NDLI: Semantic-aware Query Processing for Activity Trajectories

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Semantic-aware Query Processing for Activity Trajectories

Content Provider	ACM Digital Library
Author	Liu, Chengfei Xu, Jiajie Zheng, Kai Wu, Xian Du, Lan Liu, Huiwen
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.
Starting Page	283
Ending Page	292
Page Count	10
File Format	PDF
ISBN	9781450346757
DOI	10.1145/3018661.3018678
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semantic relevance Activity trajectories query Spatial keywords
Content Type	Text
Resource Type	Article

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