Abstract
Spatial databases are entering an era of mass deployment in various real-life applications, especially mobile and location-based services. The real-time processing of spatial queries to meet different performance goals poses new problems to the real-time and parallel processing communities. We investigate how multiple window queries can be parallelized, decomposed, scheduled and processed in real time workloads to optimize system performance, such as I/O cost, response time and miss rate. We devise in-memory R-trees to decompose queries into independent jobs. Jobs from different queries can be combined according to their spatial locality to eliminate redundant I/Os. Runtime job schedulers are elaborately devised to optimize response time or miss rate for various systems. Empirical results show a significant performance improvement over the sequential, unparalleled approach.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2003 International Conference on Parallel Processing, ICPP 2003 |
| Publisher | IEEE |
| Pages | 565-572 |
| Number of pages | 8 |
| Volume | 2003-January |
| ISBN (Electronic) | 0769520170 |
| DOIs | |
| Publication status | Published - 1 Jan 2003 |
| Externally published | Yes |
| Event | 2003 International Conference on Parallel Processing, ICPP 2003 - Kaohsiung, Taiwan Duration: 6 Oct 2003 → 9 Oct 2003 |
Conference
| Conference | 2003 International Conference on Parallel Processing, ICPP 2003 |
|---|---|
| Country/Territory | Taiwan |
| City | Kaohsiung |
| Period | 6/10/03 → 9/10/03 |
Keywords
- Cost function
- Delay
- Dynamic scheduling
- Mobile computing
- Parallel processing
- Processor scheduling
- Runtime
- Spatial databases
- System performance
- Vehicle dynamics
ASJC Scopus subject areas
- Software
- General Mathematics
- Hardware and Architecture