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 PaperResearchpeer-review

    20 Citations (Scopus)

    Abstract

    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 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
    Subtitle of host publicationFebruary 6–10, 2017, Cambridge, United Kingdom
    EditorsAndrew Tomkins, Min Zhang
    Place of PublicationNew York, New York
    PublisherAssociation for Computing Machinery (ACM)
    Pages283-292
    Number of pages10
    ISBN (Electronic)9781450346757
    DOIs
    Publication statusPublished - 2 Feb 2017
    EventACM International Conference on Web Search and Data Mining 2017 - Cambridge, United Kingdom
    Duration: 6 Feb 201710 Feb 2017
    Conference number: 10th
    https://dl.acm.org/doi/proceedings/10.1145/3018661

    Conference

    ConferenceACM International Conference on Web Search and Data Mining 2017
    Abbreviated titleWSDM 2017
    CountryUnited Kingdom
    CityCambridge
    Period6/02/1710/02/17
    Internet address

    Keywords

    • Activity trajectories query
    • Semantic relevance
    • Spatial keywords

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