6 (4101-1 to 4101-6) corresponding to the data blocks in the data areas 130-1 and 130-2. This storage area has a function of storing one or more information elements.
- FIG. 25 shows an access chart 4200 for each process.
- processing is performed on the information child storage area corresponding to each data block.
- This processing example 4300 is shown in FIG.
- the process 1 accesses the data block 1
- the information child stored in the area 1 is acquired as the process 4201 for the storage area 1 of the information child. That is, the number of information elements is subtracted.
- the process 4202 is performed on the storage area 2 of the information element corresponding to the data block.
- the five information elements acquired in the pre-process 4201 are added to the area 2 (4211).
- an information element is acquired from the area (4212).
- the information element is circulated between the storage areas corresponding to the respective data blocks.
- Data blocks that are highly relevant are classified into the same cluster according to the distribution of information elements. By regularly moving data blocks between data areas of computers according to the distribution of information elements, highly relevant data blocks can be collected in the same computer.
- FIG. 27 shows changes over time in the number of information elements stored in the storage areas 1 to 6 of the information elements.
- a table 4310 shows the initial state of the number of information elements stored in the storage area of each information element.
- the number of states of the information element is 2 (state u and state v)
- the above time change and the number of information elements in the table 4310 are calculated by the equation of state u ⁇ state v.
- the number of information elements in each area is updated every time the data block is accessed, and changes with time as shown in FIG. *
- Example 5 an information processing method when dealing with the problem of rearranging the shelves in the warehouse so as to shorten the flow line distance of the worker who performs the work is shown as Example 5. .
- FIG. 28 the structure of the target warehouse 500 is illustrated.
- the warehouse 500 has a plurality of areas.
- the warehouse 500 in FIG. 28 has four areas, area A (5010-1) to area D (5010-4).
- area A 5010-1
- area D 5010-4
- a plurality of shelves are arranged in the corresponding area.
- the shelf 5011-A1 is illustrated as one of the shelves placed in the area A (5010-A), but it is assumed that a plurality of other shelves are arranged.
- the areas B to D have a plurality of shelves.
- each shelf can hold a plurality of packages.
- an example in which the packages 5012-A1-1 and 5012-A1-2 are arranged on the shelf A-1 (5011-A1) is illustrated.
- a plurality of packages can be arranged on other shelves.
- FIG. 29 shows a conceptual example of the pickup work.
- an operator 5100 picks up a load placed on each shelf according to a predetermined load list 5110.
- the packages to be picked up are the packages 5012-A1-1, 5012-B1-2, 5012-D1-1
- the worker 5100 has the shelves 5011-A1, 5011-B1 on which the packages are placed. , 5011-D1 will be visited.
- the movement route of the worker is like a movement route 5120.
- the order of picking up the luggage is not specified.
- FIG. 30 shows a computer provided on a shelf and a mobile terminal held by an operator.
- a computer 5210 is installed on each shelf, and each worker 5100 holds a mobile terminal 5220.
- a configuration example of the computer 5210 and the mobile terminal 5220 is shown in FIG.
- FIG. 33 shows a table 5400 in which the relationship between the computers 5210 provided in each shelf and the movement destination is described. From this table 5400, shelf A-1 and shelf B-2 are region B, shelf A-2, shelf B-1, and shelf B-3 are region A. After that, each shelf moves according to the above-mentioned destination area. This movement is executed by a shelf self-propelled mechanism that receives an instruction from the computer 5210 or a robot that executes movement of the shelf. Further, the execution timing of such movement may be at night every day or every other day according to an instruction from the computer 5210.
- FIG. 34 shows a flowchart of each computer 5210.
- Each computer 5210 initializes the number of information elements after the flow starts (step 5511). Thereafter, the reception processing (step 5512) and transmission processing (step 5513) processes of the computer 5210 are started. Thereafter, the computer 5210 activates a calculation result acquisition process (step 5514). Each of these processes (steps 5511 to 5513) can be the same as each process (steps 3111 to 3114) described in FIG.
- each computer 5210 compares the number of information elements in each state held by itself, and selects the state having the maximum number of information elements. For example, when the number of states is 2 (u and v), the number of information elements in the state u on a certain vertex is 1, and the number of information elements in the state v is 2, the computer 5210 selects the state v. Thereafter, in step 5612, the computer 5210 determines a movement destination area corresponding to the state selected in step 5611 described above, and displays it on a predetermined display device or its own input / output device 1024 connected via a network or the like. For example, when the state v is the maximum state, the corresponding region is the region B (the state u is the region A).
- the effect of the fifth embodiment is as shown in FIG. FIG. 36 shows the effect of reducing the movement distance of the operator by the above-described simulation of shelf movement by the computer 5210.
- the movement distance in the initial state 5700 is 1, the movement of the worker who performs the pick-up work in the warehouse in the convergence state 5701 when the shelf movement based on the analysis result according to the fifth embodiment is performed.
- the distance was 0.08, and the reduction effect was 92%.
- Example 6 Next, an information processing method when dealing with a problem of detecting user clustering, that is, a community, in an exchange service between a plurality of users such as a social network service will be described as a sixth embodiment.
- each vertex is a user and the side between the vertices is an exchange between users in the above-described third embodiment.
- the exchange corresponds to, for example, mail transmission / reception, message transmission / reception, visit to a personal page, posting, and the like.
- FIG. 37 shows a conceptual example of the sixth embodiment.
- user 1 6011
- user 2 6012
- transmit messages 6020
- the user 1 operates the terminal 1 (6031) that he / she owns, and transmits a message to the user 2 from the message transmission screen 6040, for example.
- This message is delivered to the terminal 2 of the user 2 via the information processing system of the exchange service such as a server, and for example, a message reception screen 6041 is displayed on the corresponding terminal.
- FIG. 38 shows information in the sixth embodiment. An example of child exchange is shown.
- the information element is stored in a terminal held by each user or a storage area of each user of the information processing service providing the above-described exchange service.
- the initial state is state 1 (6101).
- user 1 6011
- the corresponding terminals 6031 and 6032 add an information element to the message.
- An example of the number of information elements 6113 added to the message by the terminals 6031 and 6032 in the state 2 (6102) is shown. In this example, five information elements are added from the terminal 6031 of the user 1.
- the number of information elements 6112 held in the terminal 6032 of the user 2 is updated based on the information element added to the above message.
- the information element circulates between users via messages.
- the community specified in this way can be applied, for example, by extracting frequently used keywords from public text information of users belonging to the same community and marketing to users belonging to the corresponding community. From the above, the user community on the AC service can be detected in this embodiment.
- Example 7 Next, in the warehouse shown in the above-described fifth embodiment, a form for simulating the optimal arrangement of shelves when the luggage list 5110 of each worker 5100 in FIG.
- the optimum arrangement of the shelves is performed in accordance with the movement of the worker 5100 in the warehouse.
- the computer 5210 obtains the luggage list 5110 and then applies. If there is sufficient time until shipment of the package in the package list 5110, the computer 5210 creates graph structure data from the package list 5110 and executes the same calculation as the information processing system 100 shown in the first embodiment. To do.
- the computer 5210 calculates the movement destination of the shelf from the calculation result (classification result) based on the relationship table between the classification result (number of information elements) and the movement destination area shown in the fifth embodiment. After the calculation of the shelf movement destination by the computer 5210, the shelf is arranged according to the movement destination of the shelf calculated by the computer 5210 before the worker 5100 actually enters the package pickup operation in the warehouse.
- the shelf arrangement means is the same as in the fifth embodiment. Therefore, the seventh embodiment corresponds to a method in which the computer 5210 generates graph structure data from one or more package lists 5110.
- FIG. 40 shows a conceptual diagram of the seventh embodiment.
- the trajectory of the movement of the worker 5100 in the warehouse can be like a graph 7010 of the trajectory of the worker 5100.
- the value added to each side of the graph 7010 is the number of times the worker has passed.
- a graph 7011 of the normalized operator trajectory is obtained. This graph 7011 is graph structure data in which a weight is added to each side.
- transition probability tables 8001 to 8003 are shown as transition probability tables 1 to 3, respectively.
- the computer of the information processing system compares the number of information elements (u, v) of its own vertex with the number of information elements ( ⁇ Nju, ⁇ Njv) of its own vertex against the transition probability tables 8001 to 8003, and the corresponding values in the table.
- the transition probability can be determined by specifying.
- the values in each of the above tables 8001 to 8003 are values obtained by experimentally calculating values in which a computer executes a simulation in advance and obtains a target result.
- each computer determines the attribute of the identifier data having the largest number among the identifier data held by itself with respect to the vertex as the attribute of the corresponding vertex. It is good also as. According to this, it is possible to efficiently cluster the events corresponding to each vertex.
- each of the computers uses, as the algorithm, the number of identifier data related to each vertex held by itself and the number of identifier data related to each vertex held by the adjacent computer as variables. It is also possible to hold a mathematical formula for calculating the transition probability of the corresponding identifier data from the own computer to another computer adjacent to the predetermined function, and calculate the transition probability using the mathematical formula. . According to this, it is possible to perform an efficient and accurate update process with respect to the number of identifier data that is the basis of clustering of each event.
- each computer has, as the algorithm, a relationship between the number of identifier data related to each vertex held by itself and the number of identifier data related to each vertex held by the adjacent computer.
- one computer may correspond to a plurality of vertices in the graph structure. According to this, it becomes possible to execute the information processing method of the present invention in a server device that controls a plurality of vertices, that is, a plurality of events.
- each terminal is replaced with each terminal instead of sending and receiving messages via the network.
- direct communication may be performed between the corresponding terminals, and the number of identifier data held by the corresponding terminals may be updated. According to this, it becomes possible to perform processing corresponding to not only a wide-area communication line such as the Internet but also a mode of message exchange by means such as various proximity wireless communication.
- each computer determines the attribute of the identifier data having the largest number among the identifier data held by the computer as the attribute of the corresponding vertex. Also good.
- the number of identifier data related to each vertex held by each computer and the number of identifier data related to each vertex held by the adjacent computer are variables as the algorithm. It is also possible to hold a mathematical formula for calculating the transition probability of the corresponding identifier data from the own computer to another computer adjacent to the predetermined function, and calculate the transition probability using the mathematical formula.
- each computer has, as the algorithm, a relationship between the number of identifier data related to each vertex held by itself and the number of identifier data related to each vertex held by the adjacent computer. It is also possible to hold a table that prescribes the transition probability of the corresponding identifier data from the own computer to another computer adjacent to the computer and calculate the transition probability using the table.
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Abstract
Description
---実施例1---
まず、本発明の情報処理システムの概念的な動作について説明する。図1に、本情報処理システムにおける解析対象となるグラフ構造モデル(以下、計算モデル1)の概念図を示す。この計算モデル10は、頂点110~114とこれら頂点間を結ぶ辺120で構成されるグラフ構造に対し、情報単位である情報子を拡散させることで計算を実施し、各頂点110~114上の情報子数を解とする計算モデルである。また、本例では計算対象を分類問題とする。
・・・・・(数式1)
ただし、Ndjは頂点jの次数。また、左辺Pは遷移確率を示し、Puは状態uの情報子の遷移確率を示す。また、uとvは情報子の異なる状態を表す。例えば、2状態(1ビット)の情報子であれば、uは状態=0、vは状態=1となる。また、NuおよびNvは情報子数を表す。例えば、Nuは状態uの情報子数、Nvは状態 vの情報子数となる。また、第3項および第4項のΣは近傍範囲内のデバイスに対し計算する。NuiおよびNvjは近傍範囲内のデバイスjの各状態の情報子数となる。また、f,gは関数、α、βは正の定数である。
---実施例2---
続いて、実施例1の計算モデルにて定義した各辺が重み係数を保持している場合について、実施例2として説明する。この実施例2においては、実施例1にて示した送信処理に各辺の重み係数に基づいた遅延処理が加わる。そのため計算機220は該当処理を実行する遅延器を備える構成となっている。
---実施例3---
続いて、実施例1にて示した図4のフローにおけるステップ311、すなわちグラフ構造データの取得処理に際し、実社会の活動に即して自動的に取得する機能を備えた情報処理システムの例について説明する。この場合の概念において、計算モデルであるグラフ構造データを情報処理システム上で算出せず、実社会の活動をそのまま用いることとなる。
---実施例4---
次に、上述の実施例3における各頂点がデータであり、頂点間の辺がデータ間のアクセスの連続性とした計算モデルに対応した情報処理システムの例として実施例4を示す。本実施例4では、複数の計算機上で複数のプロセスが処理される時の、各プロセスに必要なデータを効率よく計算機に配置する方法を提供するものである。
---実施例5---
次に、倉庫における荷物のピックアップ作業に関し、当該作業を行う作業者の動線距離を短くするように、倉庫内の棚の再配置を行う問題を扱う場合の情報処理方法について実施例5として示す。図28にて、対象とする倉庫500の構成を例示する。当該倉庫500は複数の領域を持つ。図28における倉庫500は、領域A(5010-1)~領域D(5010-4)の4つの領域を持つ。これら各領域A~Dは、該当領域内に複数の棚が配置されている。図28の例では、領域A(5010-A)に置かれている棚の1つとして棚5011-A1を図示しているが、その他の複数の棚が配置されているものとする。領域B~領域Dも領域Aと同様に複数の棚を備えている。さらに、各棚は、複数の荷物を置くことができる。図28の例では、棚A-1(5011-A1)に荷物5012-A1-1、5012-A1-2が配置された例を図示している。他の棚についても同様に複数の荷物を配置可能である。
---実施例6---
次に、ソシアルネットワークサービスなどの複数のユーザ間の交流サービスにおいて、ユーザのクラスタリング、すなわちコミュニティを検出する問題を扱う際の、情報処理方法について実施例6として説明する。ここでは、上述の実施例3において各頂点がユーザで、頂点間の辺がユーザ間の交流とする情報処理システムを想定する。ここで、交流とは、例えば、メール送受信、メッセージ送受信、個人のページへの訪問や投稿などが該当する。
図38に本実施例6における情報子の交換例を示す。図38において、情報子を、各ユーザが保持する端末または上述の交流サービスを提供している情報処理サービスの各ユーザの記憶領域、に保存するものとする。この場合、初期状態を状態1(6101)とする。 その後、ユーザ1(6011)がユーザ2(6012)にメッセージを送信する時、該当端末6031、6032はメッセージに情報子を付加する。状態2(6102)にて、端末6031、6032がメッセージに付加する情報子数6113の例を示す。ここでは、ユーザ1の端末6031から情報子5個が付加された例となっている。
---実施例7---
次に、上述の実施例5にて示した倉庫において、図29の各作業者5100の荷物リスト5110が予め入手できた場合の棚の最適配置をシミュレーションする形態について実施例7として示す。上述の実施例5においては、倉庫内での作業者5100の移動に合わせて棚の最適配置を実施するとしたが、本実施例7では、例えば計算機5210が荷物リスト5110を入手してから、該当荷物リスト5110中の荷物の発送まで十分な時間があるならば、計算機5210が、この荷物リスト5110からグラフ構造データを作成し、実施例1にて示した情報処理システム100と同様の計算を実行する。
---実施例8---
次に、情報処理システムの計算機が実施例1における遷移確率式(数式1)を表として持つ形態について、実施例8として示す。図41~43において、遷移確率表1~3として、遷移確率表8001~8003の各例を示している。情報処理システムの計算機は、この遷移確率表8001~8003に、自頂点の情報子数(u,v)と隣接頂点の情報子数(ΣNju、ΣNjv)とを照合し、表中での対応値を特定することで遷移確率を決定する事ができる。上記の各表8001~8003の値は、計算機が予めシミュレーションを実行し、目的とする結果が得られる値を実験的に算出したものとなる。
また、上述の情報処理システムにおいて、前記グラフ構造における複数頂点に対して1つの計算機が対応するとしてもよい。これによれば、複数の頂点すなわち複数の事象に関して統括するサーバ装置において本発明の情報処理方法を実行することが可能となる。
100 情報処理システム
220 計算機
221 CPU
222 主記憶装置
223 ストレージ
225 ネットワークインターフェイス
226 プログラム
3020 デバイス
5011 棚
5220 移動端末
Claims (14)
- 解析対象の事象に対応した複数の頂点と、対応する事象間の関係性に応じて該当頂点間を結ぶ辺とで構成されるグラフ構造をモデルとして、前記各頂点にそれぞれ対応し、前記辺に対応してデータ授受可能に互いに接続される複数の計算機と、
前記各計算機に蓄積される、前記頂点の事象に対する1つ以上の状態をあらわす属性を持つ識別子データを保持する記憶装置と、
前記記憶装置において前記各頂点に関して保持する識別子データの個数を、前記頂点の分布に基づく空間分布図として表示する表示装置と、を含み、
前記各計算機は、前記辺で結ばれて隣接する前記頂点に対応する計算機との間で、互いの保持する識別子データの個数に基づく所定のアルゴリズムにより、互いの計算機の間での識別子データの遷移確率を計算し、当該計算結果に応じて、互いの計算機の保持する識別子データの個数を更新するものである、
ことを特徴とする情報処理システム。 - 前記各計算機は、
前記頂点に関して自身が保持している前記識別子データのうち、個数が最も多い識別子データの属性を、該当頂点の属性と判定するものである、
ことを特徴とする請求項1に記載の情報処理システム。 - 前記各計算機は、
前記アルゴリズムとして、自身で保持する前記各頂点に関する識別子データの個数と、前記隣接する計算機が保持する前記各頂点に関する識別子データの個数とを変数とした所定関数により、自計算機から隣接する他の計算機への該当識別子データの遷移確率を計算する数式を保持しており、当該数式を用いて前記遷移確率を計算するものである、
ことを特徴とする請求項1に記載の情報処理システム。 - 前記各計算機は、
前記アルゴリズムとして、自身で保持する前記各頂点に関する識別子データの個数と、前記隣接する計算機が保持する前記各頂点に関する識別子データの個数との関係に応じて予め定められた 自計算機から隣接する他の計算機への該当識別子データの遷移確率を規定する表を保持しており、当該表を用いて前記遷移確率を計算するものである、
ことを特徴とする請求項1に記載の情報処理システム。 - 前記各計算機は、
前記遷移確率の計算結果に応じて識別子データの個数を更新する際、自身と前記隣接する計算機とで保持する識別子データの個数の総和を維持するよう更新を行うものである、
ことを特徴とする請求項1に記載の情報処理システム。 - 前記グラフ構造における複数頂点に対して1つの計算機が対応することを特徴とする請求項1に記載の情報処理システム。
- 管理計算機と、倉庫内の棚各々に設置された計算機と、前記棚に配置された荷物を集荷する各作業員が所持して前記計算機にアクセス可能な携帯端末と、を含み、
前記各計算機は、
前記棚ないし該当棚に配置された荷物に関する所定事象に対応した複数種類の識別子データの個数の情報を保持しており、
前記各携帯端末からのアクセスを受けて、該当携帯端末が保持する識別子データを受信し、当該計算機が既に保持する前記識別子データの過去の変動状況に基づき決定した識別子データを前記携帯端末へ送信し、前記携帯端末との前記識別子データの送受信の結果の差引数で、当該計算機が保持している前記複数種類の識別子データの個数を更新するものであり、
前記各携帯端末は、
前記計算機から前記識別子データを受信し、当該受信した識別子データにより、自身が保持している識別子データを更新するものであり、
前記各計算機は、
前記識別子データの個数の更新から所定時間経過後、自身が保持している前記複数種類の識別子データの個数のうち一番多い種類に対応付けられた配置先へ、自身が設置されている棚を移動する指示を前記管理計算機へ出力するものである、
ことを特徴とする情報処理システム。 - 管理計算機と、データセンタ内の各データに対応付けた計算機と、を含み、
前記各計算機は、
前記データに関する所定事象に対応した複数種類の識別子データの個数の情報を保持しており、
前記各データを利用するプログラムからのアクセスを受けて、該当プログラムが保持する識別子データを受信し、当該計算機が既に保持する前記識別子データの過去の変動状況に基づき決定した識別子データを前記プログラムへ与え、前記プログラムとの前記識別子データの送受信の結果の差引数で、当該計算機が保持している前記複数種類の識別子データの個数を更新するものであり、
前記各プログラムは、
前記計算機から前記識別子データを受信し、当該受信した識別子データにより、自身が保持している識別子データを更新するものであり、
前記各計算機は、
前記識別子データの個数の更新から所定時間経過後、自身が保持している前記複数種類の識別子データの個数のうち一番多い種類に対応付けられた配置先へ、自身が対応付けされているデータを移動する指示を前記管理計算機へ出力するものである、
ことを特徴とする情報処理システム。 - ネットワークを介してメッセージを送受信する複数の端末と管理計算機とを含み、
前記各端末は、
複数種類の識別子データの個数に関する情報を保持し、
前記メッセージを送信する場合、自身が既に保持している前記識別子データの個数の過去の変動状態に基づき決定した前記識別子データを付加したメッセージを、前記複数の端末のなかの他の端末に送信し、当該送信した識別子データの個数を差し引くことにより、自身が保持している前記複数種類の識別子データの個数を更新し、
前記メッセージを受信する場合、受信した前記メッセージに付加された識別子データの個数を加えることにより、自身が保持している前記複数種類の識別子データの個数を更新し、
前記識別子データの個数の更新から所定時間経過後、自身が保持している前記複数種類の識別子データの個数を前記管理計算機に送信するものであり、
前記管理計算機は、
前記各端末より前記複数種類の識別子データの個数を受信して、前記識別子データの個数が最も多い種類が共通する端末が同じグループであることを示す情報を、前記管理計算機が有する表示端末に表示するものである、
ことを特徴とする情報処理システム。 - 前記各端末は、
前記ネットワークを介したメッセージの送受信に代えて、各端末が物理的に近接した場合に該当端末間で直接通信を行い、該当端末が保持している識別子データの個数を更新するものである、
ことを特徴とする請求項9に記載の情報処理システム。 - 解析対象の事象に対応した複数の頂点と、対応する事象間の関係性に応じて該当頂点間を結ぶ辺とで構成されるグラフ構造をモデルとして、前記各頂点にそれぞれ対応し、前記辺に対応してデータ授受可能に互いに接続され、前記頂点の事象に対する1つ以上の状態をあらわす属性を持つ識別子データを保持する複数の計算機が、
前記辺で結ばれて隣接する前記頂点に対応する計算機との間で、互いの保持する識別子データの個数に基づく所定のアルゴリズムにより、互いの計算機の間での識別子データの遷移確率を計算し、当該計算結果に応じて、互いの計算機の保持する識別子データの個数を更新し、
前記各頂点に関して保持する識別子データの個数を、前記頂点の分布に基づく空間分布図として表示装置にて表示する、
ことを特徴とする情報処理方法。 - 前記各計算機が、
前記頂点に関して自身が保持している前記識別子データのうち、個数が最も多い識別子データの属性を、該当頂点の属性と判定する、
ことを特徴とする請求項11に記載の情報処理方法。 - 前記各計算機が、
前記アルゴリズムとして、自身で保持する前記各頂点に関する識別子データの個数と、前記隣接する計算機が保持する前記各頂点に関する識別子データの個数とを変数とした所定関数により、自計算機から隣接する他の計算機への該当識別子データの遷移確率を計算する数式を保持しており、当該数式を用いて前記遷移確率を計算する、
ことを特徴とする請求項11に記載の情報処理方法。 - 前記各計算機が、
前記アルゴリズムとして、自身で保持する前記各頂点に関する識別子データの個数と、前記隣接する計算機が保持する前記各頂点に関する識別子データの個数との関係に応じて予め定められた 自計算機から隣接する他の計算機への該当識別子データの遷移確率を規定する表を保持しており、当該表を用いて前記遷移確率を計算する、
ことを特徴とする請求項11に記載の情報処理方法。
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US10511585B1 (en) * | 2017-04-27 | 2019-12-17 | EMC IP Holding Company LLC | Smoothing of discretized values using a transition matrix |
US10572854B2 (en) * | 2017-11-09 | 2020-02-25 | Locus Robotics Corporation | Order grouping in warehouse order fulfillment operations |
CN110019989B (zh) * | 2019-04-08 | 2023-11-03 | 腾讯科技(深圳)有限公司 | 一种数据处理方法及装置 |
CN115023925B (zh) * | 2019-11-27 | 2024-03-12 | 128技术公司 | 度量和事件基础设施 |
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Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2009282574A (ja) * | 2008-05-19 | 2009-12-03 | Sony Corp | 情報処理装置、情報処理方法、およびプログラム |
WO2011145374A1 (ja) * | 2010-05-17 | 2011-11-24 | 株式会社日立製作所 | 計算機システム、及び、規則生成方法 |
JP2013041530A (ja) * | 2011-08-19 | 2013-02-28 | Fuji Xerox Co Ltd | 経路算出のためのプログラム及び経路算出装置 |
Family Cites Families (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8429184B2 (en) * | 2005-12-05 | 2013-04-23 | Collarity Inc. | Generation of refinement terms for search queries |
US9063979B2 (en) * | 2007-11-01 | 2015-06-23 | Ebay, Inc. | Analyzing event streams of user sessions |
AU2009282574B2 (en) * | 2008-08-20 | 2014-08-21 | Merck Sharp & Dohme Corp. | Ethenyl-substituted pyridine and pyrimidine derivatives and their use in treating viral infections |
US8190119B2 (en) * | 2009-03-03 | 2012-05-29 | E3 Llc | System and method for direct communication between wireless communication devices |
US8903824B2 (en) | 2011-12-09 | 2014-12-02 | International Business Machines Corporation | Vertex-proximity query processing |
AU2013200876A1 (en) * | 2012-02-24 | 2013-09-12 | Callum David Mcdonald | Method of graph processing |
US9374310B2 (en) * | 2013-10-08 | 2016-06-21 | Dell Products L.P. | Systems and methods of inter data center out-bound traffic management |
US9894633B2 (en) * | 2013-12-06 | 2018-02-13 | Google Llc | Reminders based on device proximity using bluetooth LE |
-
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2009282574A (ja) * | 2008-05-19 | 2009-12-03 | Sony Corp | 情報処理装置、情報処理方法、およびプログラム |
WO2011145374A1 (ja) * | 2010-05-17 | 2011-11-24 | 株式会社日立製作所 | 計算機システム、及び、規則生成方法 |
JP2013041530A (ja) * | 2011-08-19 | 2013-02-28 | Fuji Xerox Co Ltd | 経路算出のためのプログラム及び経路算出装置 |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2017056168A1 (ja) * | 2015-09-28 | 2017-04-06 | 株式会社日立製作所 | 情報処理システムおよび情報処理方法 |
JPWO2017056168A1 (ja) * | 2015-09-28 | 2018-01-18 | 株式会社日立製作所 | 情報処理システムおよび情報処理方法 |
WO2022009339A1 (ja) * | 2020-07-08 | 2022-01-13 | 日本電気株式会社 | 会話監視装置、制御方法、及びコンピュータ可読媒体 |
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