著者

神野良太 上原邦昭 JINNO Ryota UEHARA Kuniaki

雑誌

SIG-DOCMAS = SIG-DOCMAS

巻号頁・発行日

no.B101, 2011-12-14

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As the location-acquisition technologies become increasingly pervasive, tracking themovement of objects from trajectory datasets are more and more available. As a result, discoveringfrequent movement patterns from such a dataset has recently gained great interest. However,trajectory dataset is usually large in volume and exceeds the computation capacity of traditionalcentralized technologies. We propose a new approach to discovering patterns over a massive dataset based on distributed storage and computing. We apply the proposed approach to differentreal-world datasets in different conditions. We also discuss the results and possible future researchdirections.