Semantic-aware query processing for activity trajectories

Huiwen Liu, Jiajie Xu, Kai Zheng, Chengfei Liu, Lan Du, Xian Wu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

29 Citations (Scopus)


Nowadays, users of social networks like tweets and weibo have generated massive geo-tagged records, and these records reveal their activities in the physical world together with spatio-temporal dynamics. Existing trajectory data management studies mainly focus on analyzing the spatio-temporal properties of trajectories, while leaving the understanding of their activities largely untouched. In this paper, we incorporate the semantic analysis of the activity information embedded in trajectories into query modelling and processing, with the aim of providing end users more accurate and meaningful trip recommendations. To this end, we propose a novel trajectory query that not only considers the spatio-temporal closeness but also, more importantly, leverages probabilistic topic modelling to capture the semantic relevance of the activities between data and query. To support efficient query processing, we design a novel hybrid index structure, namely ST-tree, to organize the trajectory points hierarchically, which enables us to prune the search space in spatial and topic dimensions simultaneously. The experimental results on real datasets demonstrate the efficiency and scalability of the proposed index structure and search algorithms.

Original languageEnglish
Title of host publicationWSDM 2017
Subtitle of host publicationProceedings of the 10th ACM International Conference on Web Search and Data Mining
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages10
ISBN (Electronic)9781450346757
Publication statusPublished - 2017
Externally publishedYes
Event10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom
Duration: 6 Feb 201710 Feb 2017


Other10th ACM International Conference on Web Search and Data Mining, WSDM 2017
Country/TerritoryUnited Kingdom


  • activity trajectories query
  • spatial keywords
  • semantic relevance


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