On the Connections between Relational and XML Probabilistic Data Models

A number of uncertain data models have been proposed,based on the notion of compact representations of probability distributionsover possible worlds. In probabilistic relational models, tuples areannotated with probabilities or formulae over Boolean random variables.In probabilistic XML models, XML trees are augmented with nodesthat specify probability distributions over their children. Both kinds ofmodels have been extensively studied, with respect to their expressivepower, compactness, and query efficiency, among other things. Probabilisticdatabase systems have also been implemented, in both relationaland XML settings. However, these studies have mostly been carried outindependently and the translations between relational and XML models,as well as the impact for probabilistic relational databases of resultsabout query complexity in probabilistic XML and vice versa, have notbeen made explicit: we detail such translations in this article, in bothdirections, study their impact in terms of complexity results, and presentinteresting open issues about the connections between relational andXML probabilistic data models.

Keyword Search over Probabilistic XML Documents Based on Node Classification

This paper describes a keyword search measure on probabilistic XML data based on ELM (extreme learning machine). We use this method to carry out keyword search on probabilistic XML data. A probabilistic XML document differs from a traditional XML document to realize keyword search in the consideration of possible world semantics. A probabilistic XML document can be seen as a set of nodes consisting of ordinary nodes and distributional nodes. ELM has good performance in text classification applications. As the typical semistructured data; the label of XML data possesses the function of definition itself. Label and context of the node can be seen as the text data of this node. ELM offers significant advantages such as fast learning speed, ease of implementation, and effective node classification. Set intersection can compute SLCA quickly in the node sets which is classified by using ELM. In this paper, we adopt ELM to classify nodes and compute probability. We propose two algorithms that are based on ELM and probability threshold to improve the overall performance. The experimental results verify the benefits of our methods according to various evaluation metrics.

Efficiently Subtree Matching between XML and Probabilistic XML Documents

We explored the subtree matching problem of probabilistic XML documents: finding the matches of an XML query tree over a probabilistic XML document, using the canonical tree edit distance as a similarity measure between subtrees. Probabilistic XML is a probability distribution model capturing uncertainty of both value and structure. Query over probabilistic XML documents is difficult: an naivie algorithm has exponential complexity by directly compute the tree edit distance between the query tree and each certain XML tree represented by the probabilistic XML document. Based on the method of tree edit distance computation over certain XML subtrees, we defined a minimum-solution to the edit distance computation, which means the minimum cost to translate the query tree to the probabilistic XML tree. Furthermore, we developed an algorithm---ASM (Algorithm of Subtree Matching) to compute the minimum solution. Finally, we proved the complexity of ASM is linear in the size of the probabilistic XML document.