4 August 2004
Mining, Indexing, and Querying Historical Spatiotemporal Data
Speaker: CAO Huiping
Abstract
In many applications that track and analyze spatiotemporal data, movements
obey periodic patterns; the objects follow the same routes (approximately)
over regular time intervals. For example, people wake up at the same time
and follow more or less the same route to their work everyday. The
discovery of hidden periodic patterns in spatiotemporal data, apart from
unveiling important information to the data analyst, can facilitate data
management substantially. Based on this observation, we propose a
framework that analyzes, manages, and queries object movements that follow
such patterns. We define the spatiotemporal periodic pattern mining
problem and propose an effective and fast mining algorithm for retrieving
maximal periodic patterns. We also devise a novel, specialized index
structure that can benefit from the discovered patterns to support more
efficient execution of spatiotemporal queries. We evaluate our methods
experimentally using datasets with object trajectories that exhibit
periodicity.
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