JP2009129276A - Category naming apparatus, category naming method and category naming program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To improve statistical accuracy in category naming by efficiently classifying a plurality of records into a category having the same statistical feature and giving a clear category name to the classified category in classifying a plurality of records stored in a multivalued database into each category, in the multivalued database for storing multivalued attribute values including one or more identifiers for uniquely identifying a plurality of records. <P>SOLUTION: A category naming apparatus extracts records having the same identifier value from a plurality of records stored in the multivalued database as the same unit data, acquires a plurality of attribute values corresponding to a plurality of identifiers stored in the unit data from the multivalued database, acquires from the multivalued database a plurality of identifiers corresponding to each category of which the category name is given from the plurality of acquired attribute values, and generates a category group by associating the plurality of acquired identifiers with the category name. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a multi-value database that holds multi-value attribute values including one or more identifiers that uniquely identify a plurality of records, and a category for classifying the plurality of records stored in the multi-value database into categories. It relates to a naming device.

  Conventionally, in order to effectively use data stored in a database, the data is classified. When the data stored in the database is classified, if a classification system is given, the data is classified manually or by an automatic classification technique using a computer according to the definition of the classification system. For example, Patent Document 1 discloses a technique of automatically classifying by a document classification device based on a classification tree that is a category determined and created by a user.

  However, when no data classification system is given, if the database is a large-scale database, the construction of a manual classification system becomes complicated, and the burden is large. To that end, it is necessary to automatically create a classification system. In addition, it is important to give an appropriate name to an automatically generated category in order to use data after classification. Therefore, recently, there is a technique for automatically generating categories based on the characteristics of each record data in a database and classifying each record into a corresponding category.

  Here, a method for automatically generating a category based on the characteristics of each record data of a database according to the prior art will be described with reference to FIG. FIG. 14 is a diagram for explaining a method of automatically generating categories based on the characteristics of each record data in a database according to the conventional technique.

  As shown in FIG. 14, the category naming apparatus determines a specific attribute in a database in which the number of attribute values of one unit of data (instance) is one, and obtains a corresponding attribute value in each record data. Then, the category naming device classifies data having the same attribute value as the same category, and generates the category group by naming the attribute value as the category name.

  For example, the category naming device is used to classify employees' databases from the viewpoint of “job”, and employees with the same attribute values such as “development”, “sales”, and “purchase” from the “job” field. Employees are classified as the same category (employees with “No. 9023” and “No. 9025” whose “duties” are “development” are classified as the same category), and the category is automatically named using the attribute value as the category name And automatic generation of categories.

Japanese Patent No. 3441500

  However, the above-described conventional technique holds multiple attribute values including one or more identifiers that uniquely identify a plurality of records, such as accounting slips created in accordance with a double-entry bookkeeping framework. In the case of the value database, there is a problem that the statistical accuracy of the category to be classified is lowered. For example, the category naming apparatus according to the prior art has a double-entry bookkeeping framework in which a plurality of records correspond to one slip and the plurality of records are set as one unit or a plurality of values are stored in one field. We did not consider the case of data such as accounting slips made along the way. As a result, the category naming apparatus according to the prior art has a plurality of classification destination categories for one element in the case of data such as accounting slips created in accordance with the double-entry bookkeeping framework. Since the characteristics of other categories are mixed with the elements belonging to, statistical accuracy is lowered.

  Therefore, the present invention has been made to solve the above-described problems of the prior art, and efficiently classifies statistical characteristics into the same category, and names clear category names for the classified categories. It is an object of the present invention to provide a category naming apparatus capable of increasing statistical accuracy.

  In order to solve the above-described problems and achieve the object, the present invention provides a multi-value database that holds multi-value attribute values including one or more identifiers that uniquely identify a plurality of records. A category naming program for causing a computer to execute a method for classifying a plurality of records stored in a category, reading a plurality of records stored in the multi-value database, and identifying the same identifier value An extraction procedure for extracting as unit data, and a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure are acquired from the multi-value database, and from the acquired plurality of attribute values A naming procedure for naming a category name and a plurality of categories corresponding to the category for each category named by the naming procedure. It acquires the identifier from the multilevel database, characterized in that to execute a category generation procedure for generating the category data group in association with a plurality of identifiers and category names acquired, to the computer.

  Further, the present invention is the above invention, wherein the naming procedure acquires a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure from the multi-value database, In the plurality of acquired attribute values, when the same attribute value appears a plurality of times, the plurality of attribute values are collectively named as one category name.

  Further, the present invention is the above invention, wherein the naming procedure acquires a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure from the multi-value database, In the plurality of acquired non-numeric attributes, when the plurality of non-numeric attributes mean the same category, the same category is unified and named as one category name.

  Further, according to the present invention, in the above-described invention, in the multi-value database holding a multi-value attribute value including one or a plurality of identifiers for uniquely identifying a plurality of records, a plurality of data stored in the multi-value database are stored. A category naming method of a category naming apparatus for classifying records into categories, wherein a plurality of records stored in the multi-value database are read, and the same identifier value is extracted as the same unit data; and Acquiring a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction step from the multi-value database, and naming a category name from the plurality of acquired attribute values; , For each category named in the naming step, a plurality of identifiers corresponding to the category are retrieved from the multi-value database. Obtained to, characterized in that it includes a category generation step of generating a category data group in association with a plurality of identifiers and category names acquired, the.

  Further, according to the present invention, in the above-described invention, in the multi-value database holding a multi-value attribute value including one or a plurality of identifiers for uniquely identifying a plurality of records, a plurality of data stored in the multi-value database are stored. A category naming apparatus for classifying records into categories, wherein a plurality of records stored in the multi-value database are read, and extraction means for extracting the same identifier values as the same unit data; and the extraction means Naming means for acquiring a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the multi-value database and naming a category name from the plurality of acquired attribute values; and the naming means For each category named according to the above, a plurality of identifiers corresponding to the category are acquired from the multi-value database and acquired. And category generating means for generating a category data group in association with the number of identifiers and category name, characterized by comprising a.

  According to the present invention, in a multi-value database, there is only one category to which one data directly belongs, and a category as a minimum unit that cannot further divide properties can be generated. Can be classified into the same category, and a clear category name can be given to the classified category to increase the statistical accuracy.

  Further, according to the present invention, when a plurality of the same attribute values appear, the plurality of attribute values can be collectively named as one category name, so that it is possible to eliminate category redundancy. At the same time, the same unit data can belong to the same category more.

  Further, according to the present invention, when a plurality of non-numeric attributes are synonyms, the synonyms can be unified to form one category name, and as a result, statistical accuracy can be further increased. is there.

  Further, according to the present invention, a category name can be obtained using a numerical range, and as a result, it can be used as a clear and meaningful category.

  Further, according to the present invention, it is possible to make a category name using a plurality of attributes of one record, and as a result, it is possible to further increase the statistical accuracy.

  Further, according to the present invention, a cluster can be generated using the category data group, and as a result, a clearer cluster with high statistical similarity accuracy can be generated.

  Exemplary embodiments of a category naming apparatus according to the present invention will be described below in detail with reference to the accompanying drawings. In the following, the outline and features of the category naming apparatus according to the present invention, the configuration of the category naming apparatus and the flow of processing will be described in order, and finally the effects of the present embodiment will be described.

[Overview and Features]
First, the outline and characteristics of the category naming apparatus according to the first embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an overview and characteristics of the category naming apparatus according to the first embodiment.

  This category naming device is a multi-value database in which one or a plurality of records is a data unit, such as RDB (relational database), or ODB (object database). Maintains a multi-value database that stores one or more values in the field value of one record, and categorizes these multi-value database records by category for data with multi-value attribute values. . Here, as shown in FIG. 1, description will be made using accounting data which is RDB.

  In such a configuration, the category naming device includes a plurality of multi-value databases stored in the multi-value database in a multi-value database that holds multi-value attribute values including one or more identifiers that uniquely identify a plurality of records. This is an overview of classifying records into categories. In particular, the statistical characteristics are efficiently classified into the same category, and a clear category name is assigned to the classified category to improve statistical accuracy. The main feature is that it can be raised.

  This main feature will be described in detail. The category naming apparatus reads a plurality of records stored in the multi-value database and extracts the same identifier value as the same unit data ((1) in FIG. 1). reference).

  Specifically, the category naming apparatus reads accounting data that is a multi-value database one record at a time, and extracts the same identifier value contained in the record as the same unit data. That is, when records having the same identifier value continue, the plurality of records are records related to the same unit data. Further, in the case of records having different identifier values, the different records are breaks of unit data, and a record relating to new unit data starts from this different record. Based on such rules, the category naming apparatus extracts unit data from all the records of the multi-value database.

  For example, when the identifier for uniquely identifying the record in the accounting data is “date” and “slip number”, the category naming apparatus reads 1 record at a time, and the identifier “date: 2007070105, slip number: 108327” is 1 The record unit and the second record are the same unit data. Similarly, the category naming apparatus sets the third record, the fourth record, and the fifth record having the identifier “date: 20070105, slip number: 108328” as the same unit data. Based on such rules, the category naming apparatus extracts unit data from all records of accounting data. One or more identifiers are designated in advance by the user.

  Then, the category naming apparatus acquires a plurality of attribute values corresponding to a plurality of identifiers held in the extracted unit data from the multi-value database, and names the category name from the acquired plurality of attribute values (FIG. 1). (See (2)). Specifically, the category naming apparatus acquires a plurality of attribute values stored in the multi-value database from a plurality of identifiers held by the extracted unit data. After that, the category naming apparatus uses the values of a plurality of category generation attributes, which are the attributes for generating and naming the acquired category, and connects the plurality of category generation attribute values with an appropriate connection symbol to obtain a category name. Naming. That is, since a plurality of records correspond to the unit data, there are a plurality of attribute values of the plurality of records. Therefore, the category naming apparatus names the category name using the category generation attribute value which is the plurality of attribute values.

  More specifically, in the above example, the category naming apparatus uses a plurality of identifiers “date: 2007070105, slip number: 108327” held by the extracted unit data to store a plurality of items stored in the multi-value database. Attribute values “ordinary deposit” and “accrual” are acquired. Thereafter, the category naming apparatus names the category name by connecting the acquired “ordinary deposit” and “accrual” as “ordinary deposit_accrual” by a connection symbol. Similarly, the category naming apparatus uses a plurality of identifiers “date: 2007070105, slip number 108328” held by the extracted unit data, and uses a plurality of attribute values “payable” and “book” stored in the multi-value database. “Branch Account” and “Accounts Receivable” are acquired, and the acquired “Accounts Payable”, “Main Branch Account”, and “Accounts Receivable” are connected as “Accounts Payable_Main Branch Account_Accounts Receivable” with a connection symbol. Is named. In this way, the category naming apparatus names category names for all identifiers held by the extracted unit data.

  Subsequently, for each named category, the category naming apparatus acquires a plurality of identifiers corresponding to the category from the multi-value database, and generates a category data group by associating the acquired plurality of identifiers with the category name. (Refer to (3) in FIG. 1).

  More specifically, in the above example, the category naming apparatus has a plurality of identifiers “date: 2007070105, slip number: 108327”, “date: 2007070106, slip” corresponding to the named category name “ordinary deposit_accrual”. Number: 108330 ”is acquired from the multi-value database. Subsequently, the category naming apparatus associates the identifier “2007070105-108327, 2000070106-108330” connecting the acquired date and the slip number with “ordinary deposit_accrual” named as the new category name, and Generate a category data set. Similarly, the category naming apparatus acquires a plurality of identifiers “date: 2007070105, slip number: 108328” corresponding to the named category name “accrual_main branch account_accrual” from the multi-value database, The data group of the category is generated by associating the identifier “2007070105-108328” linked with the slip number with “accrual account_main branch account_accrual account” named as the new category name.

  For this reason, the category naming apparatus according to the first embodiment uses the same identifiers included in a plurality of attributes when performing category classification using a multi-value database that holds multi-value attribute values. As a result of being able to combine and attribute a plurality of attribute values as a category name, statistical features are efficiently classified into the same category as the main feature described above, and the category is clearly defined. It is possible to increase the statistical accuracy by naming category names.

[Configuration of Category Naming Device According to Embodiment 1]
Next, the configuration of the category naming apparatus according to the first embodiment will be described with reference to FIG. FIG. 2 is a configuration diagram illustrating the configuration of the category naming apparatus according to the first embodiment. As shown in FIG. 2, the category naming apparatus 1 includes an input unit 2, an output unit 3, an I / F unit 4, a storage unit 5, and a control unit 6. The multi-value database having one or more records held by the category naming apparatus 1 as a data unit will be described by taking accounting data that is RDB as an example.

  The input unit 2 includes a keyboard, a mouse, and the like, and receives input of various types of information. For example, the input unit 2 accepts an input instruction when a user of the category naming apparatus 1 gives an instruction for naming or creating a category.

  The output unit 3 includes a monitor (or display, touch panel), a speaker, and the like, and outputs various types of information. For example, the output unit 3 displays and outputs an input instruction such as category naming or generation received by the input unit 2.

  The I / F unit 4 is communicably connected via a network, and controls communication related to various types of information acquired via the network.

  The storage unit 5 stores data necessary for various types of processing by the control unit 6 and various types of processing results by the control unit 6, and particularly those closely related to the present invention include a multi-value database 5a and a unit data table 5b. And a category name table 5c and a category-unit data table 5d.

  The multi-value database 5a stores multi-value attribute values including one or more identifiers that uniquely identify a plurality of records. Specifically, the multi-value database 5 a stores one or more multi-value attribute values that uniquely identify a plurality of records specified in advance by the user of the category naming device 1. . For example, as illustrated in FIG. 3, the multi-value database 5 a includes “date” and “slip number” that are identifiers designated in advance by the user of the category naming device 1, “detail” that is a multi-value attribute value, ““ 20077055 ”,“ 108327 ”,“ 1 ”,“ borrow ”,“ ordinary deposit ”” are stored as “lending” and “account”. FIG. 3 is a diagram illustrating an example of the multi-value database 5a according to the first embodiment.

  The unit data table 5b stores, as unit data, records having the same plurality of identifier values. Specifically, the unit data table 5b stores unit data obtained by the unit data extraction unit 6b, which will be described later, as a record having a plurality of identifier values in the multi-value database 5a. This is information indicating which record in the value database 5a corresponds to. For example, as shown in FIG. 4, the unit data table 5b has a combination of “date” and “slip number” in association with “identifiers“ date ”and“ slip number ”in the multi-value database 5a. As “identifier” ”,“ 2007070105 ”,“ 108327 ”, and“ 2007070105-108327 ”” are stored. Further, the unit data of “date: 2007070105” and “slip number: 108327” is information corresponding to the first record and the second record of the multi-value database 5a (see FIG. 3). FIG. 4 is a diagram illustrating an example of the unit data table 5b according to the first embodiment.

  The category name table 5c stores category names that are named based on the unit data table 5b. Specifically, the category name table 5c includes a plurality of attribute values of the multi-value database 5a (see FIG. 3) based on the identifier of the unit data table 5b (see FIG. 4) generated by the unit data extraction unit 6b described later. The category name named from is stored. For example, as shown in FIG. 5, the category name table 5c includes unit data “date: 20070105” and “slip number” stored in the unit data table 5b (see FIG. 4) generated by the unit data extraction unit 6b described later. : 108327 ”and named from the attribute values of“ account item: ordinary deposit ”of the first record and“ account item: accounts receivable ”of the second record stored in the multi-value database 5a (see FIG. 3). The stored category name “ordinary deposit: account receivable” is stored. Similarly, for example, the category name table 5c includes unit data “date: 20070105” and “slip number: 108328” stored in the unit data table 5b (see FIG. 4) generated by the unit data extraction unit 6b described later. Based on the above, the third record “Account: Accounts Payable”, the fourth record “Account: Main Branch Account” and the fifth record “Account: Accrued” are stored in the multi-value database 5a (see FIG. 3). The category name “accrual: main branch account_accrual” that is named from the attribute value “payment” is stored. FIG. 5 is a diagram illustrating an example of the category name table 5c according to the first embodiment.

  The category-unit data table 5d stores a category name named based on the unit data table 5b and a combination of a plurality of identifiers corresponding to the category name. Specifically, the category-unit data table 5d includes a category name named from a plurality of attribute values in the multi-value database 5a based on an identifier of the unit data table 5b generated by the unit data extraction unit 6b described later. A combination of a plurality of identifiers corresponding to the category name is stored.

  For example, as shown in FIG. 6, the category-unit data table 5d includes unit data “date: 20070105” and “date” stored in the unit data table 5b (see FIG. 4) generated by the unit data extraction unit 6b described later. Based on the slip number: 108327 ”, the attribute values of the“ record item: ordinary deposit ”in the first record and the“ account item: accounts receivable ”in the second record stored in the multi-value database 5a (see FIG. 3). In association with the category name “ordinary deposit: accounts receivable” named from “2”, “207070105-108327” and “2007070106-108330”, which are combinations of identifiers corresponding to the category name “ordinary deposit: accounts receivable”, are stored. is doing.

  Similarly, for example, the category-unit data table 5d includes unit data “date: 20070105” and “slip number: 108328” stored in the unit data table 5b (see FIG. 4) generated by the unit data extraction unit 6b described later. ”In the third record stored in the multi-value database 5a (see FIG. 3),“ Account: Accounts payable ”in the fourth record,“ Account: Main branch account ”in the fourth record, and“ Account item ”in the fifth record. Is a combination of identifiers corresponding to the category name “accrual: main branch account_accrual” in association with the category name “accrual: main branch account_accrual”. “20070105-108328” is stored. FIG. 6 is a diagram illustrating an example of the category-unit data table 5d according to the first embodiment.

  The control unit 6 has an internal memory for storing a control program, a program that defines various processing procedures, and necessary data, and a category generation / name control unit 6a that is particularly closely related to the present invention. , A unit data extraction unit 6b, a category naming unit 6c, and a category generation unit 6d, which perform various processes.

  The category generation / naming control unit 6a instructs a unit data extraction unit 6b, a category naming unit 6c, and a category generation unit 6d, which will be described later, to start processing. Specifically, when the category generation / naming control unit 6a receives a category generation / naming instruction from the user of the category naming apparatus 1, the category generation / naming control unit 6a reads and reads the multi-value database 5a from the unit data extraction unit 6b described later. Instructs to cut out unit data.

  When the category generation / naming control unit 6a receives a notification that the unit data extraction unit 6b described later has cut out the unit data, the category generation / naming control unit 6a names a category name for each unit data to the category naming unit 6c described later. To instruct. Subsequently, when the category generation / naming control unit 6a receives a notification that a new category name has appeared by a category naming unit 6c described later, the category generation / naming control unit 6a notifies the category generation unit 6d described later that a new category name has appeared. To do. After that, when the category generation / naming control unit 6a receives a notification that the category naming unit 6c described later has determined the identifier of the unit data and the category name corresponding to the identifier, the category generation / naming control unit 6a notifies the category generation unit 6d described later. To notify the identifier and the category name corresponding to the identifier.

  The unit data extraction unit 6b reads a plurality of records stored in the multi-value database 5a, and extracts the same identifier value as the same unit data. Specifically, upon receiving a unit data extraction instruction from the category generation / naming control unit 6a, the unit data extraction unit 6b reads a plurality of records stored in the multi-value database 5a. Then, the unit data extraction unit 6b extracts the same identifier value for uniquely identifying the read record as the same unit data, and stores it in the unit data table 5b. That is, when records having the same identifier value continue, the plurality of records are records related to the same unit data. Further, in the case of records having different identifier values, the different records are breaks of unit data, and a record relating to new unit data starts from this different record. Based on such a rule, the unit data extraction unit 6b extracts unit data from all the records in the multi-value database 5a, and stores information on which record in the multi-value database 5a corresponds to each unit data. Store in the data table 5b.

  For example, when the identifier for uniquely identifying a record in the multi-value database 5a (see FIG. 3) is “date” and “slip number”, the unit data extraction unit 6b performs data from the first record of the multi-value database 5a. , And the identifier “date: 2007070105, slip number: 108327” of the first record and the second record having the same identifier is extracted as the same unit data and stored in the unit data table 5b (see FIG. 4). To do. Similarly, the unit data extraction unit 6b reads data from the third record, the fourth record, and the fifth record of the multi-value database 5a (see FIG. 3), and the identifier and the third record have the same value. The identifier “date: 2007070105, slip number: 108328” between the eyes and the fifth record is extracted as the same unit data and stored in the unit data table 5b (see FIG. 4). Based on such rules, the unit data extraction unit 6b extracts unit data from all the records in the multi-value database 5a and stores them in the unit data table 5b. One or more of these identifiers are designated in advance by the user. The unit data extraction unit 6b corresponds to “extraction means” recited in the claims.

  The category naming unit 6c acquires a plurality of attribute values corresponding to a plurality of identifiers held in the extracted unit data from the multi-value database 5a, and names the category name from the acquired plurality of attribute values. Specifically, when the category naming unit 6c receives a category naming instruction from the category generation / naming control unit 6a, the category naming unit 6c uses a plurality of identifiers held by the extracted unit data to store a plurality of values stored in the multi-value database 5a. Get attribute value. Then, the category naming unit 6c uses the values of a plurality of category generation attributes, which are attributes that are the basis for generating and naming the acquired category, and connects the plurality of category generation attribute values with an appropriate connection symbol. Are stored in the category name table 5c. That is, since one unit data of the unit data table 5b corresponds to a plurality of records, there are a plurality of attribute values of the plurality of records (conventionally, only one attribute value exists) A category name is named using a category generation attribute value which is a plurality of attribute values.

  More specifically, in the above example, the category naming unit 6c has a plurality of attribute values “account: ordinary deposit” and “account :: corresponding to the extracted unit data“ date: 2007070105, slip number: 108327 ”. Accrued money "is acquired from the multi-value database 5a (see FIG. 3). Then, the category naming unit 6c names the category name by connecting the acquired attribute values “ordinary deposit” and “accrued money” as “ordinary deposit: accrued money” by a connection symbol, and the category name table 5c (FIG. 5). Similarly, the category naming unit 6c includes a plurality of attribute values “account item: accrued”, “account item: main branch account”, and “account item” corresponding to the extracted unit data “date: 2007070105, slip number: 108328”. : Accounts receivable ”from the multi-value database 5a (see FIG. 3), and the acquired attribute values“ Accounts payable ”,“ Main branch account ”, and“ Accounts receivable ”are connected to each other by a connection symbol. The name of the category is named as “Account_accrual” and stored in the category name table 5c (see FIG. 5).

  Further, the category naming unit 6c acquires a plurality of attribute values corresponding to the extracted unit data from the multi-value database 5a, and in the acquired plurality of attribute values, “ordinary deposit”, “accrual money”, “ When the same attribute value appears multiple times, such as “Accounts receivable”, it is possible to eliminate redundancy by naming the category name as “Normal deposit_Accounts receivable” and the same unit data Can belong to the same category more. Further, in the case of the accounting data, the category naming unit 6c considers the debit and credit, and the “ordinary deposit” on the left side (debit) and the “accrual” on the right side (credit) are set as “normal deposit: Name the category by connecting “accrual”, debit “accrual” and credit “main branch account” and “accrual” as “accrual: main branch account_accrual”. Is possible. The category naming unit 6c corresponds to “naming means” described in the claims.

  For each named category, the category generation unit 6d acquires a plurality of identifiers corresponding to the category from the multi-value database 5a, and generates a category data group by associating the acquired plurality of identifiers with the category name. . Specifically, upon receiving a category generation instruction from the category generation / naming control unit 6a, the category generation unit 6d assigns a plurality of identifiers corresponding to the named category name to the multi-value database for each named category name. Obtain from 5a. Then, the category generation unit 6d associates the acquired plurality of identifiers with the category name, generates a data group of the category named as the new category name, and stores it in the category-unit data table 5d.

  Specifically, in the above example, the category generation unit 6d is named for each “ordinary deposit: unpaid money” that is the category name named by the category naming unit 6c and stored in the category name table 5c. A plurality of identifiers “date: 2007070105, slip number: 108327”, “date: 2007070106, slip number: 108330”, and identifiers “20070105-108327, 2000070106-108330” that connect the identifiers, corresponding to the category names. Obtained from the unit data table 5b. Then, the category generation unit 6d associates the plurality of identifiers “2007070105-108327” and “2007070106-108330” with “ordinary deposit: account receivable” named as the new category name corresponding to the acquired identifier. Then, a data group of the category is generated and stored in the category-unit data table 5d (see FIG. 6).

  Similarly, the category generation unit 6d creates a name for each category name “accrual: main branch account_accrual” that is a category name named by the category naming unit 6c and stored in the category name table 5c. An identifier “20070105-108328” obtained by connecting a plurality of corresponding identifiers “date: 20070105, slip number: 108328” is acquired from the unit data table 5b, and “accrual :: named as a new category name corresponding to the acquired identifier. A plurality of identifiers “2007070105-108328” are associated with “Branch account_accrual” and a data group of the category is generated and stored in the category-unit data table 5d (see FIG. 6). The category generation unit 6d corresponds to “category generation means” described in the claims.

[Processing by Category Generation / Name Control Unit According to Embodiment 1]
Next, a category generation / naming instruction process performed by the category generation / naming control unit 6a according to the first embodiment will be described with reference to FIG. FIG. 7 is a flowchart illustrating a category generation / naming instruction process performed by the category generation / naming control unit 6a according to the first embodiment.

  As shown in FIG. 7, when the category generation / naming control unit 6a receives a category generation / naming instruction from the user of the category naming apparatus 1 (Yes in step S71), the category generation / naming control unit 6a multivalues the unit data extraction unit 6b. An instruction is given to read the database 5a and to cut out unit data (step S72).

  Specifically, when the category generation / naming control unit 6a receives an instruction for category generation / naming from the category generation / naming input in the input unit 2 by the user of the category naming apparatus 1, the unit data extraction unit 6b Are instructed to read out the multi-value database 5a and extract the same identifier value as the same unit data.

  When the category generation / naming control unit 6a receives a notification that the unit data extraction unit 6b has cut out the unit data, it instructs the category naming unit 6c to name the category name for each unit data. (Step S73). Specifically, when the category generation / naming control unit 6a receives a notification that the unit data extraction unit 6b has extracted the unit data, the category generation / naming control unit 6a holds the unit data with respect to the category naming unit 6c. It is instructed to name a category name from a plurality of attribute values stored in the multi-value database 5a from a plurality of identifiers.

  Subsequently, the category generation / naming control unit 6a receives a notification that a new category name has appeared by the category naming unit 6c (step S74), and notifies the category generation unit 6d that a new category name has appeared. (Step S75). As a specific example, if a category name appears when the category name / name control unit 6a is named by the category naming unit 6c, the category generation / name control unit 6a receives a notification that the new category name has appeared. The category generation unit 6d is notified that a new category name has appeared.

  Thereafter, the category generation / naming control unit 6a receives the unit data identifier and the belonging category name by the category naming unit 6c (step S76), and uses the received identifier and belonging category name to the category generating unit 6d. Notification is made (step S77). Specifically, the category generation / naming control unit 6a receives the unit data identifier and the category name corresponding to the unit data identifier from the category naming unit 6c, and sends the category data to the category generation unit 6d. The received identifier and the category name corresponding to the identifier of the unit data are notified.

[Processing by Unit Data Extraction Unit According to Embodiment 1]
Next, unit data extraction processing by the unit data extraction unit 6b according to the first embodiment will be described with reference to FIG. FIG. 8 is a flowchart illustrating unit data extraction processing by the unit data extraction unit 6b according to the first embodiment.

  As shown in FIG. 8, when the unit data extraction unit 6b receives a unit data extraction instruction from the category generation / naming control unit 6a, the unit data extraction unit 6b reads the data of the multi-value database 5a, and when the read data is not completed ( In step S81, the next record is read from the multi-value database 5a (step S82). For example, when the unit data extraction unit 6b receives a unit data extraction instruction from the category generation / naming control unit 6a, the unit data extraction unit 6b stores the multi-value database 5a from the first record “date: 20070105, slip number: 108327”. It is read and it is determined whether or not the read data has been completed. If there is data to be read, the next record “Date: 2007070105, slip number: 108327” is read from the multi-value database 5a.

  Then, when the identifier of the unit data is not the same as that of the previous record (No at Step S83), the unit data extraction unit 6b stores the identifier in the unit data table 5b as the unit data (Step S84). Step S85). To give a specific example, the unit data extraction unit 6b determines that the identifier “date: 2007070105, slip number: 108328” of the third record read is the identifier “date: 2007070105” of the record immediately before the read record. If the identifier of the read record is not the same as the identifier of the previous record, the previous identifier “Date: 2007070105, slip number” is determined. : 108327 ”as unit data and stored in the unit data table 5b as position information corresponding to the multi-value database 5a. Note that the unit data extraction unit 6b performs the processing from step S81 again when the identifier of the unit data is the same as that of the previous record (Yes at step S83).

  Further, when the data read from the multi-value database 5a is completed (Yes at Step S81), the unit data extraction unit 6b stores the identifier in the unit data table 5b with the remaining data as the unit data (Step S86) (Step S81). S87). As a specific example, when the data read from the multi-value database 5a is completed (data of identifier “date: 2007070131, slip number: 219207”), the unit data extraction unit 6b reads the unit data table read so far. The remaining data not stored in 5b is stored as unit data, and the identifier “date: 20070131, slip number: 219207” read into the unit data table 5b is stored as position information corresponding to the multi-value database 5a, and the process ends. .

[Processing by Category Naming Unit According to Embodiment 1]
Next, the category naming process by the category naming unit 6c according to the first embodiment will be described with reference to FIG. FIG. 9 is a flowchart illustrating the category naming process performed by the category naming unit 6c according to the first embodiment.

  As shown in FIG. 9, upon receiving a category naming instruction from the category generation / naming control unit 6a (Yes at step S91), the category naming unit 6c reads the unit data in the unit data table 5b and reads the next unit data table from the unit data table. Unit data is read (step S92). Specifically, when the category naming unit 6c receives a category naming instruction from the category generation / naming control unit 6a, the unit data “date: 20070105, slip number: 108327 ”is read, and the unit data“ date: 20070105, slip number: 108328 ”of the next record is read from the unit data table 5b.

  Then, the category naming unit 6c examines all the category generation attribute values in the unit data (step S93), and names the category name based on the category generation attribute value (step S94). To give a specific example, the category naming unit 6c is a category that is an attribute that is a basis for generating and naming a category in the unit data “date: 2007070105, slip number: 108327” stored in the unit data table 5b. The generated attribute values “Account: Savings Account” and “Account: Accrued Money” are all read, and the “Generating Account: Accounts Receivable” connected to the Category Generation Attribute Value based on the Category Generation Attribute Value is the category name. Name it as

  Subsequently, when the category name is new (Yes at Step S95), the category naming unit 6c stores the new category name in the category name table 5c (Step S96). For example, the category naming unit 6c compares the category name “ordinary deposit: unpaid money” connected with the category generation attribute value with the category name stored in the category name table 5c, When the category name connected with the generated attribute value is new, the category name connected with the category generated attribute value is stored as a new category name in the category name table 5c. If the category name is not new (No at Step S95), the category naming unit 6c performs the process at Step S98 described later.

  Thereafter, the category naming unit 6c notifies the category generation / naming control unit 6a of the new category name (step S97) and notifies the category generation / naming control unit 6a of the identifier and the belonging category name (step S98). Specifically, the category naming unit 6c notifies the category generation / naming control unit 6a of a new category name “ordinary deposit: unpaid money” connected to the category generation attribute value stored in the category name table 5c. At the same time, a new category name “ordinary deposit: unpaid money” and a plurality of identifiers “date: 2007070105, slip number: 108327, identifier: 2007070105-108327” corresponding to the new category name are generated by the category generation / naming control unit 6a. Notify

  The category naming unit 6c determines whether or not the unit data to be read has ended (step S99). When the unit data to be read ends (Yes at step S99), the category naming process ends and the unit data to be read ends. If not (No at Step S99), the process at Step S92 is performed again.

[Processing by Category Generation Unit According to Embodiment 1]
Next, the category generation processing by the category generation unit 6d according to the first embodiment will be described with reference to FIG. FIG. 10 is a flowchart illustrating the category generation process performed by the category generation unit 6d according to the first embodiment.

  As shown in FIG. 10, upon receiving a category generation instruction from the category generation / naming control unit 6a (Yes in step S101), the category generation unit 6d receives a category name and an identifier from the category generation / naming control unit 6a ( Step S102). Specifically, when the category generation unit 6d receives a category generation instruction from the category generation / naming control unit 6a, the category name “ordinary deposit: unpaid money” named by the category naming unit 6c and the category The identifier “2007070105-108327” corresponding to the name is received from the category generation / naming control unit 6a.

  When the category name received from the category generation / naming control unit 6a is a new category name (Yes at Step S103), the category generation unit 6d creates an entry for a new category name in the category-unit data table 5d ( Step S104). As a specific example, the category generation unit 6d indicates in the category-unit data table 5d whether or not the category name “ordinary deposit: accrued money” received from the category generation / naming control unit 6a is a new category name. If the received category name is a new category name, a new category name entry is created in the category-unit data table 5d.

  Subsequently, the category generation unit 6d registers an identifier at the location of the corresponding category name in the category-unit data table 5d (step S105). To give a specific example, the category generation unit 6d receives a plurality of identifiers “2007070105-108327” and “2007070106-108330” corresponding to the category name “ordinary deposit: unpaid money” received from the category generation / naming control unit 6a. And register. If the category generation unit 6d is not a new category name in step S103, the category generation unit 6d registers only an identifier corresponding to the category name without newly registering the category name.

  Thereafter, the category generation unit 6d determines whether or not all the data has been processed (step S106). When all the data has been processed (Yes in step S106), the category generation processing ends and all the data is processed. If not (No at Step S106), the processing is performed from Step S102.

[Effects of Example 1]
Thus, according to the first embodiment, the category naming apparatus reads a plurality of records stored in the multi-value database, extracts the same identifier value as the same unit data, and extracts the extracted unit. Acquire multiple attribute values corresponding to multiple identifiers held in the data from the multi-value database, name the category name from the acquired multiple attribute values, and correspond to the category for each named category Since it was decided to generate a category data group by associating a plurality of identifiers from the multi-value database and associating the acquired identifiers with category names, the statistical characteristics are efficiently classified into the same category, It is possible to increase the statistical accuracy by naming a clear category name for the classified category.

  For example, the category naming apparatus 1 has a multi-value database 5a that holds a multi-value attribute value “detail, loan, account” including one or more identifiers “date, slip number” that uniquely identify a plurality of records. , When a plurality of records stored in the multi-value database 5a are classified by category, the records of the multi-value database 5a are read, and records having the same identifier “date, slip number” are extracted as the same unit data To do. Then, the category naming apparatus 1 uses the extracted unit data to acquire a plurality of attribute values stored in the multi-value database 5a, and names the category name by connecting the acquired attribute values with a connection symbol. . Subsequently, the category naming apparatus 1 stores a plurality of identifiers in a predetermined storage unit in association with the named category name, and generates a category data group. As a result, the category naming apparatus 1 can efficiently classify into categories having the same statistical characteristics, and can also increase the statistical accuracy by naming clear category names for the classified categories.

  Further, according to the first embodiment, the category naming apparatus 1 acquires a plurality of attribute values corresponding to a plurality of identifiers held in the extracted unit data from the multi-value database 5a, and acquires the plurality of attribute values acquired. In the case where the same attribute value appears multiple times, the plurality of attribute values are collectively named as one category name, so that redundancy can be eliminated and the same unit data More can belong to the same category.

  For example, the category naming apparatus 1 acquires a plurality of attribute values stored in the multi-value database 5a using the extracted unit data, and in the acquired plurality of attribute values, “ordinary deposit” and “accrual money” ”And“ accrual ”, when the same attribute value appears multiple times, it is possible to eliminate redundancy by naming the category name as“ regular deposit_accrual ”. More unit data can belong to the same category.

  In the first embodiment, the case where the statistical characteristics are efficiently classified into the same category and a clear category name is given to the classified category to increase the statistical accuracy has been described. The invention is not limited to this, and category classification and category name naming, and further, a cluster using the classified categories can be generated.

  Therefore, in the second embodiment below, cluster generation processing by the category naming apparatus 1 will be described with reference to FIG. FIG. 11 is a diagram for explaining cluster generation processing by the category naming apparatus 1 according to the second embodiment. In the second embodiment, the configuration and functions of the category naming apparatus 1 are the same as those in the first embodiment, so that the description thereof will be omitted and the cluster generation process will be described.

  As shown in FIG. 11, the category naming apparatus 1 specifies unit data and reads the contents (see (1) in FIG. 11). To give a specific example, the category naming apparatus 1 refers to the category-unit data table 5d and identifies individual unit data belonging to each category. Then, the category naming apparatus 1 refers to the unit data table 5b, finds the data storage location (data from the start record number to the end record number) in the multi-value database 5a of the unit data to be processed, and stores the contents. Read.

  Then, the category naming apparatus 1 extracts and stores feature words included in the unit data (see (2) in FIG. 11). As a specific example, the category naming apparatus 1 takes out feature words included in unit data from the read content data using a feature word extraction technique. Thereafter, the category naming apparatus 1 obtains all the feature words extracted from the unit data and their appearance frequencies, and stores them in a predetermined storage unit. In this way, the category naming apparatus 1 stores the feature words and the appearance frequencies of all the unit data in the category in a predetermined storage unit, and repeats the above procedure for all the categories, thereby repeating the multi-value database 5a. Feature words and appearance frequencies are extracted from all unit data expressed in (1). Here, the difference from the prior art of the cluster generation process by the category naming apparatus 1 according to the second embodiment is that the unit data generated in the first embodiment is used as the data that is the basis of the cluster generation. Since the feature word extraction technique is a known technique, a description thereof is omitted here.

  Subsequently, the category naming apparatus 1 generates and stores a feature vector from all the feature words and feature amounts (see (3) in FIG. 11). Specifically, the category naming apparatus 1 refers to feature words and appearance frequency data stored in a predetermined storage unit and refers to feature words and feature amounts of all unit data belonging to a specific category. Based on (appearance frequency), feature vectors serving as their centroids (average) are generated and stored in a predetermined storage unit. Then, the category naming apparatus 1 stores the feature vector data of all categories in a predetermined storage unit by repeatedly performing the above-described processing for all categories.

  Thereafter, the category naming apparatus 1 measures and stores the distance between any two cluster feature vectors (see (4) in FIG. 11). As a specific example, the category naming apparatus 1 refers to the stored cluster feature vector data, measures the distance between any two cluster feature vectors, and uses the distance as the similarity between clusters. Store in a predetermined storage unit.

  Then, the category naming apparatus 1 generates and controls a cluster (see (5) in FIG. 11). As a specific example, the category naming apparatus 1 refers to the similarity data between the stored clusters and refers to the cluster having the maximum similarity (the distance is minimum) among the existing upper clusters. A pair is obtained and generated as a new upper cluster, and the relationship between the upper cluster and the two clusters combined is stored in a predetermined storage unit. Thereafter, the category naming apparatus 1 generates centroid vectors of two clusters that are grouped as clusters as feature vectors of new upper clusters, and stores them in a predetermined storage unit. Subsequently, the category naming apparatus 1 continues the above-described processing ((3) to (5) in FIG. 11) until the number of upper clusters existing reaches a predetermined number, and the number of upper clusters reaches the predetermined number. The process is terminated at that point.

[Effects of Example 2]
In this manner, according to the second embodiment, the category naming apparatus performs feature word extraction using the generated category data group, obtains statistical similarity, and generates a cluster of the category data group. Therefore, it is possible to generate a cluster that is clearer and has high statistical similarity accuracy.

  For example, the category naming apparatus 1 uses the category data group generated in the first embodiment to identify unit data, read the contents, and extract feature words included in the unit data. The category naming apparatus 1 generates feature vectors from all feature words and feature quantities, measures the distance between any two cluster feature vectors, and generates and controls clusters. In addition, it is possible to generate a cluster with high statistical similarity accuracy.

  Although the embodiments of the present invention have been described so far, the present invention may be implemented in various different forms other than the embodiments described above. Therefore, different embodiments in (1) type of multi-value database, (2) connection symbol used for category naming, (3) category naming, (4) configuration of category naming apparatus, and (5) program will be described.

(1) Types of multi-value database In the first or second embodiment, in the multi-value database 5a, a multi-value database having one or more records such as RDB (relational database) as a data unit is used. Although described as being used, the present invention is not limited to this. For example, one or more values are included in the field value of one record such as an ODB (object database) shown in FIG. Alternatively, a multi-value database storing the above may be used. According to FIG. 12, the accounting data stored in the ODB can be converted into RDB expressing the same meaning. FIG. 12 is a diagram for explaining the equivalence of the structures of the RDB and the ODB according to the third embodiment.

(2) Connection symbol used for category naming In Embodiment 1, the connection symbol used for category naming is described as naming a category name using “_”, “:”, etc., but the present invention is not limited to this. It is not limited and can be changed according to the multi-value database to be used. For example, the category name can be named using “;”, “/”, and the like.

(3) Category Naming In the first embodiment, the category naming unit 6c acquires a plurality of attribute values corresponding to a plurality of identifiers held in the unit data extracted by the unit data extracting unit 6b from the multi-value database 5a. In the above description, the category name is named from the plurality of acquired attribute values. However, the present invention is not limited to this. For example, the category naming unit 6c has the same attribute value in the non-numeric attribute. In the case of synonyms such as “computer”, “computer”, and “Computer” meaning the category, the category may be unified and the category name may be named “computer”.

  In the first embodiment, the category naming unit 6c acquires a plurality of attribute values corresponding to a plurality of identifiers held in the unit data extracted by the unit data extracting unit 6b from the multi-value database 5a. Although described as naming a category name from a plurality of attribute values, the present invention is not limited to this. For example, the category naming unit 6c uses attribute values such as numerical values “20” and “75”. When naming a category name, a value range (range, number of digits, etc.) that is a set of the numerical values may be used to name “10 to the power of 10 to the square of 10”.

  In the first embodiment, the category naming unit 6c acquires a plurality of attribute values corresponding to a plurality of identifiers held in the unit data extracted by the unit data extracting unit 6b from the multi-value database 5a. Although it has been described that the category name is named from a plurality of attribute values, the present invention is not limited to this. For example, the category naming unit 6c includes “account item” and “department name” in one record. The category name may be combined and named as “account item + department name”.

(4) Configuration of Category Naming Device Also, the processing procedure, control procedure, specific name, information including various data and parameters shown in the above-mentioned documents and drawings (for example, “multi-value database” shown in FIG. The “unit data table” and the like can be arbitrarily changed unless otherwise specified.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure. For example, the category name table 5c and the category-unit data table 5d correspond to the category name including the new category name and the category name. All or a part of it, such as integrating as a category name-unit data table that stores multiple identifiers, is distributed or integrated functionally or physically in arbitrary units according to various burdens or usage conditions. Can be configured. Furthermore, all or a part of each processing function performed in each device may be realized by a CPU and a program that is analyzed and executed by the CPU, or may be realized as hardware by wired logic.

(5) Program In the above embodiment, the case where various processes are realized by hardware logic has been described. However, the present invention is not limited to this, and a program prepared in advance is executed by a computer. It may be realized by this. In the following, an example of a computer that executes a category naming program having the same function as the category naming apparatus shown in the above embodiment will be described with reference to FIG. FIG. 13 is a diagram illustrating a computer that executes a category naming program.

  As shown in FIG. 13, a computer 110 as a category naming apparatus is configured by connecting a keyboard 120, an HDD 130, a CPU 140, a ROM 150, a RAM 160, and a display 170 through a bus 180 or the like.

  The ROM 150 includes a category naming program that exhibits the same function as the category naming apparatus 1 shown in the first embodiment, that is, a category generation / naming control program 150a and a unit data extraction program 150b as shown in FIG. The category naming program 150c and the category generation program 150d are stored in advance. Note that these programs 150a to 150d may be appropriately integrated or distributed in the same manner as each component of the category naming apparatus 1 shown in FIG.

  Then, the CPU 140 reads these programs 150a to 150d from the ROM 150 and executes them, so that as shown in FIG. 13, the programs 150a to 150d have a category generation / name control process 140a, a unit data extraction process 140b, , Function as a category naming process 140c and a category generation process 140d. The processes 140a to 140d correspond to the category generation / naming control unit 6a, the unit data extraction unit 6b, the category naming unit 6c, and the category generation unit 6d shown in FIG.

  Then, the CPU 140 has the same identifier value as the multi-value data 160a stored in the RAM 160 that holds multi-value attribute values including one or more identifiers that uniquely identify a plurality of records. Unit data 160b holding records as unit data, category name data 160c holding category names named based on the unit data table 5b, and category names named based on the unit data table 5b Then, the category naming program is executed based on the category-unit data 160d holding the combination of identifiers corresponding to the category name.

  The above-described programs 150a to 150d are not necessarily stored in the ROM 150 from the beginning. For example, a flexible disk (FD), a CD-ROM, a DVD disk, and a magneto-optical disk inserted into the computer 110. Connected to the computer 110 via a "portable physical medium" such as an IC card, or a "fixed physical medium" such as an HDD provided inside or outside the computer 110, and further via a public line, the Internet, a LAN, a WAN, etc. Each program may be stored in “another computer (or server)” or the like, and the computer 110 may read and execute each program from now on.

(Supplementary note 1) Method of classifying a plurality of records stored in the multi-valued database into categories in a multi-valued database holding multi-valued attribute values including one or more identifiers for uniquely identifying a plurality of records Is a category naming program that causes a computer to execute
An extraction procedure for reading a plurality of records stored in the multi-value database and extracting the same identifier value as the same unit data;
A naming procedure for acquiring a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure from the multi-value database, and naming a category name from the acquired plurality of attribute values;
For each category named by the naming procedure, a plurality of identifiers corresponding to the category are acquired from the multi-value database, and a category data group is generated by associating the acquired plurality of identifiers with category names. Procedure and
A category naming program for causing a computer to execute.

(Supplementary Note 2) The naming procedure includes obtaining a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure from the multi-value database, and obtaining the plurality of attribute values The category naming program according to appendix 1, wherein when the same attribute value appears a plurality of times, the plurality of attribute values are collectively named as one category name.

(Supplementary Note 3) The naming procedure includes acquiring a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure from the multi-value database, and acquiring the plurality of non-numeric attributes acquired The category naming program according to appendix 1 or 2, wherein when the plurality of non-numeric attributes mean the same category, the same category is unified and named as one category name.

(Supplementary Note 4) The naming procedure includes acquiring a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure from the multi-value database, and obtaining the plurality of attribute values The category naming program according to appendix 1, wherein when a numerical value appears, a range of the numerical attribute value is named as a category name.

(Supplementary Note 5) The naming procedure acquires a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure from the multi-value database, The category naming program according to any one of appendices 1 to 3, wherein the category name is named in combination.

(Additional remark 6) It further provided the cluster production | generation procedure which performs a feature word extraction using the category data group produced | generated by the said category production | generation procedure, calculates | requires statistical similarity, and produces | generates the cluster of the said category data group. The category naming program according to any one of appendices 1 to 5, characterized by:

(Additional remark 7) The category which classifies the some record memorize | stored in the said multi-value database according to the category in the multi-value database holding the multi-value attribute value containing the one or several identifier which identifies a some record uniquely A category naming method for a naming device,
An extraction step of reading a plurality of records stored in the multi-value database and extracting the same identifier value as the same unit data;
Obtaining a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction step from the multi-value database, and naming a category name from the plurality of obtained attribute values;
For each category named in the naming step, a plurality of identifiers corresponding to the category are acquired from the multi-value database, and a category data group is generated by associating the acquired plurality of identifiers with category names. Process,
Category naming method characterized by including.

(Additional remark 8) The category which classifies the some record memorize | stored in the said multi-value database according to the category in the multi-value database holding the multi-value attribute value containing one or several identifiers which identify a some record uniquely A naming device,
An extraction means for reading a plurality of records stored in the multi-value database, and extracting the same identifier value as the same unit data;
Naming means for obtaining a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extracting means from the multi-value database, and naming a category name from the plurality of obtained attribute values;
For each category named by the naming means, a plurality of identifiers corresponding to the category are acquired from the multi-value database, and a category data group is generated by associating the acquired plurality of identifiers with category names. Means,
A category naming device characterized by comprising:

  As described above, the category naming apparatus according to the present invention is stored in the multi-value database in a multi-value database holding multi-value attribute values including one or more identifiers that uniquely identify a plurality of records. This is useful when you want to classify multiple records into categories, and in particular, efficiently classify the records into the same category with the same statistical characteristics, and assign a clear category name to the classified categories to improve statistical accuracy. Suitable for raising.

It is a figure which shows the outline | summary and characteristic of the category naming apparatus based on Example 1. FIG. 1 is a configuration diagram illustrating a configuration of a category naming apparatus according to Embodiment 1. FIG. It is a figure which shows an example of the multi-value database which concerns on Example 1. FIG. It is a figure which shows an example of the unit data table which concerns on Example 1. FIG. It is a figure which shows an example of the category name table which concerns on Example 1. FIG. It is a figure which shows an example of the category-unit data table which concerns on Example 1. FIG. 7 is a flowchart illustrating a category generation / naming instruction process by a category generation / naming control unit according to the first embodiment. 6 is a flowchart illustrating unit data extraction processing by a unit data extraction unit according to the first embodiment. 7 is a flowchart illustrating a category naming process by a category naming unit according to the first embodiment. 6 is a flowchart illustrating a category generation process performed by a category generation unit according to the first embodiment. It is a figure for demonstrating the cluster production | generation process by the category naming apparatus concerning Example 2. FIG. It is a figure for demonstrating the equivalence of the structure of RDB and ODB which concerns on Example 3. FIG. It is a figure which shows the computer which performs a category naming program. It is a figure for demonstrating the method of producing | generating a category automatically based on the characteristic of each record data of the database which concerns on a prior art.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Category naming device 2 Input part 3 Output part 4 I / F part 5 Memory | storage part 5a Multi-value database 5b Unit data table 5c Category name table 5d Category-unit data table 6 Control part 6a Category production | generation / name control part 6b Unit data extraction Part 6c Category naming part 6d Category generation part

Claims (5)

In a multi-value database that holds multi-value attribute values including one or more identifiers that uniquely identify a plurality of records, a computer executes a method of classifying a plurality of records stored in the multi-value database into categories. A category naming program that allows
An extraction procedure for reading a plurality of records stored in the multi-value database and extracting the same identifier value as the same unit data;
A naming procedure for acquiring a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure from the multi-value database, and naming a category name from the acquired plurality of attribute values;
For each category named by the naming procedure, a plurality of identifiers corresponding to the category are acquired from the multi-value database, and a category data group is generated by associating the acquired plurality of identifiers with category names. Procedure and
A category naming program for causing a computer to execute.   The naming procedure acquires a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure from the multi-value database, and the same attribute value in the plurality of attribute values acquired 2. The category naming program according to claim 1, wherein when a plurality of times appears, the plurality of attribute values are collectively named as one category name.   The naming procedure includes obtaining a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction procedure from the multi-value database, and obtaining the plurality of non-numeric attributes in the plurality of non-numeric attributes. 3. The category naming program according to claim 1, wherein when the non-numeric attribute means the same category, the same category is unified and named as one category name. The category of a category naming apparatus that classifies a plurality of records stored in the multi-value database by category in a multi-value database that holds multi-value attribute values including one or more identifiers that uniquely identify a plurality of records A naming method,
An extraction step of reading a plurality of records stored in the multi-value database and extracting the same identifier value as the same unit data;
Obtaining a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extraction step from the multi-value database, and naming a category name from the plurality of obtained attribute values;
For each category named in the naming step, a plurality of identifiers corresponding to the category are acquired from the multi-value database, and a category data group is generated by associating the acquired plurality of identifiers with category names. Process,
Category naming method characterized by including. A category naming apparatus that classifies a plurality of records stored in the multi-valued database by category in a multi-valued database that holds multivalued attribute values including one or more identifiers that uniquely identify a plurality of records. And
An extraction means for reading a plurality of records stored in the multi-value database, and extracting the same identifier value as the same unit data;
Naming means for obtaining a plurality of attribute values corresponding to the plurality of identifiers held in the unit data extracted by the extracting means from the multi-value database, and naming a category name from the plurality of obtained attribute values;
For each category named by the naming means, a plurality of identifiers corresponding to the category are acquired from the multi-value database, and a category data group is generated by associating the acquired plurality of identifiers with category names. Means,
A category naming device characterized by comprising:

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