Hitachi, Ltd.

Hitoshi Ashida

Akira Maeda

Yoji Taniguchi

Yukiyasu Ito

Yori Takahashi

Norifumi Nishikawa

Masashi Tsuchida

Toyohisa Morita

Mitsuhiko Yoshimura

Chiaki Hirai

Tadashi Tenma

Hideyuki Maki

Erika Ayukawa

Toshihide Ichimori

Naoko Taniguchi

In an MD schema definition modifying process for specifying tables to be modified in terms of addition of columns or the like when modification such as addition of a dimension is applied to an MD schema in a BI system having a fixed model such as a data model, tables to be modified in terms of addition of columns or the like are specified on the basis of information of modification such as addition of the dimension to the MD schema. In an extraction process of modification candidate tables, all combinations of tables and columns to be modified in terms of addition of columns or the like are output. In a decision process of modification target tables, a combination of tables to be modified can be output.

Method and system for detecting tables to be modified

An electrical form design and management method for conducting design and management on electrical forms provides a technique capable of improving the maintenance efficiency of electrical forms. The method includes a step of referencing mapping information, which associates electrical forms with components used in the electrical forms, and searching for electrical forms using an updated component, and a step of updating or adding components used in the electrical forms obtained by the search or mapping information of the electrical forms, and thereby updating or generating electrical forms.

Electrical form design and management method, and recording medium

Upon modifying a database system, a modification range is managed on the basis of a pattern name, a variable name associated therewith, an influence range detection start destination based on the variable name, and an influence range detection result as modification pattern information. And a variable value is registered according to a pattern selected based on the modification pattern information, a modification object on a plurality of middleware bases is detected as influence range information on the basis of the variable value entered in association with a modification pattern, and a schema definition of a database and a group of commands for data transformation are modified in accordance with the modification contents using the influence range information.

Method for changing database construction information

Customer data is displayed to a user for making decisions in dealing with customers. The displayed data is generated based upon characteristic rules that are generated with respect to predetermined data definition information on the customer data. After confirming the effect of adding or deleting certain conditions to and from characteristic data segments as specified by the characteristic rules, the user selects a segment of particular interest. Subsequently, the user specifies certain similar customers from the selected segment to be used for speculation based upon a speculation model so that the speculation model has a relatively high precision level. Additionally, the user modifies the conditions on the speculation results to further understand the bases for the inclusion of the customers in the speculation. The user considers the future course of action towards certain customers based upon the above understandings.

Method and system for database management for data mining

A data analyzing method for generating a rule based on data items in a data base, wherein the rule expresses relational features of the data items. The invention includes a user interface and a rule generation module. The rule generation module, in response to an input from the user via the user interface, selects data items for use in a conditional clause and a conclusion clause of a rule from the data items stored in the data base, converts, when the selected data items have numerical values, the numerical values into symbolic values and creates plural candidate rules each expressing a correlation between selected data items in a rule form having one or plural sets of item names and symbolic values. The rule generation module further calculates a criterion for evaluating strength of correlation between data items in each of the candidate rules, determines one or plural candidate rules having highest calculated criterion from the candidate rules, and outputs to the user via the user interface the one or plural candidate rules.

Data analyzing method for generating rules

A data analyzing method and system for generating a rule based on data items in a data base, wherein the rule expresses relational features of the data items. The invention includes a user interface and a rule generation module. The rule generation module, in response to an input from the user via the user interface, selects data items for use in a conditional clause and a conclusion clause of a rule from the data items stored in the data base, converts, when the selected data items have numerical values, the numerical values into symbolic values and creates plural candidate rules each expressing a correlation between selected data items in a rule form having one or plural sets of item names and symbolic values. The rule generation module further calculates a criterion for evaluating strength of correlation between data items in each of the candidate rules, determines one or plural candidate rules having highest calculated criterion from the candidate rules, and outputs to the user via the user interface the one or plural candidate rules.

Data analyzing method

In a data utilizing system, a statistic table exhibiting a characteristic distribution is automatically extracted from given statistic tables of data. After confirmation of distribution of the data on the extracted statistic table, data required is retrieved from the statistic table. The data utilizing system for extracting from data constituted by a set of records each composed of at least two item values each containing at least one numeric item value, a part of the records constituting a part of the data, includes: as statistic table list generating unit for generating from the data statistic tables of component data for which at least one item value is definitely specified, and for determining arithmetically characteristic quantities for the statistic tables, respectively, on the basis of differences between the statistic tables and relevant expected value distribution tables, to thereby generate a list of the statistic tables on the basis of the characteristic quantities; and a record list generating unit for generating from the data a list of records contained in all or at least one specified cell of a specified given one of the statistic tables.

Data extracting system based on characteristic quantities of data

The present invention is intended to automatically select least required input items for a non-linear model and improve an efficiency of building up the non-linear model. For building up, for example, a neural network as the non-linear model, a group-by rule 105 and dividing point information 106 of the data are automatically generated by a group-by rule induction device 104 by selecting a dividing method option 101 and a group selection information 103 if data for learning 101 is given. An initial neural network model generating device 107 automatically generates an initial neural network model 108 from the group-by rule 105. The initial neural network model is learned in a neural network model learning device 111 and outputted as a post-learning neural network model 112. Data for learning with group information 110 which is the data for learning is generated in a data classification device 109 by using data for learning 102, group-by rule 105 and dividing point information 106. Input-output variables can be automatically selected from the data for learning according to selection of the group and a neural network model of respective groups can be built up.

Non-linear model automatic generating method

A rule generation apparatus includes a label presenter in which when producing from training data including a set of specific values related to input and output variables rules representing input/output relationships between the input and output variables, numeric data of the training data is converted into categorical data expressed by symbols to generate an instance table, an RI device for extracting rules from the instance table, and a rule converter for converting the extracted rules into fuzzy rules. When the training data is divided to be distributively stored in a plurality of server processors, label assignment is conducted by each server processor such that a client processor later combines instance tables with each other to achieve rule induction and conversion.

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Kawasaki, Japan

Hitoshi Ashida