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Efficient Network Distance Browsing in Spatial Databases

This study presents an efficient approach for network distance browsing in spatial databases, focusing on real-time shortest path queries on static networks with variable search criteria. The proposed method includes efficient path encoding and retrieval techniques to enhance scalability and response time. Key concepts covered include spatial networks, nearest network neighbors, and quad trees. Evaluation involves micro benchmarks and real data sets.

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Efficient Network Distance Browsing in Spatial Databases

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  1. Scalable Network Distance Browsing in Spatial DatabaseSamet, H., Sankaranarayanan, J., and Alborzi H. Proceedings of the 2008 ACM SIGMOD international Conference on Management of Data Presented by: Don Eagan Chintan Patel http://www-users.cs.umn.edu/~cpatel/8715.html

  2. Outline • Motivation • Problem Statement • Proposed Approach • Other Approaches • Evaluation • Our Comments • Questions

  3. Motivation • Growing Popularity of Online Mapping Services

  4. Motivation • Real Time Shortest Paths

  5. Motivation • Static Network, Variable Queries • Find Gas Stations, Hotels, Markets etc.

  6. Motivation • Static Network, Variable Queries • Find Gas Stations, Hotels, Markets etc.

  7. Problem Statement • Input: • Spatial Network S, Node q from S • Output: • k-nearest neighbors of q • Objective: • Facilitate “fast” shortest path queries based on different search criteria's • Constraints/ Assumptions: • Static spatial network • Contiguous (connected) regions

  8. Challenges • Real-time response • Calculating all pairs shortest path is costly • Storing pre-computed naïvely doesn’t solve the problem • Scalability

  9. Contribution • Efficient path encoding • Efficient retrieval • Abstracting shortest path calculation from domain queries

  10. Key Concepts • Spatial Networks • Nearest Network Neighbor • Quad Tree • Morton Blocks • Decoupling • Scalability • Pre-computing

  11. Spatial Networks • Graph with spatial components represented as nodes/ edges • Most Transportations are modeled as graph • Intersection – Node/ vertex • Roads – Edge • Time/ Distance – Edge Weight

  12. K-Nearest Neighbors

  13. K-Nearest Neighbors

  14. K-Nearest Neighbors

  15. K-Nearest Neighbors

  16. K-Nearest Neighbors

  17. K-Nearest Neighbors

  18. Shortest Path • Dijkstra’s algorithm • Doesn’t work for real-time queries • Computationally expensive

  19. Proposed Approach • Pre-compute shortest paths • Store and Retrieve Efficiently N = Number of vertices, M = Number of edges, s = Length of the shortest path

  20. Path Encoding • Path coherence • Vertices in close proximity share portion of the shortest paths to them from distant sources

  21. Path Encoding • Path coherence • Vertices in close proximity share portion of the shortest paths to them from distant sources

  22. Path Encoding • Path coherence • Vertices in close proximity share portion of the shortest paths to them from distant sources

  23. Path Encoding • Path coherence • Vertices in close proximity share portion of the shortest paths to them from distant sources

  24. Path Encoding • Quadtree: Decompose until all vertices in block have same color

  25. How is space reduced? • Capturing boundaries !

  26. Path Retrieval • Retrieve quadtree corresponding to s

  27. Path Retrieval • Find connected node t in the quadtree containing d

  28. Path Retrieval • Repeat the process

  29. K-nearest Neighbor • Set of objects • Pre-computed paths (quadtree)

  30. K-nearest Neighbor • K = 2

  31. K-nearest Neighbor • Queue1: m a b • Queue2: a b

  32. K-nearest Neighbor • Queue1: a g e b f • Queue2: a g

  33. K-nearest Neighbor • Queue1: a g e • Queue2: a g

  34. K-nearest Neighbor • Return a and g

  35. Other Approaches • IER: Incremental Euclidian Restriction • Based on Euclidian distance • Dijkstra’s algorithm to get network distance • INE: Incremental. Network Expansion • Dijkstra'salgorithm with a buffer L containing the k nearest neighbors seen so far in terms of network distance

  36. Evaluation • Micro benchmark • Synthetic Data

  37. Evaluation • Real Data Set: Major Road of the USA

  38. Our Comments • We Liked: • Decoupling shortest path and neighbor calculation • Space reduction approach • Scalable • Correctness proofs • Detailed discussion about KNN variants

  39. Our Comments • What we didn’t like: • Experiments: • No comparison with other approaches (e.g. hierarchical, dynamic etc.) • No performance graphs/ discussion with real dataset

  40. Discussion • Other use cases? • Real Application: How to overcome assumptions?

  41. Questions ? ? ?

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