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 language | English (US) |
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Title of host publication | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 |
Editors | Naoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 542-547 |
Number of pages | 6 |
ISBN (Electronic) | 9781538650356 |
DOIs | |
State | Published - Jul 2 2018 |
Event | 2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States Duration: Dec 10 2018 → Dec 13 2018 |
Publication series
Name | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 |
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Conference
Conference | 2018 IEEE International Conference on Big Data, Big Data 2018 |
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Country/Territory | United States |
City | Seattle |
Period | 12/10/18 → 12/13/18 |
Bibliographical note
Funding Information:This work is supported in part by the National Science Foundation under Grant No. 1302439 and 1302423.
Funding Information:
ACKNOWLEDGMENT This work is supported in part by the National Science Foundation under Grant No. 1302439 and 1302423.
Publisher Copyright:
© 2018 IEEE.
Keywords
- GPU computing
- query processing
- trajectory similarity