Trip Oriented Search on Activity Trajectory

• Published in: Journal of computer science and technology Vol. 30; no. 4; pp. 745 - 761
• Main Author: 陈伟 赵雷 许佳捷 刘冠锋 郑凯 周晓方
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2015; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Science; Data Structures and Information Theory; Information Systems Applications (incl.Internet); Location based services; Measurement techniques; Performance indices; Queries; Query processing; Ratings; Regular Paper; Searching; Social networks; Software Engineering; Studies; Theory of Computation; Thresholds; Trajectories; 位置服务; 数据结构; 文本描述; 时空信息; 空间距离; 索引结构; 绩效目标; 行搜索; United States > US; trajectory search; rating score; activity trajectory
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-015-1558-6

Driven by the flourish of location-based services, trajectory search has received significant attentions in recent years. Different from existing studies that focus on searching trajectories with spatio-temporal information and text de-scriptions, we study a novel problem of searching trajectories with spatial distance, activities, and rating scores. Given a query q with a threshold of distance, a set of activities, a start point S and a destination E, trip oriented search on activity trajectory （TOSAT） returns k trajectories that can cover the activities with the highest rating scores within the threshold of distance. In addition, we extend the query with an order, i.e., order-sensitive trip oriented search on activity trajectory （OTOSAT）, which takes both the order of activities in a query q and the order of trajectories into consideration. It is very challenging to answer TOSAT and OTOSAT e？ciently due to the structural complexity of trajectory data with rating infor-mation. In order to tackle the problem e？ciently, we develop a hybrid index AC-tree to organize trajectories. Moreover, the optimized variant RAC＋-tree and novel algorithms are introduced with the goal of achieving higher performance. Extensive experiments based on real trajectory datasets demonstrate that the proposed index structures and algorithms are capable of achieving high e？ciency and scalability.