Name
Abstract | ||
|---|---|---|
Indexing moving objects has been extensively studied in the past decades. Moving objects, such as vehicles and mobile device users, usually exhibit some patterns on their velocities, which can be utilized for velocity-based partitioning to improve performance of the indexes. Existing velocity-based partitioning techniques rely on some kinds of heuristics rather than analytically calculate the optimal solution. In this paper, we propose a novel speed partitioning technique based on a formal analysis over speed values of the moving objects. We first formulate the optimal speed partitioning problem based on search space expansion analysis and then compute the optimal solution using dynamic programming. We then build the partitioned indexing system where queries are duplicated and processed in each index partition. Extensive experiments demonstrate that our method dramatically improves the performance of indexes for moving objects and outperforms other state-of-the-art velocity-based partitioning approaches. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-22363-6_12 | ADVANCES IN SPATIAL AND TEMPORAL DATABASES (SSTD 2015) |
Field | DocType | Volume |
Dynamic programming,Data mining,Computer science,Search engine indexing,Mobile device,Heuristics,Partition (number theory) | Journal | 9239 |
ISSN | Citations | PageRank |
0302-9743 | 4 | 0.41 |
References | Authors | |
26 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaofeng Xu | 1 | 425 | 37.75 |
| Li Xiong | 2 | 2335 | 142.42 |
| Vaidy S. Sunderam | 3 | 4 | 0.41 |
| Jinfei Liu | 4 | 91 | 11.12 |
| Jun Luo | 5 | 222 | 26.61 |