Abstract
The location selection (LS) problem identifies an optimal site to place a new facility such that its influence on given objects can be maximized. With the proliferation of GPS-enabled mobile devices, LS studies have made progress for moving objects. However, the state-of-the-art LS techniques over moving objects assume the new facility has no competitor, which is too restrictive and unrealistic for real-world business. In this paper we study Competitive Location Selection over Moving objects (CLS-M), which takes into account competition against existing facilities in mobile scenarios. We present a competition-based influence score model to evaluate the influence of a candidate. To solve the problem, we propose an influence pruning algorithm to prune objects who are either influenced by inferior candidates or affected by no candidate. Experimental study over two real-world datasets demonstrates that the proposed algorithm outperforms state-of-the-art LS techniques in terms of efficiency.
Highlights
Location selection (LS) problems have been extensively studied in spatial databases
To address the limitations of existing LS techniques, we study the competition-based LS problem in moving scenarios, called Competitive Location Selection over Moving objects (CLS-M), which takes into account both mobility and competition
We are ready to define the top-k Competitive Location Selection over Moving objects (CLS-M) problem addressed in this paper
Summary
Location selection (LS) problems have been extensively studied in spatial databases. Given a set of objects and a set of candidate locations C, the LS problem aims to find an optimal candidate location c ∈ C , such that c can influence a maximum number of objects. Recall that studies [3, 4] considered the impact of competition among existing facilities nearby in the LS problem. Their influence model is based on Bichromatic Reverse Nearest Neighbor (BRNN) criterion [5] and static single-point objects without considering the mobility. Their competition-based techniques are unsuitable for solving the aforementioned problem. To address the limitations of existing LS techniques, we study the competition-based LS problem in moving scenarios, called Competitive Location Selection over Moving objects (CLS-M), which takes into account both mobility and competition.
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