Group Location Selection Queries over Uncertain Objects

Abstract

Given a set of spatial objects, facilities can influence the objects located within their influence regions that are represented by circular disks with the same radius r. Our task is to select the minimum number of locations such that establishing a temporary facility at each selected location would ensure that all the objects are influenced. Aiming to solve this location selection problem, we propose a novel kind of location selection query, called group location selection (GLS) queries. In many real-world applications, every object is usually located within an uncertainty region instead of at an exact point. Due to the uncertainty of the data, GLS processing needs to ensure that the probability of each uncertain object being influenced by one facility is not less than a given threshold . An analysis of the time cost reveals that it is infeasible to exactly answer GLS queries over uncertain objects in polynomial time. Hence, this paper proposes an approximate query framework for answering queries efficiently while guaranteeing that the results of GLS queries are correct with a bounded probability. The performance of the proposed methods of the framework is demonstrated by theoretical analysis and extensive experiments with both real and synthetic data sets.

PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects

“…There have been increasing research efforts in LS problem under various scenarios [1], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]. These efforts can be broadly classified into two groups, namely, maximum influence-based (MAX-INF) and minimum distance-based (MIN-DIST).…”

“…They further proposed (1 − )-approximate MaxRS algorithm to significantly improve its efficiency in [19]. Recently, Xu et al [11] proposed group location selection (GLS) problem to find the minimum number of multiple locations with influence regions, such that all the objects can be covered. However, in contrast to our problem setting, these efforts assume that the objects are static.…”

GOAL: a clustering-based method for the group optimal location problem

“…The GOAL problem is NP-hard, as shown by Xu et al [33]. Intuitively, the problem involves generating every subset of O that can be enclosed in a circle with radius r and selecting the combination of the minimum number of disjoint subsets that can cover O, which can be reduced to the set cover problem.…”

“…Adapting the algorithm to our problem is inefficient because it requires running the algorithm iteratively with increasing value of k until all clients are covered. The other studies [12] [14] [33] share similar problem definition and techniques with [25] and hence their algorithms are not suitable to our problem either.…”

“…Few have studied the problem of computing a set of optimal locations to provide a full coverage to the clients. Xu et al [33] study the group location selection queries to compute a set of locations to cover the clients. They consider clients with uncertain locations, and their goal is to cover the clients with a certain probability.…”