A road network embedding technique for k-nearest neighbor search in moving object databases
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Nearest neighbor queries in road networks
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
Snapshot location-based query processing on moving objects in road networks
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Fast nearest neighbor search on road networks
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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
On trip planning queries in spatial databases
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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Recently, K-Nearest Neighbor(KNN) query processing over moving objects in road networks is becoming an interesting problem which has caught more and more researchers' attention. Distance pre-computation is an efficient approach for this problem. However, when the road network is large, this approach requires too much memory to use in some practical applications. In this paper, we present a simple and efficient pre-computation technique to solve this problem, with loss of some accuracy. In our pre-computation approach, we choose a proper representative nodes set R from road network G(V, E) (R is a subset of V) and compute the distance values of any pairs in R which are pre-computed. Since |R| ≪ |V|, our approach requires so less memory size that the KNN query can be processed in one common personal computer. Moreover, the approximation of distance value between any pairs in V is well bounded. The experimental results showed that this pre-computation technique yielded excellent performance with good approximation guarantee and high processing speed.