The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Evaluating a class of distance-mapping algorithms for data mining and clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Data mining: concepts and techniques
Data mining: concepts and techniques
Dynamic maintenance of data distribution for selectivity estimation
The VLDB Journal — The International Journal on Very Large Data Bases
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases
Proceedings of the 17th International Conference on Data Engineering
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Voronoi-based K nearest neighbor search for spatial network databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient shortest path finding of k-nearest neighbor objects in road network databases
Proceedings of the 2010 ACM Symposium on Applied Computing
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In this paper, we address approximate indexing for efficient processing of k-nearest neighbor(k-NN) queries in road network databases. Previous methods suffer from either serious performance degradation in query processing or large storage overhead because they did not employ indexing mechanisms based on their network distances. To overcome these drawbacks, we propose a novel method that builds an index on those objects in a road network by approximating their network distances and processes k-NN queries efficiently by using that index. Also, we verify the superiority of the proposed method via extensive experiments using the real-life road network databases.