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

• Published in: Journal of computer science and technology Vol. 30; no. 4; pp. 725 - 744
• Main Author: 商烁 谢珂心 郑凯 刘家俊 文继荣
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2015; Springer Nature B.V
• Subjects: Advertising; Algorithms; Artificial Intelligence; Computer engineering; Computer Science; Data Structures and Information Theory; Fast food; Information Systems Applications (incl.Internet); Location based services; Mapping; Object motion; Optimization; Optimization techniques; Regular Paper; Restaurants; Segments; Social security; Software Engineering; Spatial data; Studies; Supermarkets; Theory of Computation; Trajectories; 兴趣点; 基于位置的服务; 持续时间; 映射; 移动对象; 空连接; 空间点; 视频; United States > US; spatial database; trajectory; spatio-temporal join; spatial join
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-015-1557-7

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 （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs 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 τ 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 e？ciently, 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.