A hybrid algorithm for finding top-k twig answers in probabilistic XML

Uncertainty is inherently ubiquitous in data of real applications, and those uncertain data can be naturally represented by the XML. Matching twig pattern against XML data is a core problem, and on the background of probabilistic XML, each twig answer has a probabilistic value because of the uncertainty of data. The twig answers that have small probabilistic values are useless to the users, and the users only want to get the answers with the largest k probabilistic values. In this paper, we address the problem of finding twig answers with top-k probabilistic values against probabilistic XML documents directly. To cope with this problem, we propose a hybrid algorithm which takes both the probability value constraint and structural relationship constraint into account. The main idea of the algorithm is that the element with larger path probability value will more likely contribute to the twig answers with larger twig probability values, and at the same time lots of useless answers that do not satisfy the structural constraint can be filtered. Therefore the proposed algorithm can avoid lots of intermediate results, and find the top-k answers quickly. Experiments have been conducted to study the performance of the algorithm.