Towards efficient search for activity trajectories

Zheng, K; Shang, S; Yuan, NJ; Yang, Y

Towards efficient search for activity trajectories

Zheng, K Shang, S Yuan, NJ Yang, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Data Engineering, 2013, pp. 230 - 241
Issue Date:: 2013-08-15

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Field	Value	Language
dc.contributor.author	Zheng, K	en_US
dc.contributor.author	Shang, S	en_US
dc.contributor.author	Yuan, NJ	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.date.issued	2013-08-15	en_US
dc.identifier.citation	Proceedings - International Conference on Data Engineering, 2013, pp. 230 - 241	en_US
dc.identifier.isbn	9781467349086	en_US
dc.identifier.issn	1084-4627	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120773
dc.description.abstract	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. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Data Engineering	en_US
dc.relation.isbasedon	10.1109/ICDE.2013.6544828	en_US
dc.title	Towards efficient search for activity trajectories	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

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. © 2013 IEEE.

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