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, L 1 and L 2 /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.
Given a set S of sites and a set O of weighted objects located on a spatial network, the optimal network location (ONL) query computes a location on the spatial network where introducing a new site would maximize the total weight of the objects that are closer to the new site than to any other site. The existing solutions for optimal network location query assume that sites and objects rarely change their location over time, whereas there are numerous new applications with which sites and/or objects frequently change location. Unfortunately, the existing solutions for optimal network location query are not applicable to answer these socalled dynamic optimal network location queries (DONL), since the result generated by such solutions is most probably invalid by the time computation is complete. In this paper for the first time we formalize the problem of DONL queries as Continuous Maximal Reverse Nearest Neighbor (CMaxRNN) queries on spatial networks, and introduce an approach that allows for efficient and incremental update of MaxRNN query results on spatial networks. With an extensive experimental study we evaluate and demonstrate the efficiency of our proposed approach with both synthetic and real-world datasets.
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