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Data-driven Context Discovery Model for Semantic Computing in the Big Data Era

Authors:
Takafumi Nakanishi

Keywords: data-driven; context; feature selection; big data; data set

Abstract:
We introduce a data-driven context discovery model for semantic computing in the big data era. Our model extracts from data sets the appropriate feature set as the context. We suggest that selection of a target data set is one of the representation processes for this purpose and the context in a big data environment. When a person selects target data from big data, that action latently indicates the context represented by the person's intention. Selecting a feature set from big data constitutes a data-driven context creation. Recently, fragmental data has spread on the Internet. In order to analyze big data, it is necessary to aggregate the appropriate data from data that has been dispersed on the Internet. An aggregation policy represents the purposes or contexts of analysis. In the big data era, it is necessary to focus not only on analysis but also on aggregation. After data aggregation, it is necessary to extract feature sets for semantic computing. This is what our model focuses on.

Pages: 76 to 81

Copyright: Copyright (c) IARIA, 2015

Publication date: July 19, 2015

Published in: conference

ISSN: 2308-4510

ISBN: 978-1-61208-420-6

Location: Nice, France

Dates: from July 19, 2015 to July 24, 2015