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Using Dijkstra's algorithm to incrementally find the k-Nearest Neighbors in spatial network databases
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Proceedings of the 2006 ACM symposium on Applied computing table of contents
Dijon, France
SESSION: Advances in spatial and image-based information systems (ASIIS) table of contents
Pages: 58 - 62  
Year of Publication: 2006
ISBN:1-59593-108-2
Authors
Victor Teixeira de Almeida  Praktische Informatik IV Fernuniversität Hagen, Hagen, Germany
Ralf Hartmut Güting  Praktische Informatik IV Fernuniversität Hagen, Hagen, Germany
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

One of the most important kinds of queries in Spatial Network Databases (SNDB) to support Location-Based Services (LBS) is the k-Nearest Neighbors (k-NN) query. Given a point in a network, e.g. a location of a car on a road network, and a set of points of interests, e.g. hotels, gas stations, etc., the k-NN query returns the k points of interest closest to the query point. The network distance is used in such a query instead of the Euclidean distance. Dijkstra's algorithm is a well known solution to this problem. In this paper, we propose a storage schema with a set of index structures to support an efficient execution of a slightly modified version of the Dijkstra's algorithm. We show in an experimental evaluation with generated data sets that our proposal is more efficient than the state-of-the-art solution to this problem.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Victor Teixeira de Almeida: colleagues
Ralf Hartmut Güting: colleagues