Hitachi, Ltd.

Taiki Sakata

Norio Takahashi

Toshiyuki Yamagishi

Mizuhiro Sakai

Charles A Herman

Hua-Ching Su

Mamoru Ozaki

Mashiho Kawamura

Steven Dolins

When inconsistency occurs between data presented from individual group enterprises, causes of the inconsistency cannot be specified easily in a conventional consolidated settlement processing method. In the embodiments of the invention, an XBRL conversion module converts inputted account data into data resulting from synthesis of standard data and extended data by using a mapping table and transmits the converted data to a data separation module. The data separation module separates the standard data and extended data of the received data and stores these types of data in corresponding storage tables while adding them with information for mutual relevancy. In this manner, when inconsistency occurs during a consolidated settlement process, causes of the inconsistency can be specified easily by acquiring the extended data.

System and method for processing account data

An electronic tax declaration system includes an input unit that receives financial information data to be declared; a data conversion unit that converts the received financial information data according to specifications of declaration data; a declaration data generation unit that adds a digital signature to the converted financial information data for generating declaration data and stores it into a storage; a declaration transmission unit that sends the declaration data to a regulator handling a tax; a declaration result reception unit that receives a declaration result of the declaration data from the regulator and stores it into the storage associated with the declaration data; and a loan application unit that creates loan application data by extracting the declaration data and the associated declaration result from the storage and adding a digital signature to them and sends the created loan application data to an external unit.

Tax declaration system

A method of analyzing data contained in a very large database or data warehouse includes applying first a patient rule induction method with selected input variables and an output variable to develop a region containing a subset of database records having a highest average output value. Then, the subset of records may be aggregated, sorted, compared, or new measures computed, preferably using an on line analytical processing function, for an pattern analysis of the subset records. An alternative is to apply a weighted item set function to the subset to produce a reduced subset, and then applying the reduced subset to on line analytical processing for pattern analysis.

Analysis of massive data accumulations using patient rule induction method and on-line analytical processing

Growing community of inventors

Kawasaki, Japan

Taiki Sakata