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Proximity Queries in Large Traffic Networks (2007)
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BibTeX
@MISC{Kriegel07proximityqueries,
author = {Hans-Peter Kriegel and Peer Kröger and Peter Kunath and Matthias Renz and Tim Schmidt},
title = {Proximity Queries in Large Traffic Networks},
year = {2007}
}
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Abstract
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.
Keyphrases
proximity query large traffic network original network graph distance approximation euclidean distance speed-up distance-range significant performance boosting filter step true drop true hit k-nearest neighbor query upper bounding filter distance dynamic object experimental evaluation refinement step path distance several real-world data set traditional filter filter-refinement query processing
