Efficient top K temporal spatial keyword search

Chengyuan Zhang, Lei Zhu, Weiren Yu, Jun Long*, Fang Huang, Hongbo Zhao

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


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.

Original languageEnglish
Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers
Number of pages13
ISBN (Print)9783030045029
Publication statusPublished - 2018
Event22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
Duration: 3 Jun 20183 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11154 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018

Bibliographical note

© Springer Nature Switzerland AG 2018


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