Abstract
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.
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Htoo, H., Ohsawa, Y., Sonehara, N., Sakauchi, M. (2012). Aggregate Nearest Neighbor Search Methods Using SSMTA* Algorithm on Road-Network. In: Morzy, T., Härder, T., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2012. Lecture Notes in Computer Science, vol 7503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33074-2_14
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DOI: https://doi.org/10.1007/978-3-642-33074-2_14
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