document: Mining , indexing , and querying historical spatiotemporal data 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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