Efficient top K temporal spatial keyword search

Zhang, Chengyuan, Zhu, Lei, Yu, Weiren, Long, Jun, Huang, Fang and Zhao, Hongbo (2018). Efficient top K temporal spatial keyword search. IN: Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer.


Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper, we study the top-k temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques.

Publication DOI: https://doi.org/10.1007/978-3-030-04503-6_7
Divisions: Engineering & Applied Sciences
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: © Springer Nature Switzerland AG 2018
Event Title: 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018
Event Type: Other
Event Dates: 2018-06-03 - 2018-06-03
Uncontrolled Keywords: Theoretical Computer Science,Computer Science(all)
Full Text Link: https://arxiv.o ... /abs/1805.02009
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... 3-030-04503-6_7 (Publisher URL)
Published Online Date: 2018-11-21
Authors: Zhang, Chengyuan
Zhu, Lei
Yu, Weiren
Long, Jun
Huang, Fang
Zhao, Hongbo



Version: Accepted Version

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