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A New Spatial Index Structure for Efficient Query Processing in Location Based Services

A New Spatial Index Structure for Efficient Query Processing in Location Based Services. Speaker : Yihao Jhang Adviser: Yuling Hsueh. 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. Outline. Introduction Related work Grid Index B + -tree

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A New Spatial Index Structure for Efficient Query Processing in Location Based Services

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  1. ANew Spatial Index Structure for Efficient Query Processing in Location Based Services Speaker:Yihao Jhang Adviser: Yuling Hsueh 2010IEEEInternational Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

  2. Outline • Introduction • Related work • Grid Index • B+-tree • ISGrid • Query Processing • Experiment • Conclusion

  3. Introduction • A new spatial index structure. • ISGrid provides better efficient query processing than R-tree. • ISGrid is a grid structure that provides direct accesses to data and uses Minimum Boundary Rectangle(MBR) as a leaf node.

  4. Grid index • Grid is a regular tessellation of a 2-D surface that divides it into a series of contiguous cells, which can then be assigned unique identifiers and used for spatial indexing purposes.

  5. B+-tree • B+-tree is a tree structure. It usually employed in database or file operating system. • It has the link to point to the closer data and allow quick sequence read the data.

  6. ISGrid • Configuration of ISGrid

  7. ISGrid(cont.)

  8. ISGrid(cont.) • How to choose neighbor nodes? • Traditional: the order of the distance. (x) • Best method: Voronoi Diagram

  9. Query Processing • k-NN Queries • STEP 1: Searching the nearest leaf node to the query point using the grid index. • STEP 2: Searching the k-NNs through visiting the neighbor node entry.

  10. Query Processing(cont.) STEP1 STEP2

  11. Query Processing(cont.) • Range Queries • STEP1: Searching the nearest leaf node to the query point using the grid index. • STEP2: Searching the objects within a certain range using the neighbor node information.

  12. Query Processing(cont.) STEP1 STEP2

  13. Experiment • Performance of k-NN query processing.

  14. Experiment(cont.) • Performance of continuous k-NN by CNNS.

  15. Conclusions • Authors proposed an index structure, called ISGrid. • ISGrid provides efficient continuous k-NN query processing in the environment for static objects and moving queries.

  16. Thank you for Listening!

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