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Published in:
2018 | OriginalPaper | Chapter
Scalable Distributed Temporal Reasoning
Authors : Jonghoon Kim, Incheol Kim
Published in: Advances in Computer Science and Ubiquitous Computing
Publisher: Springer Singapore
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Abstract
In this paper, we propose the design and implementation of a large-scale qualitative temporal reasoner, MRQUTER, which can perform reasoning over large Web-scale knowledge bases. This temporal reasoner is built on a Hadoop cluster system using the MapReduce parallel programming framework. It decomposes the entire qualitative temporal reasoning process into several MapReduce jobs and incorporates some optimization techniques into each reasoning job component, implemented using a pair of Map and Reduce functions. Through experiments using large benchmarking temporal knowledge bases, MRQUTER shows high reasoning performance and scalability.