Abstract-Extensible Markup Language (XML) is emerging as a de facto standard for information exchange among various applications on the World Wide Web. There has been a growing need for developing high-performance techniques to query large XML data repositories efficiently. One important problem in XML query processing is twig pattern matching, that is, finding in an XML data tree D all matches that satisfy a specified twig (or path) query pattern Q. In this survey, we review, classify, and compare major techniques for twig pattern matching.1 Specifically, we consider two classes of major XML query processing techniques: the relational approach and the native approach. The relational approach directly utilizes existing relational database systems to store and query XML data, which enables the use of all important techniques that have been developed for relational databases, whereas in the native approach, specialized storage and query processing systems tailored for XML data are developed from scratch to further improve XML query performance. As implied by existing work, XML data querying and management are developing in the direction of integrating the relational approach with the native approach, which could result in higher query processing performance and also significantly reduce system reengineering costs.Index Terms-XML query processing, twig pattern matching.

No abstract

No abstract

In this paper we address the problem of evaluating XPath queries over streaming XML data. We consider a practical XPath fragment called Univariate XPath, which includes the commonly used '/' and '//' axes and allows * -node tests and arbitrarily nested predicates. It is well known that this XPath fragment can be efficiently evaluated in O(|D||Q|) time in the non-streaming environment [18], where |D| is the document size and |Q| is the query size. However, this is not necessarily true in the streaming environment, since streaming algorithms have to satisfy stricter requirement than non-streaming algorithms, in that all data must be read sequentially in one pass. Therefore, it is not surprising that state-of-the-art stream-querying algorithms have higher time complexity than O(|D||Q|).In this paper we revisit the XPath stream-querying problem, and show that Univariate XPath can be efficiently evaluated in O(|D||Q|) time in the streaming environment. Specifically, we propose two O(|D||Q|)-time stream-querying algorithms, LQ and EQ, which are based on the lazy strategy and on the eager strategy, respectively. To the best of our knowledge, LQ and EQ are the first XPath stream-querying algorithms that achieve O(|D||Q|) time performance. Further, our algorithms achieve O(|D||Q|) time performance without trading off space performance. Instead, they have better buffering-space performance than state-of-the-art stream-querying algorithms. In particular, EQ achieves optimal buffering-space performance. Our experimental results show that our algorithms have not only good theoretical complexity but also considerable practical performance advantages over existing algorithms.

No abstract