Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Efficient method for maximizing bichromatic reverse nearest neighbor
Proceedings of the VLDB Endowment
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Continuous maximal reverse nearest neighbor query on spatial networks
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald's) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L1 and L2/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this paper, we focus on optimal network location (ONL) queries, i.e., optimal location queries with which objects and sites reside on a spatial network. We introduce an approach, namely EONL (short for Expansion-based ONL), which enables efficient computation of ONL queries. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approach with real datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.