Encoding and decoding the Hilbert order
Software—Practice & Experience
IEEE Transactions on Knowledge and Data Engineering
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
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
Instance optimal query processing in spatial networks
The VLDB Journal — The International Journal on Very Large Data Bases
Single-Source multi-target a* algorithm for POI queries on road network
WAIM'11 Proceedings of the 2011 international conference on Web-Age Information Management
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Aggregate nearest neighbor (ANN) queries play an important role in location-based services (LBSs) when a group of users wants to find a suitable point of interest (POI) (e.g., a restaurant) beneficial to all of them. In ANN queries, a set of query points Q and an aggregate function (e.g., sum) are given, and then a POI is determined (or k POIs), which gives the minimum total travel distance from each query point to the POI. ANN query methods were first proposed giving results in Euclidean distance, and then were adapted to provide results using road-network distances which offer more practical solutions in the daily life. Among them, the incremental Euclidean restriction (IER) framework is a simple and powerful strategy for solving an ANN query using road-network distances. The IER framework consists of two phases: a candidate-generation phase and a verification phase. This paper proposes a powerful method that can be adapted to the verification phase, named a single-source multitarget A* (SSMTA*) algorithm. This paper first describes the SSMTA*, and then presents a method for its suitable application to ANN queries. Through experiments, this paper demonstrates that the proposed method outperforms the existing methods.