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

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

Speaker: Yihao JhangAdviser: Yuling Hsueh

2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

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Outline• Introduction• Related work

– Grid Index– B+-tree

• ISGrid• Query Processing• Experiment• Conclusion

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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.

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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.

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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.

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ISGrid• Configuration of ISGrid

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ISGrid(cont.)

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ISGrid(cont.)• How to choose neighbor nodes?

– Traditional: the order of the distance. (x)– Best method: Voronoi Diagram

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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.

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Query Processing(cont.)

STEP1

STEP2

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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.

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Query Processing(cont.)

STEP1

STEP2

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Experiment• Performance of k-NN query

processing.

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

CNNS.

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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.

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