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
Group Nearest Neighbor Queries
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Aggregate Nearest Neighbor Queries in Road Networks
IEEE Transactions on Knowledge and Data Engineering
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
Distance indexing on road networks
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Efficient Approximation of Spatial Network Queries using the M-Tree with Road Network Embedding
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
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 AKNN spatial network queries using the M-Tree
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Scalable network distance browsing in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Voronoi-based aggregate nearest neighbor query processing in road networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
On trip planning queries in spatial databases
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Aggregate nearest neighbor search methods using SSMTA* algorithm on road-network
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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Searching for the shortest paths from a starting point to several target points on a road network is an essential operation for several kinds of queries in location based services. This search can be easily done using Dijkstra's algorithm. Although an A* algorithm is faster for finding the shortest path between two points, it is not so quick when several target points are given, because it must iterate pairwise searches. As the number of target points increases, the number of duplicated calculations for road network nodes also increases. This duplication degrades efficiency. A single-source multi-target A* (SSMTA*) algorithm is proposed to cope with this problem. It requires only one calculation per node and considerably outperforms Dijkstra's algorithm, especially when the target points are distributed with bias. An application with this algorithm for aggregate nearest neighbor search demonstrated its efficiency, especially when the number of target points is large.