Name
Abstract | ||
|---|---|---|
With the widespread usage of wireless network and mobile devices, the scale of spatial-temporal data is dramatically increasing and a good deal of real world applications can be formulated as processing continuous queries over moving objects. Most existing works investigating this problem mainly concern about the centralized search algorithm for dealing with range queries over a limited volume of objects, but these approaches hardly can scale well in a cluster of servers. Additionally, the existing approaches seldom process the situation that the locations of objects and queries are simultaneously changing. To address this challenge, we propose a distributed grid index and a distributed incremental search approach to handle concurrent continuous range queries over an ocean of moving objects. As to the distributed grid index, it can be deployed on a distributed computing framework to well support the real-time maintenance of moving objects. Further, we take fully into account the condition that locations of objects and queries are both changing at the same time, and put forward a parallel search approach based on the publish/subscribe mechanism to achieve incrementally searching results of each continuous range queries with a cluster of servers. Finally, we conduct extensive experiments to sufficiently evaluate the performance of our proposal. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-63312-1_72 | INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT II |
Keywords | Field | DocType |
Continuous range queries, Distributed processing, Incremental search, Moving objects | Publication,Wireless network,Search algorithm,Computer science,Server,Range query (data structures),Incremental search,Mobile device,Grid,Distributed computing | Conference |
Volume | ISSN | Citations |
10362 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Zhou | 1 | 32 | 14.41 |
| Hao Teng | 2 | 1 | 2.37 |
| Ziqiang Yu | 3 | 8 | 3.89 |
| Dong Wang | 4 | 1351 | 186.07 |
| Jiaqi Wang | 5 | 33 | 7.67 |