Proximity queries in large traffic networks

In this paper, we present an original network graph embedding to speed-up distance-range and k-nearest neighbor queries in (weighted) graphs. Our approach implements the paradigm of filter-refinement query processing and can be used for proximity queries on both static as well as dynamic objects. In particular, we present how our embedding can be used to compute a lower and upper bounding filter distance which approximates the true shortest path distance significantly better than traditional filters, e.g. the Euclidean distance. These distance approximations can be used within a filter step to prune true drops and true hits as well as in the refinement step in order to guide an informed A* search. Our experimental evaluation on several real-world data sets demonstrates a significant performance boosting of our proposed concepts over existing work. Categories and Subject Descriptors H.3.3 [INFORMATION STORAGE AND RETRIEVAL]: Information Search and Retrieval General Terms Performance Ke...

• Type: Conference
• Year: 2007
• Where: GIS
• Authors: Hans-Peter Kriegel, Matthias Renz, Peer Kröger, Peter Kunath, Tim Schmidt
• Keywords: Database, GIS 2007, K-nearest Neighbor Queries, Keywords Proximity Queries, Shortest Path Distance