Quasi-SLCA based Keyword Query Processing over Probabilistic XML Data

TL;DR

This paper introduces a novel approach for processing probabilistic threshold keyword queries over XML data using quasi-SLCA and a probabilistic index, demonstrating improved efficiency and effectiveness through empirical evaluation.

Contribution

It proposes the first method for probabilistic threshold keyword queries over XML data, introducing quasi-SLCA, a probabilistic index, and two algorithms with performance enhancements.

Findings

The proposed methods are effective on real and synthetic datasets.

The algorithms outperform baseline in speed and accuracy.

The probabilistic index significantly reduces query processing time.

Abstract

The probabilistic threshold query is one of the most common queries in uncertain databases, where a result satisfying the query must be also with probability meeting the threshold requirement. In this paper, we investigate probabilistic threshold keyword queries (PrTKQ) over XML data, which is not studied before. We first introduce the notion of quasi-SLCA and use it to represent results for a PrTKQ with the consideration of possible world semantics. Then we design a probabilistic inverted (PI) index that can be used to quickly return the qualified answers and filter out the unqualified ones based on our proposed lower/upper bounds. After that, we propose two efficient and comparable algorithms: Baseline Algorithm and PI index-based Algorithm. To accelerate the performance of algorithms, we also utilize probability density function. An empirical study using real and synthetic data sets has verified the effectiveness and the efficiency of our approaches.