Abstract:
A data management method employing the results of an analysis of data stored in a storage unit of a computer provided with a processor and a storage unit, wherein the computer generates an analysis data set by selecting data stored in the storage unit, subjects the analysis data set to prescribed data mining, extracts a model from the analysis data set, converts the model into a relational table, and associates the relational table with a dimension table and a history table that have been stored in advance in the storage unit.

Description:
BACKGROUND 
       [0001]    The present invention relates to a technique of using information attained by data mining in an existing application. 
         [0002]    In the real world surrounding us, as a result of the development of the Web, a large amount of data transmitted on the basis of the behavior of people and data transmitted on the basis of movement of objects has been generated. There are many cases in which such data is condensed and data analysis methods for understanding trends have not been determined in advance. As a result, there is a need for methods to obtain rules to understand data and construct models through trial and error. 
         [0003]    Data mining is a method for extracting rules from data and constructing models, and specifically, an object thereof is to “extract, from a large amount of data, unknown rules, and unknown models, that is, new information that cannot be obtained by human observation alone.” Non-Patent Document 2 and Non-Patent Document 3 are known examples of data mining. Non-Patent Document 1 is known as a technique for analyzing data stored in a database. 
       RELATED ART DOCUMENTS 
       [0000]    
       
         Non-Patent Document 1: “Oracle Database Data Warehousing Guide,” [online], [searched on Aug. 1, 2013], Internet &lt;URL:
       http://docs.oracle.com/cd/B28359 — 01/server.111/b28313/schemas.htm&gt;   
     
         Non-Patent Document 2: “IBM SPSS Modeler 14.2 User&#39;s Guide,” [online], [searched on Aug. 1, 2013], Internet &lt;URL: http://faculty.smu.edu/tfomby/eco5385/data/SPSS/SPSS%20Modeler — 14 — 2_UsersGuide.pdf&gt; 
         Non-Patent Document 3: Han, J., Kamber, M., and Pai, J., “Data Mining: Concepts and Techniques, Third Edition,” Morgan Kaufmann Publishers (2011). 
       
     
       SUMMARY 
       [0008]    In recent years, there is increasing demand for using information (rules or models) or knowledge obtained by analysis in data mining, and finding the overall picture of other data, the relationship between data, or underlying structures. 
         [0009]    However, in order to combine information obtained by data mining with online analytical processing (OLAP) of an information system owned by a company or with data analysis such as statistical analysis, or to combine information obtained by data mining with business applications on enterprise systems, the information must be processed individually at the level of each application. Thus, in order to apply information obtained by data mining or the like to existing enterprise systems or information systems, it is necessary to add and modify complex data processes such as data modeling and data processing for each application, which requires a large amount of work. 
         [0010]    The present invention takes into account the above-mentioned problem, and an object thereof is to apply information obtained by data mining or the like to existing enterprise systems and information systems with ease. A representative aspect of the present disclosure is as follows. A data management method using results of analyzing data stored in a storage module by a computer comprising a processor and the storage module, the data management method comprising: a first step of selecting, by the computer, data stored in the storage module, and generating, a data set for analysis; a second step of performing, by the computer, prescribed data mining on the data set for analysis, and extracting, a model from the data set for analysis; a third step of converting, by the computer, the model to a relational table; and a fourth step of associating, by the computer, with a dimension table and a history table stored in advance in the storage module in association with the relational table. 
         [0011]    According to the present invention, it is possible to use models extracted by data mining without modifying existing business applications. Also, it is possible to extract models by performing analysis and evaluation repeatedly on the same data set for analysis using different parameters. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]      FIG. 1  is a block diagram showing one example of a data management device of an embodiment of this invention 
           [0013]      FIG. 2  is a schematic view showing an example of a process performed by the data management device of an embodiment of this invention. 
           [0014]      FIG. 3  is a block diagram indicating a relation between the database, the data warehouse, the data set for analysis, and the model of an embodiment of this invention. 
           [0015]      FIG. 4  is a flowchart showing one example of a process performed in an information system and an enterprise system of an embodiment of this invention. 
           [0016]      FIG. 5  shows an example of clustering performed by the data mining module of the data management device of an embodiment of this invention. 
           [0017]      FIG. 6  shows an example of a decision tree executed by the data mining module of the data management device of an embodiment of this invention. 
           [0018]      FIG. 7  is an example of the definition of the star schema of an embodiment of this invention. 
           [0019]      FIG. 8  shows the relation between data when generating the star schema of an embodiment of this invention. 
           [0020]      FIG. 9  is a flowchart showing an example of the table definition process performed by the data management device of an embodiment of this invention. 
           [0021]      FIG. 10  is a flowchart showing an example of a process performed by the data loading processor of the data management device of an embodiment of this invention. 
           [0022]      FIG. 11  shows an example of the clustering results being added to the data warehouse of an embodiment of this invention. 
           [0023]      FIG. 12  shows an example of the data set for analysis selected by the data selection module of an embodiment of this invention. 
           [0024]      FIG. 13  shows an example of a relational table of an embodiment of this invention. 
           [0025]      FIG. 14  is a flowchart showing one example of a process performed by the data management device in which the clustering results are converted to the relational table of an embodiment of this invention. 
           [0026]      FIG. 15  shows an example of the decision tree being obtained by extracting the decision tree from the data set for analysis of an embodiment of this invention. 
           [0027]      FIG. 16  shows an example of the data set for analysis of an embodiment of this invention. 
           [0028]      FIG. 17  is a schematic view showing an example of a prediction process performed by the data management device of an embodiment of this invention. 
           [0029]      FIG. 18  is a descriptive drawing showing another example of a prediction process performed by the data management device of an embodiment of this invention. 
           [0030]      FIG. 19  is a flowchart showing an example of the prediction process performed by the data management device of an embodiment of this invention. 
       
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
       [0031]    Hereinafter, embodiments for carrying out the present invention will be described in detail with reference to accompanying drawings. 
         [0032]      FIG. 1  is a block diagram showing an example of a data management device of an embodiment of the present invention. A data management device  1  obtains new information by performing data mining on data selected from a database  10  as a business application comprising an enterprise system, and executes a literacy extraction system  30  that causes the new information to be added to a business application  340  and a data warehouse  11 . 
         [0033]    The data management device  1  is a computer comprised of a CPU  8  that performs calculations, a main memory  2  that stores data and programs, an auxiliary storage device  4  that stores the database  10  and programs, a network interface  5  that allows communication with the network  500 , an auxiliary storage device interface  3  that reads from and writes to the auxiliary storage device  4 , input devices  6  including a keyboard and a mouse, and output devices  7  including displays, speakers, and the like. 
         [0034]    In the main memory  2 , an operating system (OS)  20  is loaded and executed by the CPU  8 . In the OS  20 , new literacy is obtained on the basis of data in the database  10  and the data warehouse  11 , and a literacy extraction system  30  that adds this new information to the business application  340  and the data warehouse  11  operates. 
         [0035]    The literacy extraction system  30  is comprised of an enterprise system and an information system. The enterprise system is comprised of the business application  340  and a prediction OLAP analysis  330 . The business application  340  is comprised of a database management system (DBMS) that manages the database  10 , for example. DB 1 -DB 4  in the drawing are databases for each operation. 
         [0036]    Meanwhile, the information system includes a table definition processing module  310 , a data loading processing module  320 , a data cleansing module  410 , a data selection module  420 , a data mining module  430 , a model evaluation module  440 , and an literacy applying module  450  as processors. The prediction OLAP analysis  330  may be used in the information system. 
         [0037]    As will be described later, in the information system, the data cleansing module  410  performs cleansing on data in the database  10 , and stores the data in the data warehouse  11 . The data selection module  420  selects data to be analyzed from among data stored in the data warehouse  11 , and outputs the data set for analysis  12 . Next, the data mining module  430  analyzes the data set for analysis  12  and extracts a model  13 . Next, the model evaluation module  440  evaluates the model  13 , and if it is useful literacy, then the model evaluation module  440  causes the new literacy to be added to the business application  340  using the literacy applying module  450 . The data of the data warehouse  11  may be used from the enterprise system. 
         [0038]    The CPU  8  is a functional module that realizes a prescribed function by executing a process according to programs in respective functional modules. For example, the CPU  8  functions as the table definition processing module  310  by executing a process according to a table definition program. The same applies for other programs. Additionally, the CPU  8  also operates as functional modules realizing, respectively, a plurality of processes executed by respective programs. The computer and the computer system are a device and system including these functional modules. 
         [0039]    Programs, data, data structures, and the like realizing respective functions of the literacy extraction system  30  can be stored in a storage device such as the auxiliary storage device  4 , a non-volatile semiconductor memory, a hard disk drive, or a solid state drive (SSD), or in a computer-readable non-transitory data storage medium such as an IC card, an SD card, or a DVD. 
         [0040]    The auxiliary storage device  4  stores the database  10  having data to be analyzed, a data warehouse  11  storing data and the like that has been selected from the database  10  to be analyzed, a data set for analysis  12  to be subject to data mining, and a model  13 , which is the result of data mining. 
         [0041]    Although not shown, as described above, it is possible to store programs of the OS  20  and literacy extraction system  30  in the auxiliary storage device  4 . 
         [0042]    Also, in  FIG. 1 , an example is illustrated in which DB 1  to Db 4 , which are comprised of relational databases (RDB), are stored in the database  10 , but this database  10  is original data to be analyzed, and can be comprised of a duplication or a portion of external databases. 
         [0043]    In the data management device  1  of the present invention, two processes are repeated: a process of extracting the model  13  from data in the database  10  using the data mining module  430 , and obtaining the model  13  as new literacy (use of literacy extraction process of  FIG. 2 ); and a process of applying the new literacy to the database  10  of the business application  340  (use of data analysis in  FIG. 2 ).  FIG. 2  is a schematic view showing an example of a process performed by the data management device. Below, a summary of the process performed by the data management device  1  of the present invention will be described with reference to  FIG. 2 . 
         [0044]    First, the data cleansing module  410  performs data cleansing on the database  10  generated by the enterprise system. In the data cleansing module  410 , erroneous or duplicate data is specified in the database  10 , and this data is removed in order to maintain consistency in the database  10 . The data in the database  10  that has been cleansed is stored in the data warehouse  11 . 
         [0045]    Next, the data selection module  420  selects data stored in the data warehouse  11  according to the purpose of the data mining, and generates a data set for analysis  12 . Then, the data mining module  430  performs a prescribed data mining process on the data set for analysis  12 , and extracts literacy such as unknown models. Examples of literacy include models  13  such as a decision tree  13 - 1  or clustering results  13 - 2 . A well-known or publicly known data mining method may be used, and details thereof will not be given here. 
         [0046]    In the model evaluation module  440 , the model obtained by the data mining module  430  is displayed in a visualization tool, and is obtained as useful literacy according to human evaluation or calculation of an evaluation value. The visualization tool is software that displays data in graphs, tables, or the like. The model evaluation module  440  is not limited to human evaluation, and evaluation may be performed by using software that calculates an evaluation value for the model  13  and evaluates the model  13  as useful literacy according to the size of the evaluation value. The evaluation value differs depending on the data mining method, but cases will be shown in which the model is a cluster or a decision tree. If the model is a cluster, then because human evaluation of clustering results is qualitative and subjective, evaluation is performed according to the size of an entropy value of each cluster in the clustering results as a quantitative evaluation scale, a cohesion value of each cluster calculated using squared error, a separation value among clusters using the distance between centroids of two clusters, and the like. In the model is a decision tree, then the cross-validation method is used to calculate how reliably predictions can be made by a decision tree created by learned data, and the model is evaluated according to the prediction accuracy. 
         [0047]    A model  13  comprised of the results of the model evaluation module  440  and decision tree or clustering results as useful literacy is extracted (S 1 ). As useful literacy, the definition of the model  13  may be set as new literacy in addition to the model  13  comprised of the decision tree or clustering results. 
         [0048]    Next, in the literacy applying module  450 , literacy (model) obtained by the model evaluation module  440  is added to the data of the business application  340  and the data of the data warehouse  11 . 
         [0049]    The literacy applying module  450  for the business application  340  can apply new literacy to the database  10  of the business application  340  by converting the model  13  including the extracted decision tree and clustering results to an SQL model (S 3 ). One method of converting the model  13  into an SQL model is, as described later, to obtain the decision tree by the data mining module  430  and express the decision tree or decision table in SQL. 
         [0050]    Also, the literacy applying module  450  for the data warehouse  11  converts the model  13  including the extracted decision tree  13 - 1  and clustering results  13 - 2  into the relational table  14  and then stores the relational table  13  in the data warehouse (DWH)  11  (S 2 ). The model  13  stored in the data warehouse  11  is added again to data mining and extraction of new literacy is performed. The relational table  14  can include clustering results, an SQL expression of a decision table, or an SQL expression of a decision tree, for example. 
         [0051]    The literacy extraction process comprised of the steps above is repeated, and newly obtained literacy (model  13 ) is used in the business application  340  and the data warehouse  11 , which means that a more sophisticated business analysis can be expected. 
         [0052]    The user of the data management device  1  may determine whether the newly obtained literacy (model  13 ) is used by the business application  340  or by the data warehouse  11 . After performing evaluation using the model evaluation module  440 , a command can be received from an input device  6  indicating whether the model  13  is to be used by the business application  340  or the data warehouse  11 , thereby allowing the user to determine whether the business application  340  or the data warehouse  11  is to use the model  13 , for example. 
         [0053]      FIG. 3  is a block diagram indicating a relation between the database  10 , the data warehouse  11 , the data set for analysis  12 , and the model  13 . The data management device  1  configures a star schema  130  according to a preset definition. 
         [0054]    In  FIG. 3 , an example is illustrated in which DB 1  to DB 4  (see  FIG. 1 ), which are comprised of relational databases (RDB), are stored in the databases  10 , but these databases  10  are original data to be analyzed, and can be comprised of a duplication or a portion of external databases. 
         [0055]    Among the data of the database  10 , data to be analyzed is sequentially extracted and used as a fact table  110  of the star schema  130 . 
         [0056]    The group of tables defined by the star schema  130  include the fact table  110  as original data of the database  10  and a plurality of dimension tables  120   a  to  120   d  defining data to be analyzed or aggregated. Below, the dimension tables will be collectively referred to as the database  10 . The fact table  110  and the dimension tables  120  ( 120   a  to  120   d ) are associated with main keys. 
         [0057]    In the example of  FIG. 3 , the structure of the star schema  130  includes dimension tables  120   a  to  120   d  for product, customer, period, and region, in relation to the fact table  110 . 
         [0058]    Thus, the dimension table  120   a  is a product dimension table relating to the product name (see  FIG. 8 ), the dimension table  120   b  is a period dimension table relating to the period (see  FIG. 8 ), the dimension table  120   c  is a customer dimension table relating to the customer (see  FIG. 8 ), and the dimension table  120   d  is a region dimension table relating to the region name (see  FIG. 8 ). 
         [0059]    Also, data from the star schema  130  to be stored in the data warehouse  11  is selected according to the purpose of the data mining, and the data set for analysis  12  is generated (see  FIGS. 11 ,  12 , and  16 ). 
         [0060]    Additionally, the model  13  including the decision tree and clustering results extracted by the data mining module  430  is converted to a relational table  14  of clustering results (see  FIGS. 11 and 13 ), or an SQL expression of the decision tree or decision table (see  FIGS. 15 and 17 ). 
         [0061]      FIG. 4  is a flowchart showing one example of a process performed in an information system and an enterprise system. The data cleansing module  410  performs cleansing of data in the database  10 . Data for which consistency was verified by the data cleansing module  410  is stored in the data warehouse  11  (DWH in the drawings). 
         [0062]    In the data warehouse  11 , the star schema  130  is configured from data of the database  10  on the basis of a preset definition  520  of the star schema. 
         [0063]    Next, the data selection module  420  extracts, from the star schema  130  of the data warehouse  11 , data to be analyzed as a data set for analysis  12  (learned data). The data set for analysis  12  is extracted by performing an inquiry such as association joining or aggregation on the plurality of dimension tables  120   a  to  120   d  and a history table (fact table  110 ) stored in the data warehouse  11 . 
         [0064]    The data mining module  430  performs data mining on the data set for analysis  12  extracted from the data warehouse  11 , and obtains the model  13  such as the decision tree  13 - 1  and the clustering results  13 - 2 . The decision tree  13 - 1  and the clustering results  13 - 2  are converted to the relational table  14 . 
         [0065]    The model evaluation module  440  displays in the output device  7  information obtained by the data mining module  430 , or in other words, the model  13 , such as the decision tree  13 - 1  and the clustering results  13 - 2 , and the relational table  14  using a visualization tool, and obtains this literacy as useful literacy through human evaluation and interpretation. Evaluation of the model on the basis of the prediction OLAP analysis  330  may be performed by the model evaluation module  440 . 
         [0066]    Meanwhile, the literacy applying module  450  converts the clustering results obtained as mentioned above to an SQL model, and then to the relational table  14  (see  FIGS. 11 and 13 ), and then stores the relational table  14  in the data warehouse  11  (S 2 ). Then, data mining is performed again by a different method or with the use of different parameters. 
         [0067]    If the obtained model  13  and relational table  14  are to be applied to the business application  340  of the enterprise system, then the relational table of the clustering results (see  FIGS. 11 and 13 ) and the relational table  14  obtained by converting the decision tree or the decision table to an SQL expression (see  FIGS. 15 and 17 ) are combined with the business application  340 , the relational tables being obtained from the model  13  including the extracted decision tree and clustering results (S 3 ). In this case, as described below, the model  13  is the decision tree  13 - 1  for performing predictions on attributes of new data using the prediction OLAP analysis  330 . 
         [0068]    In particular, the model evaluation module  440  creates the model  13  through trial and error by repeating analysis and evaluation with different categories and types. By defining category standards for income based on amount, the amount is converted to a category value of {high, low}, for example. The number of times a customer has accessed a website over a week is converted to a category defined as {low, mid, high}, with low being once, mid being 2 to 5 times, and high being 6 times or more. This type of data process is characterized in that analysis is repeated on the same data set for analysis  12  with different setting parameters for analysis such as data mining while changing the categories by trial and error. 
         [0069]      FIG. 5  shows an example of clustering performed by the data mining module  430  of the data management device  1 . Clustering involves calculating the distance between members of the data set for analysis  12  in a population on the basis of defined attributes, and members are categorized by similarity according to the distance between data points. 
         [0070]      FIG. 5  shows an example in which the data set for analysis  12  is data indicating the relation between the length of contract in months of a tablet and the age of the person who has signed the contract. “Manual” in the drawing indicates an example in which the data set for analysis  12  is categorized according to human experience or hypothesis. When categorized manually, it is possible to categorize the length of the contract as long or short, and the age of the person who has signed the contract as high or low, as shown in the drawing. 
         [0071]    By contrast, if the model  13  is set as the clustering results  13 - 2  by the data mining module  430 , then clusters that cannot be categorized by human experience or hypothesis can be extracted. In clusters  1  to  4 , distances between data points of each cluster are close, and in addition, a cluster N can be seen in which the age group is within a prescribed range (where the people who signed the contracts are middle aged), and includes the clusters  1  and  3 . In other words, by clustering, it is possible to obtain as the model the cluster N, which cannot be obtained by manual means. 
         [0072]    By performing evaluation on the clustering results using the model evaluation module  440 , it is possible to extract the middle aged group of the cluster N regardless of the length of the contracts, and it is possible to obtain literacy such as that for proposing business strategies for the middle aged group comprising the two clusters  1  and  3  included in the cluster N. 
         [0073]      FIG. 6  shows an example of a decision tree  13 - 1  executed by the data mining module  430  of the data management device  1 . The decision tree  13 - 1  is generated from past data and is a model to make predictions on new data. In the decision tree  13 - 1  shown in the drawing, recommended products are predicted on the basis of a person&#39;s occupation, age, tastes (like or dislike or movies), and whether or not the person has purchased a tablet. A user or the like of the data management device  1  sets the recommended products. 
         [0074]    By using the above decision tree  13 - 1  on new customer data, it is possible to predict the best products for each new customer. 
         [0075]    Next, an example of data that generates the star schema  130  is shown in  FIGS. 7 and 8 . 
         [0076]      FIG. 7  is an example of the definition  520  of the star schema  130 . In the table definition processing module  310 , the definition  520  of the star schema  130  of  FIG. 7  is read in, and the fact table (customer sale history table  110   a ) and the dimension tables  120   a  to  120   d  shown in  FIG. 8  are generated. 
         [0077]    The definition  520  includes definitions of the plurality of dimension tables  120   a  to  120   b  indicating the meaning of data in the database  10 , and a definition of a history table (fact table) storing the data of the database  10  as one-dimensional sequential data. 
         [0078]      FIG. 8  shows the relation between data when generating the star schema.  FIG. 8  shows an example of generating the dimension tables  120  and the fact table  110  (customer sale history table  110   a ) from the sale database of the database DB 1  included in the database  10  shown in  FIG. 1 . This process is performed in the table definition processing module  310  of the literacy extraction system  30  shown in  FIG. 1 . In the present embodiment, an example is shown in which the customer sale history table  110   a  is generated as the fact table  110 . 
         [0079]    The table definition processing module  310  generates the customer sale history table  110   a  from the sale database of the database DB 1 . The customer sale history table  110   a  is comprised of one record (or row) including a product identifier  111  for products sold, a customer identifier  112  for customers who have purchased such products, a region code  113  for regions where such products were sold, a period code  114  storing a period when such products were sold, a selling price  115  storing the price of products sold, and a number  116  of products sold. In the present embodiment, the product identifier  111 , the customer identifier  112 , the region code  113 , and the period code  114  of the customer sale history table  110   a  are handled as main keys including a plurality of identifiers, and the selling price  115  and the number  116  are handled as attributes. 
         [0080]    Next, the table definition processing module  310  generates from the database  10  the product dimension table  120   a  having as the main key the product identifier  111  of the customer sale history table  110   a . The product dimension table  120   a  is comprised of one record (or row) including the product identifier  121  as the main key, a product name  122 , and a contract length  129  in months. In the present embodiment, the product identifier  121  is handled as an identifier associated with the product identifier  111  of the customer sale history table  110   a , and the product name  122  is handled as an attribute. 
         [0081]    Next, the table definition processing module  310  generates from the database  10  the customer dimension table  120   c  having as the main key the customer identifier  112  of the customer sale history table  110   a . The customer dimension table  120   c  is comprised of a record (or row) including the customer identifier  125  as the main key, a customer name  126 , an age  126   a , an age  126   b , a occupation  126   c , an income  126   d , and a movie  126   e . In the present embodiment, the customer identifier  125  is handled as an identifier associated with the customer identifier  112  of the customer sale history table  110   a , and the customer name  126  to movies  126   e  are handled as attributes. 
         [0082]    Next, the table definition processing module  310  generates from the database  10  the region dimension table  120   d  having as the main key the region code  113  of the customer sale history table  110   a . The region dimension table  120   d  is comprised of one record (or row) including the region code  127  as the main key and the region name  128 . In the present embodiment, the region code  127  is handled as an identifier associated with the region code  113  of the customer sale history table  110   a , and the region name  128  is handled as an attribute. 
         [0083]    Next, the table definition processing module  310  generates from the database  10  the period dimension table  120   b  having as the main key the period code  114  of the customer sale history table  110   a . The period dimension table  120   b  is comprised of one record (or row) including the period code  123  as the main key and a period  124 . In the present embodiment, the period code  123  is handled as an identifier associated with the period code  114  of the customer sale history table  110   a , and the period  124  is handled as an attribute. 
         [0084]    As described above, the table definition processing module  310  adds identifiers as data to be analyzed and places the identifiers in correspondence with attributes associated therewith. The identifiers and the plurality of dimension tables  120 , in which attributes corresponding to the identifiers are stored as rows, are created. The customer sale history table  110   a  is generated in which the plurality of identifiers corresponding to the identifiers of the plurality of dimension tables and attributes corresponding to the plurality of identifiers are stored as associated with rows. 
         [0085]      FIG. 9  is a flowchart showing an example of the table definition processing module  310  performed by the data management device  1 . This process is executed on the basis of a command by a user of the data management device  1 . The data management device  1  starts the process of  FIG. 9  after reading in the definition  520  of the star schema  130  shown in  FIG. 7 . 
         [0086]    The data management device  1  defines the plurality of dimension tables  120   a  to  120   d  having main keys identifying the data to be analyzed and the plurality of attributes associated with the main keys as respective columns on the basis of the read-in definition  520  (S 11 ). 
         [0087]    The data management device  1  configures the main keys from the plurality of columns referring to the main keys of the plurality of dimension tables, and defines the history table  110   a  having as columns the plurality of attributes associated with the main keys (S 12 ). 
         [0088]    By the process above, as shown in  FIG. 8 , the plurality of dimension tables  120   a  to  120   d  indicating the meaning of the database  10  having real world data, and the customer sale history table  11   a  storing real world data as one-dimensional sequential data are generated. 
         [0089]      FIG. 10  is a flowchart showing an example of a process performed by the data loading processing module  320  of the data management device  1 . This process is executed after the process shown in  FIG. 9  is completed. Alternatively, the process is executed when a user or the like of the data management device  1  issues such a command through the input device  6 . 
         [0090]    The data loading processing module  320  loads data from the database  10  or the data warehouse  11  to the respective dimension tables  120   a  to  120   d  for analysis, which were generated by the table definition processing module  310  (S 21 ). 
         [0091]    Next, the data loading processing module  320  loads data from the database  10  to the customer sale history table  110   a  (fact table  110 ) for analysis, which was generated by the table definition processing module  310 . Then, the data loading processing module  320  loads the column data referring to the main keys of the dimension tables  120   a  to  120   d  and attributes associated with these columns as rows in the customer sale history table  110   a  (S 22 ). 
         [0092]    By the processes above, data from the fact table  110  (customer sale history table  110   a ) of the star schema  130 , and the database  10  of the dimension tables  120   a  to  120   d  are incorporated. 
         [0093]      FIG. 11  shows an example of the clustering results being applied to the data warehouse  11 . This process is executed after the process shown in  FIG. 9  is completed. 
         [0094]    The data mining module  430  performs data mining on the data set for analysis  12  extracted by the data selection module  420  from the data warehouse  11 .  FIG. 12  shows an example of the data set for analysis  12  selected by the data selection module  420 . In this example, the data set for analysis  12  configures one record from the customer ID  1211 , age  1212 , and length of contract  1213  in months. As for the elements comprising the data set for analysis  12 , the user of the data management device  1  selects data from the dimension tables  120   a  to  120   d  and the customer sale history table  110   a  using the input device  6  or the like. 
         [0095]    In the example of  FIG. 12 , the data selection module  420  obtains the customer ID  125  and the age  126   b  of the customer from the customer dimension table  120   c . Next, the data selection module  420  obtains the product identifier  111  corresponding to the customer ID  125  from the customer sale history table  110   a  and obtains the length of contract  129  in months corresponding to the product identifier  111  from the product dimension table  120   a . Then, the data selection module  420  couples the length of contract  129  with the customer ID  125  and age  126   b , writes data to the customer ID  1211 , age  1212 , and length of contract  1213  to generate the data set for analysis  12 . 
         [0096]    Next, as a result of performing clustering on the data set for analysis  12  using the data mining module  430 , the model  13 - 2  such as shown in  FIG. 11  is obtained. After being evaluated by the model evaluation module  440 , the literacy applying module  450  converts the model  13  of the clustering results  13 - 2  to the relational table  14 , as described later. 
         [0097]    The literacy applying module  450  stores the relational table  14  obtained by conversion from the clustering results  13 - 2  in the data warehouse  11 . The literacy applying module  450  extracts a tree structure from the model  13  of the clustering results  13 - 2 , converts the tree structure to SQL, and performs inquiries on the customer sale history table  110   a  and the dimension tables  120   a  to  120   d , thereby generating the relational table  14 . 
         [0098]    The literacy applying module  450  stores the obtained literacy in the data warehouse  11  as the relational table  14 , and performs association of the customer sale history table  110   a  and the dimension tables  120   a  to  120   d . In this manner, it is possible for the business application  340  and the like to perform inquiries on the customer sale history table  110   a , the dimension tables  120   a  to  120   d , and the relational table  14  stored in the data warehouse  11 . 
         [0099]      FIG. 13  shows an example of a relational table  14 . The relational table  14  shows an example of one record being comprised of a cluster ID  1411  in which cluster identifiers are stored, a customer ID  1412 , age  1413 , and a length of contract  1414  in months. The cluster ID  1411  corresponds to the clustering results  13 - 2 , the customer ID  1412  and age  1413  correspond to the customer dimension table  120   c , the length of contract  1414  corresponds to the product dimension table  120   a , and the customer dimension table  120   c  and product dimension table  120   a  are associated with the customer identifier  112  and product identifier  111 . The literacy applying module  450  can store in the data warehouse  11  the relations of the dimension tables  120   a  to  120   d  and customer sale history table  110   a  corresponding to respective fields of the relational table  14 . 
         [0100]      FIG. 14  is a flowchart showing one example of a process performed by the data management device  1  in which the clustering results  13 - 2  are converted to the relational table  14 . 
         [0101]    The data cleansing module  410  performs data cleansing on the database  10  used by the business application  340  of the enterprise system (S 31 ). The data cleansing module  410  ensures consistency in the database  10 , and the data of the database  10  that has been cleansed is stored in the data warehouse  11 . 
         [0102]    Next, the data selection module  420  selects data stored in the data warehouse  11  according to the purpose of the data mining, and generates a data set for analysis  12 . The data set for analysis  12  is extracted from the data warehouse  11  by the data selection module  420  performing inquiries such as association joining and aggregation on the plurality of dimension tables  120   a  to  120   d  and the customer sale history table  110   a  (fact table  110 ) including the data for analysis (S 32 ). 
         [0103]    The data mining module  430  performs data mining on the data set for analysis  12  and extracts the model  13  (S 33 ). The model  13  is extracted from the data set for analysis  12  as the clustering results  13 - 2  shown in  FIG. 5  and the decision tree  13 - 1  shown in  FIG. 6 , for example. When visualizing and evaluating the extracted model  13 , the visualization tool determines whether or not the model  13  extracted by evaluation of the model (model evaluation module  440 ) is new literacy. If the model  13  extracted by the data mining module  430  is obtained as new literacy, then the model evaluation module  440  may be omitted. 
         [0104]    The model  13  obtained as new literacy is stored in the data warehouse  11  after the literacy applying module  450  converts the model  13  to the relational table  14  when performing another instance of data mining (S 34 ). 
         [0105]    As described above, in the present embodiment, by storing the obtained model  13  in the data warehouse  11  after converting it to the relational table  14 , it is possible to perform data mining again by another method. 
         [0106]    By converting the obtained model  13  to the relational table  14 , it is possible for the data selection module  420  to perform inquiries on the dimension tables  120   a  to  120   d  and customer sale history table  110   a  (fact table  110 ) generated from the database  10 , and the relational table  14  based on the new literacy. 
         [0107]    By repeating data mining with different parameters, it is possible to generate the model  13  by trial and error, and it is possible to extract and obtain a new model  13  without relying on human experience or hypothesis. By storing the model  13  in the data warehouse  11  as the relational table  14 , it is possible to perform an inquiry thereon and on the star schema  130  as described above. 
         [0108]    Data stored in the data warehouse  11  is not limited to data generated by the business application  340 , but may be a model obtained by performing data mining on the basis of data generated or aggregated in another computer system or a relational table obtained by conversion from this model. 
         [0109]      FIGS. 15 to 19  show an example of the literacy applying module  450  converting a model as new literacy obtained by the data mining module  430  to an SQL model (SQL expression) and the business application  340  using this model as shown in step S 3  in  FIGS. 2 and 3 . Below, an example is described in which the decision tree  13 - 1  for predicting the attributes of new data is converted by the prediction OLAP analysis  330  to an SQL expression on the basis of a data set for analysis (learned data)  12 ′ extracted from the data warehouse  11 . 
         [0110]      FIG. 15  shows an example of the decision tree  13 - 1  being obtained by extracting the decision tree from the data set for analysis  12 ′ extracted by the data selection module  420  from the data warehouse  11  as a data mining process. 
         [0111]      FIG. 16  shows an example of the data set for analysis  12 ′. The data set for analysis  12 ′ is comprised of data differing from the data set for analysis  12  shown in  FIG. 12 . In the example of  FIG. 16 , the data set for analysis  12 ′ comprises one record including the customer ID  1221 , age  1222 , occupation  1223 , income  1224 , movies  1225  in which the like or dislike of movies is stored, and tablet possession  1226  in which possession or lack thereof of a tablet is stored. As for the elements comprising the data set for analysis  12 ′, the user of the data management device  1  selects data from the dimension tables  120   a  to  120   d  and the customer sale history table  110   a  using the input device  6  or the like. In this example, the data set for analysis  12 ′ is generated by the data selection module  420  performing an inquiry on the customer dimension table  120   c , the product dimension table  120   a , and the customer sale history table  110   a . In the data set for analysis  12 ′, the product identifier  121  of the product dimension table  120   a  is searched according to the product identifier  111  corresponding to the customer ID  1221 , and if a tablet is present among the product names, then the tablet possession  1226  is set to “yes,” and if not, the tablet possession  1226  is set to “no.” 
         [0112]    The data mining module  430  extracts the decision tree from the data set for analysis  12 ′, and obtains the decision tree  13 - 1  shown in  FIG. 15 . This decision tree  13 - 1  is applied to the business application  340  and predicts attributes of new data. In the present embodiment, an example is shown in which the possession or lack thereof of a tablet is predicted as the attribute to be predicted. 
         [0113]    The literacy applying module  450  obtains the decision tree  13 - 1  as a model  13  containing new literacy. The literacy applying module  450  converts the decision tree  13 - 1  extracted as the data mining results to the relational table  14 ′. 
         [0114]    The literacy applying module  450  converts the decision tree  13 - 1  to the SQL expression  1310  of the decision tree or the SQL expression  1320  of the decision table shown in  FIG. 15  as the relational table  14 ′. The SQL expression  1320  of the decision table is comprised of one record including the occupation  1321 , movies  1322 , age  1323 , and tablet possession  1324 . 
         [0115]    The literacy applying module  450  generates the SQL expression  1310  of a decision tree or the SQL expression  1320  of a decision table from the decision tree  13 - 1 , and combines this with the business application  340  as shown in  FIGS. 17 and 18 . 
         [0116]      FIG. 17  is a schematic view showing an example of a prediction process performed by the data management device  1 . The data management device  1  receives new data  100  in which the “tablet possession” column is unspecified. The data management device  1  performs the prediction OLAP analysis  330  on the received data  100 , and, referring to the relational table  14 ′ including the SQL expression  1310  of a decision tree or the SQL expression  1320  of a decision table, determines that “tablet possession” is “yes,” and adds this predicted value to the data  100 . Then, the literacy applying module  450  adds data  100 ′ in which the predicted value has been added to the fact table  110  of the star schema  130  as the prediction fact table  110   b.    
         [0117]    In this manner, the SQL expression for predicting new data is generated from the decision tree  13 - 1 , and the prediction value for the new data is added to the fact table  110  of the star schema  130 , thereby allowing this predicted value to be used by the business application  340  or the like. 
         [0118]      FIG. 18  is a descriptive drawing showing another example of a prediction process performed by the data management device  1 .  FIG. 15  shows an example in which the SQL expression  1310  (SQL model) of the decision tree or the SQL expression  1320  of the decision table obtained as new literacy is used by the business application  340 . In this example, the prediction of tablet sales for potential customers is performed using the SQL expression  1310  of the decision tree or the SQL expression  1320  of the decision table obtained as shown in  FIG. 15 . 
         [0119]    In  FIG. 18 , the fact table  110  of the star schema  130  stores actual sales (“actual amount” in drawing) and the estimate during Jun. 1-20, 2013. The business application  340  reads in the fact table  110  of the star schema  130  and displays tablet sales to the output device  7 . 
         [0120]    As shown in  FIG. 18 , the predicted data to be processed is a profile  200  of a potential customer for a tablet. The data management device  1  uses the SQL expression  1310  of the decision tree (or SQL expression  1320  of the decision table) from the profile  200  and predicts possession or lack thereof  210  of a tablet for each customer, and predicts sales value for a tablet to a person who does not own a tablet. 
         [0121]    The prediction OLAP analysis  330  of the data management device  1  reads in the profile  200  and predicts the possession or lack thereof  210  of a tablet for each customer using the SQL expression  1310  of the decision tree. Then, the prediction OLAP analysis  330  calculates the sales prediction for Jun. 21-30, 2013 on the basis of the possession or lack thereof  2010  of a tablet, and adds this to the fact table  110  as the fact table  110   c . The sales predictions for each day are calculated by separating the profile  200  into the respective days or preparing the profile  200  for each day. 
         [0122]    The business application  340  reads in the fact table  110  and the prediction data (prediction  21 - 30  in drawing) fact table  110   c , displays the actual sales of Jun. 1-20, 2013 with a solid line (solid line  1 - 20  in drawing), displays the estimate of Jun. 1-20, 2013 with a broken line, and displays the predicted value for Jun. 21-30, 2013 with a dotted line. 
         [0123]    As described above, by converting the model  13  (decision tree  13 - 1 ) obtained from the data set for analysis  12 ′ in the information system to an SQL expression (SQL model) relational table  14 ′ and using this in the business application  340 , it is possible to provide a method for using new data. 
         [0124]      FIG. 19  is a flowchart showing an example of the prediction process performed by the data management device  1 . 
         [0125]    The data cleansing module  410  performs data cleansing on the database  10  generated by the business application  340  (S 41 ). After data consistency is ensured in the database  10  by the data cleansing module  410 , the data is stored in the data warehouse  11 . 
         [0126]    Next, the data selection module  420  selects data stored in the data warehouse  11 , and generates a data set for analysis  12 ′. The data set for analysis  12 ′ is extracted from the data warehouse  11  by the data selection module  420  performing inquiries such as association joining and aggregation on the plurality of dimension tables  120   a  to  120   d  and the history table  110   a  (fact table  110 ) including the data for analysis (S 42 ). 
         [0127]    The data mining module  430  performs data mining on the data set for analysis  12 ′ and extracts the model  13  (S 43 ). The model  13  is extracted from the data set for analysis  12 ′ as the decision tree  13 - 1  shown in  FIG. 6 , for example. If the model  13  extracted by the data mining module  430  is obtained as new literacy as is, then the model evaluation module  440  may be omitted. 
         [0128]    Next, the data management device  1  converts the model  13  obtained as new literacy to the relational table  14 ′ (S 44 ). At this time, as shown in  FIG. 15 , the literacy applying module  450  converts the model  13  into the relational table  14 ′ comprised of the SQL expression (or predicate expression)  1310  of a decision tree or the SQL expression  1320  enabling prediction. 
         [0129]    Next, when the prediction OLAP analysis  330  receives new data, it uses the SQL expression  1310  of the decision tree or the SQL expression  1320  of the decision table, and generates the predicted results as the new fact table  110   c  (S 45 ). The prediction OLAP ANALYSIS  330  adds the newly generated fact table  110   c  to the customer sale history table  110   a  stored in the data warehouse  11  (S 46 ). 
         [0130]    Next, the literacy applying module  450  combines the SQL expression  1310  of the obtained decision tree or the SQL expression of the decision table with the business application  340  (S 47 ). Then, by executing the business application  340  (S 48 ), it is possible to use the newly added fact table  110   c  together with the existing fact table  110 . 
         [0131]    As described above, the model  13  extracted from the data set for analysis  12  by the data mining module  430  is converted to the relational table  14 ′ comprised of the SQL expression  1310  of the decision tree or the SQL expression  1320  of the decision table  1320  predicting new data. Then, using the data predicted by the SQL expression  1310  of the decision tree or the SQL expression  1320  of the decision table, the new fact table  110   c  is added to the existing fact table  110 . By combining the SQL expression  1310  of the decision tree or the SQL expression  1320  of the decision table with the business application  340 , it is possible to use the existing fact table  110  to which the new fact table  110   c  was added. In other words, by predicting data attributes using the SQL expression  1310  of the decision tree or the SQL expression  1320  of the decision table and providing the predicted results to the business application  340 , it is possible to use the new model  13  without adding modifications to the existing business application  340 . 
         [0132]    As described above, in the present embodiment, literacy obtained by the data mining module  430 , or in other words, the model  13  such as the decision tree  13 - 1  and the clustering results  13 - 2  can be combined with the SQL data model of the business application  340  of the enterprise system. Also, by storing the relational table converted from the obtained model  13  in the data warehouse  11 , it is possible to perform data mining again by another method. In other words, the model  13  comprised of the decision tree  13 - 1  and the clustering results  13 - 2  is converted to an SQL model and expressed as the relational table  14  (or  14 ′), thereby enabling inquiry of the fact table  110  and the dimension tables  120   a  to  120   d  of the data warehouse  11 . 
         [0133]    The inquiry process on the relational table  14 ′ of the obtained model  13  can be executed without modifying the existing business application  340 . Also, by repeatedly performing analysis and evaluation on the same data set for analysis  12  ( 12 ′) while changing categories and types and with differing setting parameters, it is possible to extract a new model  13  by trial and error. In particular, by repeating analysis and evaluation on a large quantity of data with differing setting parameters, it is possible to extract new literacy, or in other words, new models  13  without reliance on human experience or hypothesis, and to apply this information to the business application  340 . 
         [0134]    Also, in the embodiment above, a decision tree and clustering were described as methods for data mining, but another method such as association rule extraction and the like can be used, for example. In the case of association rule extraction, significant rules among a plurality of data items are discovered while focusing on data items appearing simultaneously. These rules can be expressed as “CASE-WHEN-THEN-” in a manner similar to the SQL expression (SQL expression  1310  of the decision tree shown in  FIGS. 15 and 17 ) of the decision tree in the embodiment. In other words, by association rule extraction, it is possible to apply the association rule SQL expression (CASE˜WHEN˜THEN˜) to the relational table  14  (relational table  14  shown in  FIGS. 3 and 4 ). In this manner, it is possible to recommend products to be bought simultaneously on the basis of the association rule extraction in a manner similar to the product recommendation using the decision tree shown in  FIG. 6 . Furthermore, by applying the SQL expression (CASE˜WHEN˜THEN˜) to the relational table  14  using another statistical analysis method such as regression analysis or discriminant analysis, this method can similarly be used. 
         [0135]    Also, in the embodiment above, an example was shown in which the business application  340  managing the database  10 , the data warehouse  11 , and the literacy extraction system  30  are all provided on the same computer, but these may be provided in separate computers. For example, a configuration may be adopted in which the business application  340  and the database  10  are provided on a business server and the data warehouse  11  and the literacy extraction system  30  are provided on an analysis server. 
         [0136]    Also, in the present embodiment, an example was shown in which the data management device is comprised of a calculator including an auxiliary storage device  4 , but a configuration may be adopted in which the data management device  1  and the auxiliary storage device are connected through a network. 
         [0137]    The computers, processing units, and processing means described related to this invention may be, for a part or all of them, implemented by dedicated hardware. 
         [0138]    The variety of software exemplified in the embodiments can be stored in various media (for example, non-transitory storage media), such as electro-magnetic media, electronic media, and optical media and can be downloaded to a computer through communication network such as the Internet. 
         [0139]    This invention is not limited to the foregoing embodiments but includes various modifications. For example, the foregoing embodiments have been provided to explain this invention to be easily understood; they are not limited to the configurations including all the described elements.