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 1

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

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. 3

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. 4

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. 5

ISGrid Configuration of ISGrid 6

ISGrid(cont.) 7

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

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. 9

Query Processing(cont.) 10 STEP1 STEP2

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. 11

Query Processing(cont.) 12 STEP1 STEP2

Experiment Performance of k-NN query processing. 13

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

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. 15

Thank you for Listening! 16