Approximate XML query matching and rewriting using Intuitionistic Fuzzy Trees

Abstract

XML is undoubtedly becoming the predominant de facto standard for data representation and communication, especially on the web, which in turn is causing XML data repositories to grow rapidly. Current XML Query languages, such as Xquery, have limited capabilities in querying multiple data sources with different structures (schemas) which is inefficient. Therefore, an urgent need has been identified for XML querying techniques that can overcome the rising diversity in XML data schemas. In this work, we propose our approach of using Intuitionistic Fuzzy Trees (IFTr) to achieve approximate XML query matching by considering a novel approach of matching arcs as basic units of data schemas. Additionally, we provide an algorithm for rewriting the original query to be able to retrieve data from local data sources. Our approach was tested using synthetic data sources with high degree of structural diversity, and it proved useful result while pertaining good performance and low memory usage.

New Concepts of Intuitionistic Fuzzy Trees with Applications

“…Chountas et al [21-23, 40, 56] discussed an intuitionistic fuzzy version of the special particular case of a graph, the tree, called an intuitionistic fuzzy tree. Alzebdi et al [4] proposed their approach of using intuitionistic fuzzy trees to achieve an approximate XML query matching by considering a novel approach of matching arcs as the basic units of data schemas. Bujnowski et al [14] presented a new classifier called an intuitionistic fuzzy decision tree and studied its properties.…”