1 / 18

The Parallel Processing of Spatial Selection for Very Large Geo-spatial Databases

The Parallel Processing of Spatial Selection for Very Large Geo-spatial Databases. TAMURA Keiichi , NAKANO Yuya, KANEKO Kunihiko, MAKINOUCHI Akifumi Graduate School of Information Science and Electrical Engineering Kyushu University 6-10-1 Hakozaki, Fukuoka, Japan. Contents. Background

rona
Download Presentation

The Parallel Processing of Spatial Selection for Very Large Geo-spatial Databases

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Parallel Processing of Spatial Selection for Very Large Geo-spatial Databases TAMURA Keiichi, NAKANO Yuya, KANEKO Kunihiko, MAKINOUCHI Akifumi Graduate School of Information Science and Electrical Engineering Kyushu University 6-10-1 Hakozaki, Fukuoka, Japan

  2. Contents • Background • Spatial selection • Design and Implementation • Experimental Result • Conclusion

  3. Background • Geo-spatial Database (geographical database, earth science database) • 2-d data (point, polygon, line segment) • spatial selection Large volume of spatial data sets Parallel processing of spatial selection 3

  4. Window Spatial Selection “Find all objects that intersect with a given window” 4

  5. B A A1 B3 B1 C A R*-tree A2 A3 C1 B2 A A B C C2 A１ A2 A3 B1 B2 B3 C1 C2 MBR (Minimum Bounding Rectangle) Objects R*-tree 5

  6. candidates hits hits Spatial Access Method (SAM) Spatial Selection Database Filter Step search R*-tree Refinement Step access Objects Result 6

  7. Operation Operation Operation Operation Partitioning partition1 Partition N Site1 Site N Partition N Partition1 Partition Parallelism Original database 7

  8. DatabaseDesign Original database Round Robin Partitioning R*-tree Object Object Object Partitions N-1 Partitions N Partitions1

  9. 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 Partition1 Partition4 Partition3 Partition2 Round Robin partitioning For N Partitions Put kth object in the partition((k-1) mod N)+ 1 9

  10. Design • Partitions are mapped to sites using Round Robin partitioning • Filter Step • Parallel search using each partition own R*-tree • Refinement Step • Parallel access using each partition 10

  11. Implementation Main Query Local Area Network Site 1 Site N-1 Site N Site N+1 Sub Query Sub Query Sub Query Coordinate Site 11

  12. Result request Filter Step Refinement Step MainQuery SubQuery SubQuery SubQuery R*-tree Object Object Object Partitions N-1 Partitions N Partitions1 12

  13. Filter Step Refinement Step 1 1 N-1 N-1 N N result request MainQuery SubQuery R*-tree Object Object Object Partitions N-1 Partitions N Partitions1 13

  14. Related Work • Paradise, University of Wisconsin • Partition parallelism 14

  15. Extended Sequoia 2000 Benchmark • Sequoia 2000 Benchmark • Benchmark test for geographical, scientific, engineering databases • Real data and real 11 queries • Extend the size of data and query for • Parallel database • Requirement of new area applications 15

  16. Environment • Sun Ultra/5 Workstation ×16 • Processor 177Mhz ×１，Memory 128Mbyte • Disk 30GB • Sun Ultra/10 Workstation×1 • Processor 440Mhz ×１，Memory 1024Mbyte • Disk 100GB • Network 100M-Ethernet Switch • DBMS ShusseUo 16

  17. Result (Query6) 24.0 Warm Hot 20.0 16.0 12.0 8.0 4.0 0

  18. Conclusion • Parallel processing of spatial selection • Round Robin partitioning • Each Partition has its own R*-tree • Filter Step • Parallel search using each partition own R*-tree • Refinement Step • Parallel access candidates using partition • Experimental Result • Good speed up 18

More Related