Name
Abstract | ||
|---|---|---|
The range search on trajectories is fundamental in a wide spectrum of applications such as environment monitoring and location based services. In practice, a large portion of spatio-temporal data in the above applications is generated with low sampling rate and the uncertainty arises between two subsequent observations of a moving object. To make sense of the uncertain trajectory data, it is critical to properly model the uncertainty of the trajectories and develop efficient range search algorithms on the new model. Assuming uncertain trajectories are modeled by the popular Markov Chains, in this paper we investigate the problem of range search on uncertain trajectories. In particular, we propose a general framework for range search on uncertain trajectories following the filtering-and-refinement paradigm where summaries of uncertain trajectories are constructed to facilitate the filtering process. Moreover, statistics based and partition based filtering techniques are developed to enhance the filtering capabilities. Comprehensive experiments demonstrate the effectiveness and efficiency of our new techniques. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2806416.2806430 | ACM International Conference on Information and Knowledge Management |
Field | DocType | Citations |
Data mining,Search algorithm,Computer science,Sampling (signal processing),Markov chain,Location-based service,Filter (signal processing),Partition (number theory),Trajectory | Conference | 1 |
PageRank | References | Authors |
0.35 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liming Zhan | 1 | 24 | 2.93 |
| Ying Zhang | 2 | 1288 | 90.39 |
| Wenjie Zhang | 3 | 1616 | 105.67 |
| Xiaoyang Wang | 4 | 104 | 16.69 |
| Xuemin Lin | 5 | 5585 | 307.32 |