VID Join: Mapping Trajectories to Points of Interest to Support Location-Based Services

被引:11
|
作者
Shang, Shuo [1 ]
Xie, Kexin [2 ]
Zheng, Kai [3 ]
Liu, Jiajun [4 ]
Wen, Ji-Rong [5 ]
机构
[1] China Univ Petr, Dept Comp Sci, Beijing 102249, Peoples R China
[2] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia
[3] Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China
[4] Commonwealth Sci & Ind Res Org, Kenmore, Qld 4069, Australia
[5] Renmin Univ China, Key Lab Data Engn & Knowledge Engn, Beijing 100080, Peoples R China
基金
中国国家自然科学基金;
关键词
trajectory; spatial database; spatial join; spatio-temporal join; DISCOVERY; QUERIES;
D O I
10.1007/s11390-015-1557-7
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Variable influence duration (VID) join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects (e.g., travelers), and spatial points are points of interest (POIs, e.g., restaurants). VID join returns all pairs of (tau (s) , p) if tau (s) is spatially close to p for a long period of time, where tau (s) is a segment of trajectory tau a T and p a P. Each returned (tau (s) , p) implies that the moving object associated with tau (s) stayed at p (e.g., having dinner at a restaurant). Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between tau and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join efficiently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data.
引用
收藏
页码:725 / 744
页数:20
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