Name
Abstract | ||
|---|---|---|
The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data streams. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management, and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from trajectory streams. The traveling buddies are microgroups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along the trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. In addition, we extend the proposed framework to discover companions on more complicated scenarios with spatial and temporal constraints, such as on the road network and battlefield. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. Experimental results show that our proposed buddy-based approach is an order of magnitude faster than the baselines and achieves higher accuracy in companion discovery. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2542182.2542185 | ACM TIST |
Keywords | Field | DocType |
trajectory stream,object detail,spatio-temporal data,flexible companion discovery,moving-together object,data structure,data processing,companion discovery,closed companion candidate,trajectory data stream,mobile technology,clustering,algorithms,performance,trajectory | Overhead (computing),Data structure,Data mining,Data stream mining,Data processing,Data stream,Computer science,Artificial intelligence,Cluster analysis,Machine learning,Trajectory,Scalability | Journal |
Volume | Issue | ISSN |
5 | 1 | 2157-6904 |
Citations | PageRank | References |
28 | 1.12 | 44 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lu An Tang | 1 | 427 | 27.74 |
| Yu Zheng | 2 | 8939 | 432.87 |
| Nicholas Jing Yuan | 3 | 2617 | 128.44 |
| Jiawei Han | 4 | 43085 | 3824.48 |
| Alice Leung | 5 | 187 | 11.25 |
| Wen-Chih Peng | 6 | 1645 | 106.49 |
| Thomas La Porta | 7 | 801 | 91.33 |