FastTopK: A Fast Top-K Trajectory Similarity Query Processing Algorithm for GPUs

Hamza Mustafa, Eleazar Leal, Le Gruenwald

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the increasing prevalence of location sensor devices like GPS, it has been possible to collect large datasets of a special type of spatio-temporal data called trajectory data. A trajectory is a discrete sequence of positions that a moving object occupies in space as time passes. Such large datasets enable researchers to study the behavior of the objects describing these movements by issuing spatial queries. Among the queries that can be issued are top-K trajectory similarity queries, which retrieve the K most similar trajectories to a given query trajectory. This query has applications in many areas, such as urban planning, ecology and social networking; however, this query is computationally expensive. In this work, we introduce a new parallel top-K trajectory similarity query technique for GPUs, FastTopK, to deal with these challenges. Our experiments on two large real-life datasets showed that FastTopK produces on average 107.96X smaller candidate result sets, and 3.36X faster query execution times than the existing state-of-the-art technique, TKSimGPU.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages542-547
Number of pages6
ISBN (Electronic)9781538650356
DOIs
StatePublished - Jan 22 2019
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: Dec 10 2018Dec 13 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
CountryUnited States
CitySeattle
Period12/10/1812/13/18

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Keywords

  • GPU computing
  • query processing
  • trajectory similarity

Cite this

Mustafa, H., Leal, E., & Gruenwald, L. (2019). FastTopK: A Fast Top-K Trajectory Similarity Query Processing Algorithm for GPUs. In Y. Song, B. Liu, K. Lee, N. Abe, C. Pu, M. Qiao, N. Ahmed, D. Kossmann, J. Saltz, J. Tang, J. He, H. Liu, & X. Hu (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 542-547). [8621907] (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8621907