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Towards Efficient Indexing of Spatiotemporal Trajectories on the GPU for Distance Threshold Similarity Searches

Michael Gowanlock, Henri Casanova
Department of Information and Computer Sciences and NASA Astrobiology Institute, University of Hawai’i, Honolulu, HI, U.S.A.
arXiv:1410.2698 [cs.DC], (10 Oct 2014)

@article{2014arXiv1410.2698G,

   author={Gowanlock}, M. and {Casanova}, H.},

   title={"{Technical Report: Towards Efficient Indexing of Spatiotemporal Trajectories on the GPU for Distance Threshold Similarity Searches}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1410.2698},

   primaryClass={"cs.DC"},

   keywords={Computer Science – Distributed, Parallel, and Cluster Computing, Computer Science – Databases},

   year={2014},

   month={oct},

   adsurl={http://adsabs.harvard.edu/abs/2014arXiv1410.2698G},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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Applications in many domains require processing moving object trajectories. In this work, we focus on a trajectory similarity search that finds all trajectories within a given distance of a query trajectory over a time interval, which we call the distance threshold similarity search. We develop three indexing strategies with spatial, temporal and spatiotemporal selectivity for the GPU that differ significantly from indexes suitable for the CPU, and show the conditions under which each index achieves good performance. Furthermore, we show that the GPU implementations outperform multithreaded CPU implementations in a range of experimental scenarios, making the GPU an attractive technology for processing moving object trajectories. We test our implementations on two synthetic and one real-world dataset of a galaxy merger.
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