Towards efficient search for activity trajectories

Zheng, Kai, Shang, Shuo, Yuan, Nicholas Jing and Yang, Yi (2013). Towards efficient search for activity trajectories. In: ICDE 2013 - 29th International Conference on Data Engineering. 29th International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, (230-241). 8-12 April 2013. doi:10.1109/ICDE.2013.6544828

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Author Zheng, Kai
Shang, Shuo
Yuan, Nicholas Jing
Yang, Yi
Title of paper Towards efficient search for activity trajectories
Conference name 29th International Conference on Data Engineering, ICDE 2013
Conference location Brisbane, Australia
Conference dates 8-12 April 2013
Proceedings title ICDE 2013 - 29th International Conference on Data Engineering   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2013
Sub-type Fully published paper
DOI 10.1109/ICDE.2013.6544828
ISBN 9781467349086
ISSN 1084-4627
Start page 230
End page 241
Total pages 12
Collection year 2014
Language eng
Abstract/Summary The advances in location positioning and wireless communication technologies have led to a myriad of spatial trajectories representing the mobility of a variety of moving objects. While processing trajectory data with the focus of spatio-temporal features has been widely studied in the last decade, recent proliferation in location-based web applications (e.g., Foursquare, Facebook) has given rise to large amounts of trajectories associated with activity information, called activity trajectory. In this paper, we study the problem of efficient similarity search on activity trajectory database. Given a sequence of query locations, each associated with a set of desired activities, an activity trajectory similarity query (ATSQ) returns k trajectories that cover the query activities and yield the shortest minimum match distance. An order-sensitive activity trajectory similarity query (OATSQ) is also proposed to take into account the order of the query locations. To process the queries efficiently, we firstly develop a novel hybrid grid index, GAT, to organize the trajectory segments and activities hierarchically, which enables us to prune the search space by location proximity and activity containment simultaneously. In addition, we propose algorithms for efficient computation of the minimum match distance and minimum order-sensitive match distance, respectively. The results of our extensive empirical studies based on real online check-in datasets demonstrate that our proposed index and methods are capable of achieving superior performance and good scalability.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

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