Efficient top K temporal spatial keyword search


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: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Funding Information: Acknowledgments. This work was supported in part by the National Natural Science Foundation of China (61702560, 61379110, 61472450), the Key Research Program of Hunan Province (2016JC2018), Natural Science Foundation of Hunan Province (2018JJ3691), and Sc
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)
ISBN: 9783030045029
Last Modified: 08 Jan 2024 09:57
Date Deposited: 08 Jan 2019 09:16
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)
PURE Output Type: Conference contribution
Published Date: 2018
Published Online Date: 2018-11-21
Accepted Date: 2018-06-01
Authors: Zhang, Chengyuan
Zhu, Lei
Yu, Weiren (ORCID Profile 0000-0002-1082-9475)
Long, Jun
Huang, Fang
Zhao, Hongbo



Version: Accepted Version

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