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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

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

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  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

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