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
An efficient trajectory index structure for moving objects in location-based services
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
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Recently, the spatial network databases (SNDB) have been studied for emerging applications such as location-based services including mobile search and car navigation. In practice, objects, like cars and people with mobile phones, can usually move on an underlying network (road, railway, sidewalk, river, etc.), where the network distance is determined by the length of the practical shortest path connecting two objects. In this paper, we propose materialization-based query processing algorithms for typical spatial queries in SNDB, such as range search and k nearest neighbors (k-NN) search. By using a materialization-based technique with the shortest network distances of all the nodes on the network, the proposed query processing algorithms can reduce the computation time of the network distance as well as the number of disk I/Os required for accessing nodes. Thus, the proposed query processing algorithms improve the existing efficient k-NN (INE) and range search (RNE) algorithms proposed by Papadias et al. [1], respectively. It is shown that our range query processing algorithm achieves about up to one of magnitude better performance than RNE and our k-NN query processing algorithm achieves about up to 150% performance improvements over INE.