Probabilistic Maximum Range-Sum Queries on Spatial Database

Qiyu Liu, Xiang Lian, Lei Chen

Abstract: Maximum Range-Sum (MaxRS) query is an important operator in spatial database for retrieving regions of interest (ROIs). Given a rectangular query size a × b and a set of spatial objects associated with positive weights, MaxRS retrieves rectangular regions Q of size a × b, such that the sum of object weights covered by Q (i.e., range-sum) is maximized. Due to the inaccuracy of the location acquisition, the collected locations of spatial objects are inherently uncertain and imprecise, which can be modeled by uncertain objects. In this paper, we propose a Probabilistic Maximum Range-Sum (PMaxRS) query over uncertain spatial objects, which obtains a set γ* of rectangles such that the probability that each region Q ϵ γ* has the maximum range-sum exceeds a user-specified threshold Pt. We show that determining whether a given region Q is #P-complete. To tackle the hardness, we introduce the PMaxRS_Framework based on pruning and refinement strategies. In the pruning step, we propose a candidate generation technique to reduce the search space. In the refinement step, we design an efficient sampling-based approximation algorithm to verify the remaining candidate regions. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of our algorithms.