Kabushiki Kaisha Toshiba

Toshiba Solutions Corporation

Hideki Iwasaki

Shigeru Matsumoto

Shigeaki Sakurai

Rumi Hayakawa

Shozo Isobe

Kazuyuki Goto

Kazuyoshi Nishi

Yasunari Miyabe

Kyoko Makino

Seiji Egawa

Mikito Kobayashi

A sequential data analysis apparatus extracts a pattern of two or more sets of items based on an appearance frequency of each of different sets of items in first sequential data, selects a pattern of two or more sets of items based on an appearance frequency of a sub-pattern formed of a portion of the extracted pattern, creates a related pattern including the same last set of items as and the other sets of items different from the selected characteristic pattern, calculates an evaluation value of the related pattern, creates a prediction model by organizing data of the characteristic pattern and the related pattern, and applies second sequential data to the prediction model to determine a result which the second sequential data is likely to lead to.

Sequential data analysis apparatus and program

A data analyzing apparatus of an embodiment generates a format variation, analytical algorithm name, and analytical parameter not stored in a first storage device, and executes analysis. The data analyzing apparatus determines whether the application accuracy is lower than the knowledge model accuracy. If the determination result is 'not lower', the data analyzing apparatus reactivates a format variation generating device and an analytical parameter generating device. If the determination result is 'lower', the data analyzing apparatus reads out a format variation and knowledge model name associated with the highest priority order in the first storage device, and executes analysis.

Data analyzing apparatus and program

According to one embodiment, a topic extracting apparatus extracts each term from a target document set, and calculates an appearance frequency of each term and a document frequency that each term appears. The topic extracting apparatus acquires a document set of appearance documents with respect to each extracted term, calculates a topic degree, extracts each term whose topic degree is not lower than a predetermined value as a topic word, and calculates freshness of the extracted topic word based on an appearance date and time. The topic extracting apparatus presents the extracted topic words in order of the freshness and also presents the number of appearance documents of each presented topic word per unit span.

Topic extraction apparatus and program

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Fuchu, Japan

Hideki Iwasaki