Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Framework for Generating Network-Based Moving Objects
Geoinformatica
IEEE Transactions on Knowledge and Data Engineering
Spatial Databases-Accomplishments and Research Needs
IEEE Transactions on Knowledge and Data Engineering
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Nearest neighbor queries in road networks
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Indexing of network constrained moving objects
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Computational data modeling for network-constrained moving objects
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
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
The islands approach to nearest neighbor querying in spatial networks
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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In this paper, we design the architecture of disk-based data structures for spatial network databases (SNDB). Based on this architecture, we propose new query processing algorithms for range search and k nearest neighbors (k-NN) search, depending on the density of point of interests (POIs) in the spatial network. For this, we effectively combine Euclidean restriction and the network expansion techniques according to the density of POIs. In addition, our two query processing algorithms can reduce the computation time of network distance between a pair of nodes and the number of disk I/Os required for accessing nodes by using maintaining the shortest network distances of all the nodes in the spatial network. It is shown that our range query processing algorithm achieves about up to one order of magnitude better performance than the existing range query processing algorithm, such as RER and RNE [1]. In addition, our k-NN query processing algorithm achieves about up to 170~400% performance improvements over the existing network expansion k-NN algorithm, called INE, while it shows about up to one order of magnitude better performance than the existing Euclidean restriction k-NN algorithm, called IER [1].