Torch: A Search Engine for Trajectory Data

Wang, S, Bao, Z, Culpepper, S, Xie, Z, Liu, Q and Qin, X 2018, 'Torch: A Search Engine for Trajectory Data', in Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '18), Ann Arbor, United States, 8-12 July 2018, pp. 535-544.


Document type: Conference Paper
Collection: Conference Papers

Title Torch: A Search Engine for Trajectory Data
Author(s) Wang, S
Bao, Z
Culpepper, S
Xie, Z
Liu, Q
Qin, X
Year 2018
Conference name SIGIR '18
Conference location Ann Arbor, United States
Conference dates 8-12 July 2018
Proceedings title Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '18)
Publisher Association for Computing Machinery
Place of publication United States
Start page 535
End page 544
Total pages 10
Abstract This paper presents a new trajectory search engine called Torch for querying road network trajectory data. Torch is able to efficiently process two types of typical queries (similarity search and Boolean search), and support a wide variety of trajectory similarity functions. Additionally, we propose a new similarity function LORS in Torch to measure the similarity in a more effective and efficient manner. Indexing and search in Torch works as follows. First, each raw vehicle trajectory is transformed to a set of road segments (edges) and a set of crossings (vertices) on the road network. Then a lightweight edge and vertex index called LEVI is built. Given a query, a filtering framework over LEVI is used to dynamically prune the trajectory search space based on the similarity measure imposed. Finally, the result set (ranked or Boolean) is returned. Extensive experiments on real trajectory datasets verify the effectiveness and efficiency of Torch.
Subjects Information Retrieval and Web Search
Database Management
DOI - identifier 10.1145/3209978.3209989
Copyright notice © 2018 Association for Computing Machinery.
ISBN 9781450356572
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