Transformation of XML data using an unranked tree transducer

Transformation of data documents is of special importance to use XML as the universal data interchange format on the Web. Data transformation is used in many tasks that require data to be transferred between existing, independently created Web-oriented applications. To perform such transformation one can use W3C's XSLT or XQuery. But these languages are devoted to detailed programming of transformation procedures. In this paper we show how data transformation can by specify by means of high-level rule specifications based on uniform unranked tree transducers. We show that our approach is both descriptive and expressive, and we illustrate how it can be used to specify and perform transformations of XML documents

Axiomatization of frequent sets

In data mining association rules are very popular. Most of the algorithms in the literature for finding association rules start by searching for frequent itemsets. The itemset mining algorithms typically interleave brute force counting of frequencies with a meta-phase for pruning parts of the search space. The knowledge acquired in the counting phases can be represented by frequent set expressions. A frequent set expression is a pair containing an itemset and a frequency indicating that the frequency of that itemset is greater than or equal to the given fre-quency. A system of frequent sets is a collection of such expressions. We give an axiomatization for these systems. This axiomatization characterizes complete systems. A system is complete when it explicitly contains all information that it logically implies. Every system of frequent sets has a unique completion. The completion of a system actually represents the knowledge that maximally can be derived in the meta-phase