A PRQ Search Method for Probabilistic Objects

TL;DR

This paper introduces a simplified and optimized PRQ search method for probabilistic objects, utilizing pre-approximation and cost-based strategies to improve efficiency, validated through extensive experiments.

Contribution

The paper presents a novel PRQ search approach that employs pre-approximation and cost analysis for enhanced efficiency in probabilistic object queries.

Findings

Effective reduction of problem complexity through pre-approximation

Optimizations based on subdivision count and span size improve performance

Experimental results demonstrate the method's efficiency and effectiveness

Abstract

This article proposes an PQR search method for probabilistic objects. The main idea of our method is to use a strategy called \textit{pre-approximation} that can reduce the initial problem to a highly simplified version, implying that it makes the rest of steps easy to tackle. In particular, this strategy itself is pretty simple and easy to implement. Furthermore, motivated by the cost analysis, we further optimize our solution. The optimizations are mainly based on two insights: (\romannumeral 1) the number of \textit{effective subdivision}s is no more than 1; and (\romannumeral 2) an entity with the larger \textit{span} is more likely to subdivide a single region. We demonstrate the effectiveness and efficiency of our proposed approaches through extensive experiments under various experimental settings.