Patent Publication Number: US-10783184-B2

Title: Data generation method and computer system

Description:
CLAIM OF PRIORITY 
     The present application claims priority from Japanese patent application JP 2016-129753 filed on Jun. 30, 2016, the content of which is hereby incorporated by reference into this application. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a method and a system for generating data (feature value) for classifying an identification target into a predetermined class using a plurality of time series data. 
     2. Description of the Related Art 
     Attention is attracted to a technology for analyzing or predicting a state of the social infrastructure or the like from data of the real world and cyberspace or controlling the social infrastructure or the like for the purpose of efficient design and operation of the social infrastructure, cities and so forth. 
     Within the technology described above, an identification technology especially of input data occupies an important role. Input data is configured from audio data, sensing data of a chemical substance, a temperature, a humidity or the like, log data of a machine such as an automobile or the like and a computer, and log data of an e-mail, a social networking service (SNS) or the like. 
     An identification process in which input data is used is a process of classifying in what state an environment and elements (a person, a thing, information and so forth) are. In particular, the identification process is a process for determining a class to which input data belongs from among a plurality of classes defined in advance. For example, a class representative of a traffic state such as “traffic jam” or “accident,” a class representative of a substance of a target such as “explosive” and so forth are available. 
     In an identification process in which time series data is used as input data, generally time series data is converted into a statistical feature value such as an average or a variance in the time direction or frequency conversion. Then, a class to which the input data belongs is determined using the statistical feature value. 
     The method described above has a problem in that the identification accuracy decreases significantly when the time series data exhibits a complicated transient response, when a plurality of time series data having different features from each other exist (when lengths in the time direction in determining a statistical feature value are different from each other) or when the relationship between a plurality of time series data has some noise. 
     For example, in an identification process for classifying a type or the like of an object using a plurality of chemical sensing data, since the chemical substance disperses from the object through the space and reaches a sensor, depending upon the measurement environment, a significant dispersion appears in the arrival time. When a plurality of chemical substances are measured, transient responses between sensing data of the chemical substances exhibit a state in which noise appears therein. 
     As a technology for extracting a feature value of time series data, a technology disclosed in JP-2008-116588-A is known. JP-2008-116588-A describes: “a one-dimensional time series signal is analyzed by an unsteady chaos analysis, and a high order local autocorrelation coefficient is calculated from a two-dimensional image generated by the analysis to extract a feature. The calculation of the high order local autocorrelation coefficient is performed on the basis of binary image information generated by converting two-dimensional image information into binary information using a threshold value obtained by calculating a histogram of a two-dimensional image generated by the analysis of the one-dimensional time series signal. The one-dimensional time series signal is an audio signal or an acoustic signal, and the unsteady chaos analysis is performed by a recurrence plot technique.” 
     SUMMARY OF THE INVENTION 
     Generally, when a time variation of data is to be identified, a secondary feature value is generated for multidimensional input data for each dimension and is inputted to an identification unit such as a neural network or a support vector machine or the like to perform machine learning. The secondary feature value may be a feature value calculated by a statistical process such as a histogram, an average value or a variance value in the time direction and so forth, or a frequency, a phase or the like calculated using fast Fourier transform (FFT) or the like. In the case of the learning method described above, although fitting to teacher data used upon learning can be performed with high accuracy, the identification accuracy for unknown data degrades. 
     Further, even if the technology of JP-2008-116588-A is expanded to multidimensional input data, since the relationship between dimensions (influence between input data) is not taken into consideration, a high degree of identification accuracy cannot be implemented. 
     It is considered that, since the known technology has a problem in the feature value, the identification accuracy of the identification process for a plurality of time series data is low. The present invention provides a method and a system for generating data including a feature value with which an identification process of high accuracy for a plurality of time series data is implemented. 
     According to a typical example of the invention disclosed in the present application, there is provided a data generation method for a computer system which includes a plurality of computers each including a processor, a memory connected to the processor and a network interface connected to the processor, at least one of the computers including a data generation unit configured to acquire a plurality of data and generate pattern data representative of a feature value for identifying a class to which an identification target belongs using the plurality of data, at least one of the computers including a storage unit configured to retain graph information for managing a graph configured from a plurality of vertexes and sides which connect the plurality of vertexes to each other, the data generation method including a first step by the data generation unit of acquiring the plurality of data and the graph information and assuring storage regions in number equal to the number of vertexes included in the graph for storing the plurality of data, a second step by the data generation unit of converting each of the plurality of data into an input value and setting at least one input value to a storage region corresponding to at least one of the vertexes included in the graph, a third step by the data generation unit of executing an updating process for updating a value set to a storage region corresponding to a first vertex using the value set to the storage region corresponding to the first vertex and a value set to a storage region corresponding to a different vertex directly connected to the first vertex, and a fourth step by the data generation unit of outputting a set of values set to the storage regions individually corresponding to the plurality of vertexes included in the graph as the pattern data. 
     With the present invention, pattern data which is a feature value on which an influence between data and a transient response of data are reflected can be generated. The identification accuracy of the identification process can be improved by using the pattern data. Subjects, constitutions and effects other than those described above will become apparent from the following description of the embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram depicting an example of a configuration of a computer system of an embodiment 1; 
         FIG. 2  is a diagrammatic view illustrating an example of time series data in the embodiment 1; 
         FIGS. 3A and 3B  are diagrammatic views illustrating different examples of a graph in the embodiment 1; 
         FIG. 4  is a view illustrating an example of graph data in the embodiment 1; 
         FIG. 5  is a flow chart illustrating a pattern data generation process executed by a data processing unit in the embodiment 1; 
         FIG. 6  is a diagrammatic view illustrating a concept of an inflow process of time series data in the embodiment 1; 
         FIG. 7  is a diagrammatic view illustrating an example of pattern data in the embodiment 1; 
         FIG. 8  is a diagrammatic view illustrating an example of an inputting method of pattern data to an identification unit in the embodiment 1; 
         FIG. 9  is a view illustrating an example of a list used when a converter in the embodiment 1 converts data of a vertex; 
         FIG. 10  is a view illustrating identification accuracy of an identification process of a computer in the embodiment 1; 
         FIG. 11  is a flow chart illustrating a modification to the pattern data generation process executed by the data processing unit in the embodiment 1; 
         FIG. 12  is a flow chart illustrating a pattern data generation process executed by a data processing unit in an embodiment 2; 
         FIGS. 13 and 14  are diagrammatic views illustrating concepts of an inflow process of time series data and an outflow process of a particle in the embodiment 2; 
         FIGS. 15A and 15B  are diagrammatic views illustrating different examples of pattern data in the embodiment 2; 
         FIG. 16  is a view illustrating an example of structure data of graph data in an embodiment 3; 
         FIG. 17  is a diagrammatic view illustrating an example of a graph in the embodiment 3; 
         FIG. 18  is a diagram illustrating an example of time series data inputted upon generation of pattern data in the embodiment 3; 
         FIG. 19  is a diagrammatic view illustrating an example of pattern data in the embodiment 3; and 
         FIG. 20  is a flow chart illustrating a process executed by a system of an embodiment 4. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Embodiment 1 
     First, a generation method of pattern data using a plurality of time series data and an identification process using pattern data according to an embodiment 1 are described. 
       FIG. 1  is a block diagram depicting an example of a configuration of a computer system  100  of the embodiment 1. 
     As depicted in  FIG. 1 , the computer system  100  of the embodiment 1 is configured from a plurality of computers  101  and a storage system  102 , which are connected to each other by a network  103 . 
     The computer system  100  of the embodiment 1 include three computers  101 - 1 ,  101 - 2  and  101 - 3 . It is to be noted that the number of computers  101  may otherwise be smaller than 3 or greater than 3. 
     The network  103  may be a wide area network (WAN), a local area network (LAN), a storage area network (SAN) or the like. It is to be noted that the embodiment 1 is not limited in regard to the type of the network  103 . Further, the network which connects the computers  101  and the network which connects each of the computers  101  and the storage system  102  may be different from each other. 
     Each of the computers  101  executes a pattern data generation process, an identification process in which generated pattern data is used, and further processes. The computer  101  includes a processor  110 , a memory  111  and a network interface  112 , which are connected to each other by a bus  114 . 
     The processor  110  includes one or more central processing units (CPUs)  115  which execute an arithmetic operation process. Each of the CPUs  115  implements functions of the computer  101  by executing a program stored in the memory  111 . Further, a process to be executed in the computer  101  is executed by one or more of the CPUs  115 . It is to be noted that a plurality of processes may be executed by one CPU  115 . It is to be noted that the CPU  115  may be an arithmetic operation unit such as a field programmable gate array (FPGA) or a graphics processing unit (GPU). 
     In the description given below, where a process is described using a functioning unit (module) as a subject, this represents that the CPU  115  is executing a program which implements the functioning unit. 
     The memory  111  stores programs to be executed by the CPU  115  (processor  110 ) and information to be used in the programs. The programs and the information stored in the memory  111  are hereinafter described. Further, the memory  111  includes a memory space allocated to a process executed by the CPU  115 . 
     It is to be noted that the memory space may be secured in the memory area of a plurality of memories  111  or may be secured in the memory area of one memory  111 . Further, the memory  111  may include a single memory space allocated to a plurality of processes or may include a plurality of memory spaces individually allocated to a plurality of processes. 
     The network interface  112  communicates with an external apparatus through the network  103 . In the embodiment 1, the processor  110  accesses a different computer  101  or the storage system  102  through the network interface  112 . 
     The storage system  102  stores various data to be used by the computer  101 . The storage system  102  includes a processor  130 , a memory  131 , a network interface  132 , a disk interface  133  and a plurality of hard disk drives (HDDs)  134 , which are connected to each other by a bus  135 . 
     The processor  130 , memory  131  and network interface  132  are same as the processor  110 , memory  111  and network interface  112 , respectively. The disk interface  133  is an interface for connecting to the plurality of HDDs  134 . Each of the HDDs  134  is a storage apparatus for storing various data. It is to be noted that the storage system  102  may have a storage apparatus other than an HDD such as a solid state drive (SSD). 
     Here, the programs and the information stored in the memory  111  of the computer  101  are described. The memory  111  stores a program which implements a data processing unit  120 . Further, the memory  111  stores graph data  121 , time series data  122  and pattern data  123 . 
     The data processing unit  120  executes a pattern data generation process and an identification process. It is to be noted that the data processing unit  120  may execute a process other than the processes described above. 
     In the pattern data generation process, the data processing unit  120  (CPU  115 ) inputs values of time series data to vertexes included in a graph to be used for generation of the pattern data  123  and updates the values of the time series data set to the vertexes to generate pattern data  123 . Here, the graph is configured from a plurality of vertexes and a plurality of sides connecting the vertexes to each other. 
     In the identification process, the data processing unit  120  (CPU  115 ) converts data of an identification target into pattern data  123  and inputs the pattern data  123  to an identification unit to perform predetermined identification. In the embodiment 1, a convolutional neutral network (CNN) or a neural network (NN) is used as the identification unit. The NN is used for general data recognition, and the CNN is used for image recognition and so forth. 
     It is to be noted that the data processing unit  120  may be configured from a plurality of program modules. For example, the data processing unit  120  may include a data generation unit for generating pattern data  123  and an identification processing unit for executing an identification process. Alternatively, different program modules may be provided in different computers  101 . 
     The graph data  121  is data of a graph configured from a plurality of vertexes and a plurality of sides. Details of the graph data  121  are hereinafter described with reference to  FIGS. 3A, 3B and 4 . 
     The time series data  122  is data of an identification target. The time series data  122  retains values included in a predetermined range of time including the present point of time or values of a predetermined number of samples including a value at present. It is to be noted that a plurality of time series data  122  may be stored in the memory  111 . The plurality of time series data  122  may be data of the same type or data of different types. An example of the time series data  122  is hereinafter described with reference to  FIG. 2 . 
     The pattern data  123  is a feature value generated from a plurality of time series data  122  and is given as a set of values set to vertexes of the graph. 
     In the embodiment 1, the graph data  121  and the time series data  122  are stored in the storage system  102 . Accordingly, the CPU  115  acquires the graph data  121  and the time series data  122  from the storage system  102  and loads the acquired graph data  121  and time series data  122  into the memory  111 . 
       FIG. 2  is a view illustrating an example of the time series data  122  in the embodiment 1. 
       FIG. 2  illustrates four time series data  122  of time series data A, time series data B, time series data C and time series data D. The ordinate and abscissa of the time series data  122  depicted in  FIG. 2  represent strength and time, respectively. 
     In the embodiment 1, it is supposed that each time series data  122  which varies as time passes in the real world is converted into pattern data  123 . In this case, the data processing unit  120  cyclically acquires a value (strength) at present of the time series data  122  to generate pattern data  123 . 
     It is to be noted that, if time series data  122  indicative of a history in the past or the like is inputted, then the data processing unit  120  sets simulation time as a variable and acquires the value of the simulation time of each time series data  122 . A generation method of pattern data  123  using time series data  122  indicative of a history in the past or the like is hereinafter described as a modification to the embodiment 1. 
       FIGS. 3A and 3B  are diagrammatic views illustrating examples of a graph in the embodiment 1.  FIG. 4  is a view illustrating an example of the graph data  121  in the embodiment 1. 
     A graph  300  is configured from a plurality of vertexes  301  and a plurality of sides  302 . Each of the vertexes  301  is connected to another vertex  301  through a side  302 . A double-sided arrow mark of a side which connects vertexes  301  to each other indicates directions of interactions. 
     The graph  300  depicted in  FIG. 3A  is a lattice-shaped directed graph configured from six vertexes  301  both in the vertical and horizontal directions. As depicted in an enlarged view of a portion of the graph  300  in a frame  310 , a vertex I 0  ( 301 ) is a vertex from which data flows out to a different vertex I 1  ( 301 ) and into which data flows from the different vertex I 1  ( 301 ). 
     In the pattern data generation process, a value of the time series data  122  is set to at least one vertex  301 . The value set to the vertex  301  is updated using the value set to the own vertex  301  and the value set to a neighboring vertex  301 . A set of values set to the vertexes  301  of the graph  300  is outputted as pattern data  123 . 
     It is to be noted that the graph  300  is not limited to such a lattice-shaped graph as depicted in  FIG. 3A . For example, such a graph  300  as depicted in  FIG. 3B  may be used. The graph  300  depicted in  FIG. 3B  has a greater number of sides  302  in the proximity of the center thereof than that of the graph  300  depicted in  FIG. 3A . 
     Referring to  FIG. 4 , the graph data  121  which is data for managing the graph  300  includes structure data  400  and inflow definition data  410 . 
     The structure data  400  is data for managing the structure of the graph  300  and the values set to the vertexes  301 . The structure data  400  includes a plurality of entries each configured from a vertex ID  401 , an outflow vertex ID  402 , an inflow vertex ID  403  and a vertex data region  404 . 
     The vertex ID  401  is an identifier for uniquely identifying each vertex  301  included in the graph  300 . Each of the outflow vertex ID  402  and the inflow vertex ID  403  is an identifier of a vertex  301  directly connected through a side  302  to a vertex  301  corresponding to the vertex ID  401 . The outflow vertex ID  402  is an identifier of a vertex  301  connected through a side  302  directed from a vertex  301  corresponding to the vertex ID  401  to a different vertex  301 . The inflow vertex ID  403  is an identifier of a vertex  301  connected through a side  302  directed from a different vertex  301  to the vertex  301  corresponding to the vertex ID  401 . 
     By defining each vertex ID as the outflow vertex ID  402  and the inflow vertex ID  403  separately, the connection relationship of the vertexes can be managed. It is to be noted that, where all vertexes  301  are connected by bidirectional sides  302  as depicted in  FIG. 3A , the identifier of the same vertex  301  is stored in the outflow vertex ID  402  and the inflow vertex ID  403 . 
     The vertex data region  404  stores values of the individual time series data  122  set to a vertex  301  corresponding to the vertex ID  401 . In the vertex data region  404  for one entry, columns in number equal to the number of the time series data  122  inputted to the graph  300  are generated. In each column, an identifier of the time series data  122  is set. For example, where four time series data  122  are inputted to the graph  300 , four columns are generated in the vertex data region  404 . 
     It is to be noted that the vertex data region  404  may be managed otherwise as information separate from the structure data  400 . It is to be noted that a plurality of time series data  122  may share one column in the vertex data region  404 . In this case, since the number of time series data  122  and the number of columns of the vertex data region  404  do not coincide with each other, a corresponding relationship of the columns of the vertex data region  404  to the time series data  122  may be determined in advance. 
     It is to be noted that, where it is necessary to manage an identifier, a length and a vertex  301  to be connected, the structure data  400  may be divided into two data including data for managing the vertexes  301  and data for managing the sides  302 . 
     The inflow definition data  410  is definition information of a vertex  301  to which a value of time series data  122  is to be inputted. The inflow definition data  410  includes a plurality of entries each including a data ID  411 , a vertex ID  412  and a standardization constant  413 . 
     The data ID  411  is an identifier for identifying time series data  122 . For example, in the data ID  411 , a type, a name or the like of time series data  122  is stored. The vertex ID  412  is an identifier of a vertex  301  to which a value of time series data  122  is to be inputted. The standardization constant  413  is a value to be used to standardize a value to be inputted to the vertex  301 . 
     As hereinafter described, the data processing unit  120  standardizes a value of time series data  122  in accordance with the inflow definition data  410  and inputs the standardized value to at least one vertex  301  included in the graph  300 . For example, a value of the time series data A is inputted to the vertex  301  whose vertex ID  412  is “I 14 .” 
     It is to be noted that the structure data  400  and the inflow definition data  410  may be managed otherwise as separate data from each other. 
       FIG. 5  is a flow chart illustrating a pattern data generation process executed by the data processing unit  120  in the embodiment 1.  FIG. 6  is a diagrammatic view illustrating a concept of an inflow process of time series data  122  in the embodiment 1.  FIG. 7  is a diagrammatic view illustrating an example of the pattern data  123  in the embodiment 1. 
     In the embodiment 1, the data processing unit  120  cyclically executes the pattern data generation process described below. 
     First, the data processing unit  120  acquires graph data  121  from the storage system  102  and stores the acquired graph data  121  into the memory  111  (step S 501 ). Further, the data processing unit  120  acquires values (latest values) of time series data  122  at the present point of time from the storage system  102  and stores the acquired values into the memory  111  (step S 502 ). It is to be noted that, if the graph data  121  is already stored in the memory  111 , then the data processing unit  120  may omit the process at step S 501 . 
     Then, the data processing unit  120  executes an inflow process of the time series data  122  (step S 503 ). In particular, the following process is executed. 
     The data processing unit  120  generates columns in number equal to the number of time series data  122  in the vertex data region  404  of the structure data  400 . The data processing unit  120  sets “0” as an initial value to the generated columns. As a data process, the data processing unit  120  generates storage regions in number equal to the number of vertexes  301  for storing values in the memory  111  and sets “0” to the storage regions. It is to be noted that, if columns are already generated in the vertex data region  404 , then the process is omitted. 
     The data processing unit  120  selects a value of a target from among the acquired values of the time series data  122 . The data processing unit  120  refers to the data ID  411  of the inflow definition data  410  to search for an entry which coincides with the identifier of the time series data  122  corresponding to the target value. 
     The data processing unit  120  standardizes the target value on the basis of the standardization constant  413  in the searched out entry. For example, where the value acquired from the time series data A is “30,” the data processing unit  120  standardizes the value by dividing the value “30” by the standardization constant “1.” 
     It is assumed that an algorithm to be used for standardization of a value is set in advance in the data processing unit  120 . It is to be noted that it is possible to set different algorithms for standardization to different ones of the time series data  122 . 
     The data processing unit  120  acquires an identifier of the vertex  301  from the vertex ID  412  in the searched out entry. The data processing unit  120  refers to the vertex ID  401  of the structure data  400  to search for an entry which coincides with the acquired identifier of the vertex  301 . 
     The data processing unit  120  refers to the vertex data region  404  in the searched out entry to specify a column to which the standardized value is to be set. The data processing unit  120  updates the value in the specified column using the standardized value. For example, the data processing unit  120  overwrites the standardized value in the specified column or adds the standardized value to the value stored in the specified column. 
     The data processing unit  120  executes the process described above for all of the values of the time series data  122 . It is to be noted that, although, in the embodiment 1, a standardized value is inputted to a vertex  301  of the graph  300 , alternatively a quantized value may be inputted. 
       FIG. 6  illustrates a concept of the inflow process of time series data  122  described above. For example, after a value of the time series data A is standardized or quantized, the converted value is inputted to the vertex  301  whose vertex ID  401  is “I 14 .” In the following description, the value of time series data  122  set to a vertex  301  in the inflow process is referred to also as input value. The process at step S 503  is such as described above. 
     Then, the data processing unit  120  starts a loop process for generating pattern data  123  (step S 504 ). The data processing unit  120  sets, upon starting of the loop process, “1” to a variable indicative of the number of times of execution of the loop process. The loop process is repetitively executed by the number of times set in advance. 
     The data processing unit  120  executes an update value calculation process (step S 505 ). In the update value calculation process, the data processing unit  120  calculates an update value of a vertex  301  of a target on the basis of the value set to the target vertex  301  and a value set to a different vertex  301 . In particular, such processes as described below are executed. 
     The data processing unit  120  refers to the structure data  400  to select one target vertex  301  from among the vertexes  301  included in the graph  300 . For example, the data processing unit  120  selects a vertex  301  in order from the top entry of the structure data  400 . The data processing unit  120  acquires the inflow vertex ID  403  of the selected entry and searches for an entry which coincides with the identifier of the vertex  301  whose vertex ID  401  is acquired. In the following description, the vertex  301  corresponding to the inflow vertex ID  403  is referred to also as inflow vertex  301 . 
     The data processing unit  120  acquires a value in the vertex data region  404  in the entry corresponding to the target vertex  301  and a value in the vertex data region  404  in the entry corresponding to the inflow vertex  301 . The data processing unit  120  uses the values set to the vertexes  301  to calculate an update value for the value set to the target vertex  301 . The value set to a vertex  301  is updated by an interaction with a value set to a neighboring vertex  301 . 
     For example, if the columns of the time series data A and the time series data B are included in the vertex data region  404  in the entry corresponding to the target vertex  301 , the update value in the time series data A for the target vertex  301  is calculated in accordance with the expression (1) given below and the value in the time series data A of the target vertex  301  is updated in accordance with the expression (2) given below. 
                   [     Expression   ⁢           ⁢   1     ]                             Δ   ⁢           ⁢     D     i   ,   A         =       f   ⁡     (     D     i   ,   A       )       -     g   ⁡     (     D     i   ,   B       )       +       ∑   j     ⁢     h   ⁡     (     D     j   ,   A       )         -       ∑   j     ⁢     l   ⁡     (     D     j   ,   B       )                   (   1   )               [     Expression   ⁢           ⁢   2     ]                             D     i   ,   A       ←       D     i   ,   A       +     Δ   ⁢           ⁢     D     i   ,   A                   (   2   )               
where D i,A  represents a value of the time series data A before updating set to the target vertex  301  whose identifier is “i”; D i,B  a value of the time series data B before updating set to the target vertex  301  whose identifier is “i”; f(D i,A ) a function which includes D i,A  as a variable; g(D i,B ) a function which includes D i,B  as a variable; h(D j,A ) a function which includes, as a variable, a value of the time series data A set to an inflow vertex  301  whose identifier is “j”; and l(D j,B ) a function which includes, as a variable, a value of the time series data B set to the inflow vertex  301  whose identifier is “j.” It is to be noted that particular functions may be provided in advance or may be determined from a result of an experiment or the like.
 
     The first term of the right side of the expression (1) is a value calculated from a value of the time series data  122  of the update target set to the target vertex  301 . The second term of the right side of the expression (1) is a value calculated from a value of different time series data  122  set to the target vertex  301 . The third term of the right side of the expression (1) is a value calculated from a value of the time series data  122  of the update target set to the inflow vertex  301 . The fourth term of the right side of the expression (1) is a value calculated from a value of the different time series data  122  of the inflow vertex  301 . It is to be noted that the third and fourth terms of the right side of the expression (1) represent sum values of the function which includes the value of the time series data  122  set to the inflow vertex  301  as a variable. 
     The expression (1) is a differential equation called reaction diffusion equation. The first and second terms of the right side of the expression (1) are each called reaction term, and the third and fourth terms of the right side of the expression (1) are each called diffusion term. 
     Since the values of same data act in a direction in which they increase the update value as indicated by the expression (1), the first and third terms have the positive sign. On the other hand, since values of different data act in a direction in which they decrease the update value, the second and fourth terms have the negative sign. 
     It is to be noted that, when four time series data  122  are inputted to the graph  300 , an expression for determining an update value for the time series data A of the target vertex  301  and an update expression for the time series data A of the target vertex  301  are given by expressions (3) and (4) below, respectively. 
     
       
         
           
             
               
                 
                   
                       
                   
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     The expression (3) is different from the expression (1) in that it includes an increased number of variables of the functions g and l. The general form of the update expressions does not rely upon the number of time series data  122  to be inputted to the graph  300 . 
     Here, the process at step S 505  is described using the portion in the frame  310  of  FIG. 3A . Here, it is assumed that the update value is determined in accordance with the expression (5) given below. It is assumed that the value of the time series data A of the vertex I 0  ( 301 ) is “0” and the value of the time series data B of the vertex I 0  ( 301 ) is “10.” Further, it is assumed that the value of the time series data A of the vertex I 1  ( 301 ) is “10” and the value of the time series data B of the vertex I 1  ( 301 ) is “20.” Furthermore, the value of the time series data A of the vertex I 6  ( 301 ) is “20,” and the value of the time series data B of the vertex I 6  ( 301 ) is “5.” 
     
       
         
           
             
               
                 
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     In this case, the updated value of the time series data A of the vertex I 0  ( 301 ) is “3” as indicated by the following expression (6):
 
[Expression 6]
 
Δ D   i,A =10×0−0 2 −2+(10+20)−(20+5)=3  (6)
 
     Accordingly, the value of the column of the time series data A in the vertex data region  404  in the entry corresponding to the vertex I 0  ( 301 ) is updated from “0” to “3.” It is to be noted that, at this point of time, the data processing unit  120  does not update the value of the time series data  122  of the vertex I 0  ( 301 ). The data processing unit  120  stores the update values which associate the identifier of the vertex  301  and the identifier of the time series data  122  with each other into the memory  111 . Such processes as described above are executed at step S 505 . 
     Then, the data processing unit  120  updates the value of each vertex  301  on the basis of a result of the update value calculation process (step S 506 ). In particular, the data processing unit  120  adds the update value to the vertex data region  404  of the entry corresponding to each vertex  301  included in the graph  300 . At this time, the data processing unit  120  adds “1” to the variable representative of the number of times of execution of the loop process. 
     Thereafter, the data processing unit  120  decides whether or not the number of times of execution of the loop process is greater than a predetermined threshold value (step S 507 ). 
     If the number of times of execution of the loop process is equal to or smaller than the predetermined threshold value, then the data processing unit  120  returns the processing to step S 505  to execute similar processes. If the number of times of execution of the loop process is greater than the predetermined threshold value, then the data processing unit  120  stores the structure data  400  as pattern data  123  into the memory  111  (step S 508 ). Thereafter, the data processing unit  120  ends the pattern data generation process. 
     It is to be noted that the data processing unit  120  may output the structure data  400 , from which the columns for the outflow vertex ID  402  and the inflow vertex ID  403  are deleted, as pattern data  123 . 
     In the embodiment 1, the values of the vertexes  301  in the vertex data region  404  are not initialized. This is because to allow the values to be left makes it possible to perform a process in which a result of processing in the preceding operation cycle is reflected. It is to be noted that the data processing unit  120  may initialize, after it stores the pattern data  123  into the memory  111 , the values of the vertexes  301  of the graph data  121  in the vertex data region  404 . 
     The data processing unit  120  disperses the value of time series data  122  inputted to a certain vertex  301  to vertexes  301  of the graph  300  by repetitively executing the loop process by the predetermined number of times. Since values set to the vertexes  301  disperse while interacting with each other as indicated by the expression (3), the values of the time series data  122  set to the vertexes  301  are different from each other. Accordingly, the pattern data  123  is data indicative of a distribution of values of the time series data  122  and forms a geometrical pattern if differences in value are visualized. 
     As depicted in  FIG. 7 , the graph  300  to which no time series data  122  is inputted does not form any pattern. On the other hand, if the values of the time series data  122  at a certain point of time are inputted to the graph  300 , then pattern data  123  which forms a pattern is outputted. 
     The pattern data  123 - 1  represents data outputted by the pattern data generation process for the first time. The pattern data  123 - 2  represents data outputted by the pattern data generation process for the second time; the pattern data  123 - 3  represents data outputted by the pattern data generation process for the third time; and the pattern data  123 - 4  represents data outputted by the pattern data generation process for the fourth time. In the embodiment 1, every time the pattern data generation process is executed, one pattern data  123  is generated. 
     Generally, a column included in the vertex data region  404  corresponds to protein and each value of time series data  122  stored in the column corresponds to a concentration, and a reaction diffusion equation relates to formation of a pattern of animals and plants or the like. 
     In this manner, the data processing unit  120  can generate, from a plurality of time series data  122 , pattern data  123  indicative of a transient response which takes an influence between data into consideration. In other words, the data processing unit  120  can generate various pattern data  123  from a plurality of input data. 
     The data processing unit  120  executes an identification process using the pattern data  123  generated by the pattern data generation process. 
       FIG. 8  is a diagrammatic view illustrating an example of an inputting method of pattern data  123  to the identifier in the embodiment 1.  FIG. 9  is a view depicting an example of a list used when a converter in the embodiment 1 converts data of a vertex. 
     First, the data processing unit  120  inputs values to be set to the vertexes  301  of the pattern data  123  to a converter  801  such that the values are converted into data of a data format which can be handled by the identifier. 
     To one vertex  301 , values in number equal to the number of time series data  122  inputted to the graph  300  are set. It is necessary to input, to a CNN  802  which is used as an identifier, one-dimensional data to one vertex  301 . Therefore, the converter  801  converts a plurality of values set to one vertex  301  into one-dimensional data. 
     For example, the converter  801  may use the following conversion method in which such a list  900  which associates data IDs and conversion values with each other as depicted in  FIG. 9  is used. The converter  801  refers to a plurality of values set to one vertex  301  and specifies an identifier of the time series data  122  which exhibits the highest value. The converter  801  inputs a conversion value corresponding to the specified identifier of the time series data  122  to the CNN  802  based on the list  900 . It is to be noted that the conversion value is stored into the memory  111 . 
     For example, in the case of a vertex  301  with regard to which the value of the time series data A is “10”; the value of the time series data B is “20”; the value of the time series data C is “0”; and the value of the time series data D is “5,” a conversion value “0.25” is inputted as the value of the vertex  301  to the CNN  802 . 
     Upon learning, the data processing unit  120  inputs pattern data  123  generated from time series data  122  for learning to the CNN  802 . In the identification process, the data processing unit  120  inputs pattern data  123  generated from time series data  122  for identification to the CNN  802 . 
     The CNN  802  outputs a value corresponding to a class into which the pattern data  123  is to be classified. For example, if the output value indicative of a value of a first class is “0.5” or more and besides the output value of the other classes is lower than “0.5,” then the inputted data is classified into the first class. 
       FIG. 10  is a view illustrating identification accuracy of an identification process of the computer  101  in the embodiment 1. 
     In  FIG. 10 , identification accuracy of an identification process which classifies data into four classes is illustrated 
     It is to be noted that the value in each parentheses of identification accuracy represents identification accuracy when the output value for each class is “0.5” or more. For example, a case is considered in which the number of test sample data to be inputted to the first class is 1000 and the output value of the first class is “0.5” or more and besides the number of data whose identification result is the first class is 250. If the number of test sample data whose output value of the first class is lower than 0.5 is 50 from among the  250  test sample data, then the detection accuracy degree is 80% as indicated by the expression (7) given below. 
                   [     Expression   ⁢           ⁢   7     ]                                 (     250   -   50     )     250     ×   100     =     80   ⁡     [   %   ]               (   7   )               
As depicted in  FIG. 10 , it is demonstrated that the identification accuracy of the identification process is improved by using the pattern data  123 .
 
     Although, in the present embodiment, an NN or a CNN is used as an identification unit, by suitably changing the conversion method of the converter  801 , it is possible to cope also with a different identification algorithm such as a support vector machine. 
     (Modification to Embodiment 1) 
     Here, a pattern data generation process based on a simulation is described.  FIG. 11  is a flow chart illustrating a modification to the pattern data generation process executed by the data processing unit  120  in the embodiment 1. 
     The data processing unit  120  starts a loop process for simulation time after it acquires graph data  121  (step S 1101 ). In particular, the data processing unit  120  sets the time most in the past of time series data  122  to a variable representative of simulation time. Thereafter, the data processing unit  120  executes the process repetitively until the simulation time coincides with the time most in the future of the time series data  122 . 
     The data processing unit  120  acquires the values of the time series data  122  at the simulation time from the storage system  102  and stores the acquired values of the time series data  122  into the memory  111  (step S 1102 ). 
     After the data processing unit  120  stores pattern data  123  generated on the basis of the values of the time series data  122  at arbitrary simulation time into the memory  111  (step S 508 ), it decides whether or not the simulation time coincides with the time most in the future (step S 1103 ). 
     If the simulation time does not coincide with the time most in the future, the data processing unit  120  updates the simulation time and then returns the processing to step S 1102  to execute similar processes. If the simulation time coincides with the time most in the future, then the data processing unit  120  ends the pattern data generation process. 
     Embodiment 2 
     In an embodiment 2, a different input value from that in the embodiment 1 is used. In the embodiment 2, the data processing unit  120  converts a value of time series data  122  into a number of particles and inputs a predetermined number of particles as an input value to the graph  300 . In the following, the embodiment 2 is described focusing on the difference from the embodiment 1. 
     The computer system  100  of the embodiment 2 has a configuration same as that of the computer system  100  of the embodiment 1, and therefore, description of the same is omitted herein. Further, the computer  101  and the storage system  102  in the embodiment 2 have configurations same as those of the computer  101  and the storage system  102  in the embodiment 1, and therefore, description of them is omitted herein. 
     Further, the structure data  400  of the graph data  121  in the embodiment 2 is same as the structure data  400  of the graph data  121  in the embodiment 1 and therefore, description of the same is omitted herein. The inflow definition data  410  of the graph data  121  in the embodiment 2 does not include the standardization constant  413 . The time series data  122  in the embodiment 2 is same as the time series data  122  in the embodiment 1, and therefore, description of the same is omitted herein. 
       FIG. 12  is a flow chart illustrating a pattern data generation process executed by the data processing unit  120  in the embodiment 2.  FIGS. 13 and 14  are diagrammatic views illustrating a concept of an inflow process of time series data  122  and an outflow process of particles in the embodiment 2. 
     The data processing unit  120  acquires values of the time series data  122  at the present point of time (step S 502 ) and then executes an inflow process of the time series data  122  (step S 1201 ). In particular, the following processes are executed. 
     The data processing unit  120  generates columns in number equal to the number of the time series data  122  in the vertex data region  404  of the structure data  400 . The data processing unit  120  sets “0” as an initial value to the generated columns. It is to be noted that, if columns are generated already in the vertex data region  404 , then the process just described is omitted. 
     The data processing unit  120  selects a value of a target from among the acquired values of the time series data  122 . The data processing unit  120  converts the target value into a number of particles. 
     Various conversion algorithms may be applied to convert a value into a number of particles. For example, if the value includes a fraction, then an algorithm which converts the value into an integer by rounding up, rounding down or rounding off may be applied. Alternatively, another algorithm which converts a value into a number of particles by scaling may be applied. It is to be noted that different conversion algorithms may be set to different ones of the time series data  122 . 
     The data processing unit  120  refers to the data ID  411  of the inflow definition data  410  to search for an entry which coincides with the identifier of the time series data  122  corresponding to the target value. The data processing unit  120  acquires an identifier of the vertex  301  from the vertex ID  412  in the searched out entry. The data processing unit  120  refers to the vertex ID  401  of the structure data  400  to search for an entry which coincides with the acquired identifier of the vertex  301 . 
     The data processing unit  120  refers to the vertex data region  404  in the searched out entry to specify a column in which the input value (number of particles) is to be set. The data processing unit  120  updates the value in the column specified using the input value. For example, the data processing unit  120  overwrites the number of particles in the specified column or adds the number of particles to the value stored in the specified column. 
     The data processing unit  120  executes the processes described above for all values of the time series data  122 . 
     For example, where the value of time series data  122  is “10,” the value is converted into a number of particles “10,” which is placed into the column of the vertex data region  404  for the predetermined vertex  301 . 
     Where a value of time series data  122  is handled as a number of particles, the sum total of the number of particles of the time series data  122  included in the graph  300  does not vary in principle. 
     After the process at step S 1201 , the data processing unit  120  starts a loop process for generating pattern data  123  (step S 504 ). Upon starting of the loop process, the data processing unit  120  sets “1” to the variable indicative of a number of times of execution of the loop process. 
     The data processing unit  120  executes an updating process of a value (step S 1202 ). In the updating process of a value, the data processing unit  120  updates the value set to each vertex  301  by moving a predetermined number of particles to each vertex  301 . In particular, the following processes are executed. 
     The data processing unit  120  refers to the vertex data region  404  of the structure data  400  to select a type of a particle of a target. The data processing unit  120  refers to the vertex data region  404  of the structure data  400  to search for an entry in which the value of a column corresponding to the type of the target particle is higher than “0.” In other words, a vertex  301  to which a particle corresponding to the type of the particle of the processing target is set is searched out. The data processing unit  120  selects one vertex  301  (entry) of the target from within the searched out entry. 
     The data processing unit  120  acquires the inflow vertex ID  403  of the selected entry and searches for an entry in which the vertex ID  401  coincides with the identifier of the acquired vertex  301 . 
     The data processing unit  120  acquires a number of particles of the type of the target particle set to the target vertex  301  and a number of particles of the type of the target particle set to the inflow vertex  301 . 
     The data processing unit  120  selects one target particle from among the particles corresponding to the type of the target particle. It is to be noted that, since the particles of the same type are not identified from each other, any one of the particles may be selected. 
     The data processing unit  120  determines the vertex  301  as a movement destination of the selected particle on the basis of the number of particles set to each vertex  301 . The vertex  301  of the movement destination of the target particle is determined by an interaction between the number of particles set to the own vertex  301  and the number of particles set to the inflow vertex  301 . 
     In the embodiment 2, since an interaction is replaced into a movement of a particle, an interaction formula is given as a movement determination expression of a particle. 
     For example, where the vertex data region  404  in the selected entry includes columns for the time series data A and the time series data B, the movement determination expression of a particle of the time series data A of the vertex  301  of the processing target is given by the following expression (8). 
                   [     Expression   ⁢           ⁢   8     ]                             P     i   ,   A       =       f   ⁡     (     N     i   ,   A       )       -     g   ⁡     (     N     i   ,   B       )       +       ∑   j     ⁢     h   ⁡     (     N     j   ,   A       )         -       ∑   j     ⁢     l   ⁡     (     N     j   ,   B       )                   (   8   )               
where N i,A  represents a number of particles of the time series data A before updating set to the target vertex  301  whose identifier is “i”; N i,B  a number of particles of the time series data B before updating set to the target vertex  301  whose identifier is “i”; f(N i,A ) a function which includes N i,A  as a variable; g(N i,B ) a function which includes N i,B  as a variable; h(N j,A ) a function which includes, as a variable, a number of particles of the time series data A set to an inflow vertex  301  whose identifier is “j”; and l(N j,B ) a function which includes, as a variable, a number of particles of the time series data B set to the inflow vertex  301  whose identifier is “j.”
 
     The data processing unit  120  uses the value of P i,A  and the threshold value to decide whether or not one of particles of the time series data A set to the target vertex  301  is to be moved to the inflow vertex  301 . For example, where P i,A  is higher than 0, the data processing unit  120  decides that one particle is to be moved, but where P i,A  is equal to or lower than 0, the data processing unit  120  decides that one particle is not to be moved. 
     It is to be noted that, where a plurality of inflow vertexes  301  are involved, the data processing unit  120  selects one inflow vertex  301  as the movement destination from among the plurality of inflow vertexes  301 . 
     When a particle is to be moved to an inflow vertex  301 , the data processing unit  120  refers to the vertex data region  404  in the entry of the target vertex  301  to decrement the number of target particles by one. Further, the data processing unit  120  refers to the vertex data region  404  in the entry of the inflow vertex  301  and increments the number of target particles by one. 
     When a particle is not to be moved to the inflow vertex  301 , the data processing unit  120  does not perform updating of the vertex data region  404  in the entries. 
     The data processing unit  120  executes the processes described above for all particles of the selected type. Further, the data processing unit  120  executes the same processes for the particles of all types set to the target vertex  301 . 
     It is to be noted that, where an attribute such as the distance is applied to the side  302 , a particle may be moved taking the attribute into consideration. For example, where “5” is set as the distance to the side  302 , a particle may be moved after the loop process from step S 504  to step S 507  is executed by five times. 
     In the embodiment 2, since a particle is moved to a vertex  301  determined in accordance with the expression (8), such updating as at step S 506  in the embodiment 1 is not performed. For example, if the number of particles of the time series data B is “2” and one particle is moved to the inflow vertex  301 , then the number of particles of the time series data B upon execution of the decision process using the expression (8) for the other particles of the time series data B becomes “1.” Such processes as described above are executed at step S 1202 . 
     Thereafter, the data processing unit  120  executes an outflow process of a particle (step S 1203 ). In the outflow process of a particle, a particle which satisfies a predetermined condition is outputted (deleted) from the graph  300 . In particular, the following processes are executed. 
     In the embodiment 2, a particle is inputted to the graph  300 . Therefore, every time series data  122  is inputted, the number of particles stored in the graph  300  increases. Therefore, the data processing unit  120  deletes a particle which satisfies a predetermined condition from the graph  300  to decrease the number of particles stored in the graph  300 . As a method for deleting a particle from the graph  300 , such a method as illustrated in  FIG. 13  or  FIG. 14  may be performed. 
     In  FIG. 13 , the data processing unit  120  in the present embodiment outputs, after execution of an updating process of a value, a particle which has moved to a vertex  301  on an outer periphery of the lattice-like graph  300  to a path  1300 . The particle is discharged from an outlet  1301  through the path  1300 . In other words, the particle is deleted from the graph  300 . As the data process, the number of particles set to the vertex  301  is initialized. It is to be noted that the vertex  301  from which a particle is to be outputted is not limited to a vertex  301  on the outer periphery. 
     As a method for setting a vertex  301  from which a particle is to be outputted, a method which provides a column for identification of a vertex  301  from which a particle is to be outputted in the structure data  400  may be available. 
     As particular processes, the data processing unit  120  refers to the structure data  400  and stores the total value of the values in all columns of the vertex data region  404  in an entry corresponding to a vertex  301  on the outer periphery as a number of output particles into the memory  111 . Further, the data processing unit  120  sets the values in all columns of the vertex data region  404  in the entry to “0.” 
     By summing the number of particles stored in the graph  300  and the number of output particles, the total number of inputted particles can be grasped. 
     In  FIG. 14 , the memory  111  includes a particle storage region  1400  for storing particles for each time series data  122 . In the particle storage region  1400 , a predetermined number of particles are stored in advance. In particular, a value indicative of a number of particles is stored in each particle storage region  1400 . It is to be noted that the particle storage regions  1400  are generated upon starting of a pattern data generation process. 
     In the inflow process of time series data  122 , the data processing unit  120  converts values of the time series data  122  into the number of particles, extracts a number of particles equal to the number of particles from the corresponding particle storage region  1400  and inputs the extracted particles to a vertex  301 . At this time, the data processing unit  120  decrements the value in the particle storage region  1400  by a value equal to the number of extracted particles. 
     In the outflow process of a particle, the data processing unit  120  stores particles to be set to a vertex  301  on the outer periphery into the particle storage region  1400  through the path  1300  and the outlet  1301 . As a data process, the data processing unit  120  increments the value in the particle storage region  1400  by the value in the vertex data region  404 . 
     It is to be noted that, if, in the inflow process of time series data  122 , a number of particles equal to the number of converted particles are not stored in the particle storage region  1400 , then the data processing unit  120  inputs particles in number equal to the number of particles which can be extracted to the graph  300 . For example, if the number of converted particles is “10” and the number of particles in the particle storage region  1400  is “3,” then the data processing unit  120  inputs three particles to the graph  300 . Consequently, the influence of time series data  122  having a high strength decreases. This is applied incorporating the nature that a cell which is excited remains less sensitive for a while. 
       FIGS. 15A and 15B  are diagrammatic views illustrating examples of the pattern data  123  in the embodiment 2. 
     Here, pattern data  123  when two time series data groups having different phases from each other are inputted are depicted. One of the time series data groups includes four time series data  122 . Further, it is assumed that the graph  300  to be used is a lattice-like graph  300 . Further, it is assumed that the management method for a particle illustrated in  FIG. 14  is adopted. 
     In  FIGS. 15A and 15B , a vertex  301  to which no particle is inputted is represented by a blank round mark, and the vertexes  301  are indicated in different colors in accordance with the type of particles whose number of particles is greatest. As depicted in  FIGS. 15A and 15B , every time the pattern data generation process is executed, one pattern data  123  is generated. If time series data  122  of a different phase are inputted, then pattern data  123  of different patterns are outputted. By using such pattern data  123  as just described, the identification accuracy of the identification process can be improved. 
     Embodiment 3 
     In an embodiment 3, a region for a graph  300  is divided for every time series data  122 . Further, in the embodiment 3, a value of time series data  122  is converted into a number of particles. In the following, the embodiment 3 is described focusing on the differences thereof from the embodiment 2. 
     The computer system  100  of the embodiment 3 has a configuration same as that of the computer system  100  of the embodiment 1, and therefore, description of the same is omitted herein. Further, the computer  101  and the storage system  102  in the embodiment 3 have configurations same as those of the computer  101  and the storage system  102  in the embodiment 1, and therefore, description of them is omitted herein. 
     Further, the inflow definition data  410  of the graph data  121  in the embodiment 3 does not include the standardization constant  413 . Since the time series data  122  in the embodiment 3 is same as the time series data  122  in the embodiment 1, and therefore, description of the same is omitted herein. 
     In the embodiment 3, the structure data  400  of the graph data  121  is different.  FIG. 16  is a view illustrating an example of the structure data  400  of the graph data  121  in the embodiment 3.  FIG. 17  is a view illustrating an example of the graph  300  in the embodiment 3. 
     The structure data  400  includes, in addition to the vertex ID  401 , outflow vertex ID  402 , inflow vertex ID  403  and vertex data region  404 , an active vertex ID  1601  and a suppression vertex ID  1602 . 
     The active vertex ID  1601  is an identifier of a vertex  301  which provides a retention action to a particle set to the vertex  301 . The suppression vertex ID  1602  is an identifier of a vertex  301  which provides a movement action to a particle set to the vertex  301 . A difference in action between vertexes is represented as a difference in polarity in the expression (8). The retention action corresponds to the second and fourth terms of the expression (8) and the active action corresponds to the first and third terms of the expression (8). 
     In the embodiment 3, a flow of a particle between vertexes  301  is managed with the outflow vertex ID  402  and the inflow vertex ID  403 , and an interaction between vertexes  301  is managed with the active vertex ID  1601  and the suppression vertex ID  1602 . 
     The graph  300  corresponding to the structure data  400  illustrated in  FIG. 16  is the graph  300  depicted in  FIG. 17 . The graph  300  is divided into a plurality of regions  1700  in advance in accordance with the number of time series data  122  inputted to the graph  300 . Further, vertexes  301  which have a same relative position in the regions  1700  are connected to each other by a side  1701  which provides a suppression action. 
     The graph  300  depicted in  FIG. 17  is structured such that a particle moves in an upward direction from below. Accordingly, a value of each time series data  122  is inputted to a vertex  301  at the lowermost position. Further, a particle having moved to a vertex  301  at the uppermost position is outputted to the particle storage region  1400  through the path  1300  and the outlet  1301 . 
     By using such a graph  300  as just described, a history in the time direction can be left. In particular, around vertexes  301  at a lower position, an influence of a new value of time series data  122  is reflected, but around vertexes  301  at an upper position, an influence of an old value of time series data  122  is reflected. 
     It is to be noted that the structure of the graph  300  depicted in  FIG. 17  is an example, and the structure of the graph  300  is not limited to this. The graph  300  can be configured in various structures by changing the outflow vertex ID  402 , inflow vertex ID  403 , active vertex ID  1601  and suppression vertex ID  1602 . For example, a multilayer bipartite graph such as, for example, a scale-free graph, a random graph and a neural network can be configured. 
       FIG. 18  is a diagram illustrating an example of the time series data  122  inputted upon generation of pattern data  123  in the embodiment 3.  FIG. 19  is a diagrammatic view illustrating an example of the pattern data  123  in the embodiment 3. 
     It is assumed that two such time series data  122  as illustrated in  FIG. 18  are inputted to the graph  300 . The graph  300  is such a lattice-shaped graph as depicted in  FIG. 17  and is divided into two regions  1700 - 1  and  1700 - 2 . It is assumed that a value of each time series data  122  is inputted to a vertex  301  at the center of a lowermost position of each of the regions  1700 - 1  and  1700 - 2 . Further, it is assumed that a particle is outputted from a vertex  301  at an uppermost position of each of the regions  1700 - 1  and  1700 - 2 . Furthermore, it is assumed that a suppression action acts between the region  1700 - 1  and the region  1700 - 2  as depicted in  FIG. 17 . 
     In  FIG. 19 , the display format of a vertex  301  is changed in response to the number of particles set to the vertex  301 . 
     As depicted in  FIG. 19 , pattern data  123  on which a history is reflected from a vertex  301  at a lower position toward a vertex  301  at an upper position of the graph  300  are generated. It can be seen that each of the region  1700 - 1  and the region  1700 - 2  is influenced by the number of particles in the other region  1700 . For example, a vertex  301  in the region  1700 - 2  influenced by a suppression action from a vertex  301  great in number of particle in the region  1700 - 1  is small in number of particle. 
     Embodiment 4 
     An embodiment 4 described below is directed to a control system for an apparatus in which the pattern data generation process described hereinabove in connection with the embodiments 1 to 3 is used. Here, description is given taking a system which measures a chemical substance or the like, identifies a class to which an identification target belongs and controls an apparatus on the basis of a result of the identification as an example. 
     The system includes two chemical sensors, one temperature sensor and one humidity sensor. It is to be noted that the types of the sensors are not limited to them, and various sensors such as a piezoelectric sensor, a gravity sensor, an optical sensor, an infrared sensor, a vibration sensor, an acceleration sensor, a thermal sensor, a speed sensor, a rotational speed sensor, a flow sensor and a sound sensor can be used. 
     The system periodically acquires a value measured by each sensor. The value acquired from each sensor corresponds to a value of time series data  122 . Further, in the system, a class to which an identification target belongs is identified using pattern data  123  generated from values acquired from the sensors. Further, it is assumed that the system determines contents of control of the apparatus on the basis of a result of the identification. 
     For example, where the system is a system for controlling a robot in which wheels and a motor are incorporated and uses three classes of “feed,” “natural enemy” and “others,” the following control is performed. If the identification result is “feed,” then the system controls the rotational speed of the motor and the direction of the wheels such that the robot approaches an arbitrary target. If the identification result is “natural enemy,” then the system controls the rotational speed of the motor and the direction of the wheels such that the robot moves away from an arbitrary target. Further, if the identification result is “others,” then the system determines contents of control such that a behavior in the preceding operation cycle is maintained. 
     It is assumed that the system has a hardware configuration and a software configuration similar to those of the computer  101  in the embodiment 1. 
       FIG. 20  is a flow chart illustrating a process to be executed by the system in the embodiment 4. 
     After the system is activated, the CPU  115  executes an initialization process (step S 2001 ). Thereafter, the CPU  115  starts a loop process (step S 2002 ). The loop process is executed periodically. The system continuously executes the loop process until after it receives an explicit stopping instruction of the process such as turning off of the power supply. 
     The CPU  115  executes a sensing process (step S 2003 ). In particular, the CPU  115  issues an instruction for measurement to the sensors. Each sensor performs measurement for the identification target. A result of the measurement is stored into the memory  111 . 
     Then, the CPU  115  executes a pattern data generation process (step S 2004 ). The pattern data generation process here may be any of the pattern data generation processes described hereinabove in connection with the embodiments 1 to 3. Pattern data  123  generated by the pattern data generation process is stored into the memory  111 . 
     Then, the CPU  115  executes an identification process using the pattern data  123  (step S 2005 ). For the identification process, the identification process described hereinabove in connection with the embodiment 1 is applied. A result of the identification is stored into the memory  111 . 
     It is to be noted that, as occasion demands, the CPU  115  may transmit the identification result to an external apparatus or may display the identification result on an outputting apparatus such as a display unit. It is to be noted that, as the displaying method of the identification result, a method for displaying using a figure, a table or characters may be applied. Further, where the system includes light emitting diodes (LEDs) corresponding to the classes, a method of causing the LED corresponding to the identification result to flicker or a like method may be applied. 
     Then, the CPU  115  determines contents of control of the apparatus on the basis of the identification result (step S 2006 ) and outputs a control signal for executing the determined control contents to the apparatus (step S 2007 ). If a predetermined condition is satisfied, the CPU  115  ends the loop process (step S 2008 ). 
     For example, the system retains programs to be executed for the individual classes as subroutines or libraries in advance and executes the program of the corresponding class on the basis of the identification result. 
     Where the system controls a robot in which wheels and a motor are incorporated, the CPU  115  determines the rotational speed of the motor and the direction of the wheels and outputs a control signal for changing the rotational speed of the motor and the direction of the wheels to the robot. Consequently, the robot can be controlled in accordance with the class. 
     Embodiment 5 
     An embodiment 5 is directed to a data analysis system which uses any of the pattern data generation processes described hereinabove in connection with the embodiments 1 to 3. Here, a big data process is taken as an example. 
     In internet of things (IoT), data are acquired from a plurality of apparatus connected through a network. By analyzing a large amount of data, useful knowledge can be obtained. Data acquired from apparatus may be logs of the purchase, stock price, exchange, weather, SNS and so forth. Further, as classes, demand expansion, demand reduction, maintenance of the status quo and so forth may be available. 
     Further, a data center acquires data indicative of a state of an apparatus included in the data center. By analyzing a large amount of data, optimum operation of the data center can be anticipated. 
     A particular flow of processes is same as that in the embodiment 4. However, data acquired as time series data  122  is values acquired from an apparatus or the like connected through a network. In the embodiment 5, the processes at steps S 2006  and S 2007  may not be executed. 
     It is to be noted that the present invention is not limited to the embodiments described hereinabove but includes various modifications. Further, the embodiments in the foregoing description have been described in detail in regard to the configuration in order to facilitate understandings of the present invention, and the present invention is not necessarily limited to the embodiments which include all of the constructs described hereinabove. Further, it is possible to add, delete or replace some constructs of the embodiments to, from or with other constructs. 
     Further, the constructs, functions, processing units, processing means and so forth described above may be partly or entirely implemented by hardware, for example, by designing them in an integrated circuit or the like. Further, the present invention can be implemented also by a program code of software which implements the functions of the embodiments. In this case, a storage medium in which the program code is recorded is provided to a computer, and a processor provided in the computer reads out the program code stored in the storage medium. In this case, the program code itself read out from the storage medium implements the functions of the embodiments, and the program code itself and the storage medium in which the program code is stored constitute the present invention. As the recording medium for supplying such a program code as described above, for example, a flexible disk, a compact disc read only memory (CD-ROM), a digital versatile disc (DVD)-ROM, a hard disk, a solid state drive (SSD), an optical disk, a magneto-optical disk, a CD-recordable (R), a magnetic tape, a nonvolatile memory card, a ROM and so forth are used. 
     Further, the program code for implementing the functions described in the description of the embodiments can be incorporated in a wide range of programs such as, for example, an assembler, C/C++, perl, Shell, professional hypertext preprocessor (PHP) or Java (registered trademark) or a script language. 
     Further, the program code of software which implements the functions of the embodiments may be distributed through a network such that it is stored into storage means of a computer such as a hard disk or a memory or into a storage medium such as a CD-rewritable (RW) or a CD-R and a processor provided in the computer reads out and executes the program code stored in the storage means or the storage medium. 
     In the embodiments described above, control lines and information lines only necessary for description of the embodiments are indicated, but all control lines or information lines necessary for a product are not necessarily indicated. All constructs may be connected to each other.