Structural XML Query Processing

Abstract

Since the boom in new proposals on techniques for efficient querying of XML data is now over and the research world has shifted its attention toward new types of data formats, we believe that it is crucial to review what has been done in the area to help users choose an appropriate strategy and scientists exploit the contributions in new areas of data processing. The aim of this work is to provide a comprehensive study of the state-of-the-art of approaches for the structural querying of XML data. In particular, we start with a description of labeling schemas to capture the structure of the data and the respective storage strategies. Then we deal with the key part of every XML query processing: a twig query join, XML query algebras, optimizations of query plans, and selectivity estimation of XML queries. To the best of our knowledge, this is the first work that provides such a detailed description of XML query processing techniques that are related to structural aspects and that contains information about their theoretical and practical features as well as about their mutual compatibility and general usability.

Solving the Intractable Problem: Optimal Performance for Worst Case Scenarios in XML Twig Pattern Matching

“…The experimental results are very similar over the five datasets so, in this paper, we focus on the results from the TreeBank dataset because it is the most complex XML document from most aspects of query processing [6], [35] even though it is not the largest in our experiments. This means that the suboptimal evaluation of the existing approaches can be demonstrated here most clearly.…”

“…A Twig Pattern Query (TPQ) is an XML path expression which represents the basic building block of XML query languages. The existing literature on XML query processing is extensive and focuses particularly on the twig pattern matching problem because it is the hardest [6], [40]. A Twig Pattern Match is defined as mapping function M between a given tree pattern query Q and an XML document D, M : Q → D that maps nodes of the query Q into nodes of the document D preserving structural relationships and satisfying the predicates of Q.…”

“…On the other hand, bottom-up algorithms store elements of the input document in post-order manner and inspect matching elements through virtual sub-trees. A major drawback of this second approach is its high memory consumption because all elements mapping to leaf query nodes reside in the main memory until the entire document has been completely processed but, in terms of processing time, it is faster than top down algorithms [6].…”

“…Figure 1 depicts the simple partition technique used by XML query processing called a tag streaming scheme where each query node q is associated with a stream T q consisting of all nodes with the same tag as q sorted in an order compatible with the depth-first traversal of the XML tree. Holistic algorithms can not guarantee that the head elements would form matches to TPQs comprising of P-C edges [6], [13], [20]. This dilemma (i.e., two head elements block each other with respect to P-C relationship) means that holistic algorithms must either output useless intermediate results or risk missing some potential answers to TPQs.…”

GridTables: A One-Size-Fits-Most H2TAP Data Store

“…In addition to that, it is not yet clear how to combine novel research suggestions in a unified system, and how such suggestions may affect or benefit from each other. In particular, our research community shows opportunities and challenges of modern hardware in database systems in isolation, among them the need for analysis of novel adaptive data layouts and data structures for operational and analytical systems [6,7,9,55], novel processing, storage and federation approaches on non-relational data models [11,18,51,52,62], benefits and drawbacks of porting to new compute platforms [12,16,33,63], opportunities and limitations of GPUs and other co-processors as building blocks for storage and querying purposes [8,14,33], novel proposals for main memory databases on modern hardware [3,15,21,53,56], adaptive optimization, and first attempts towards self-managing database systems [17,36,43,46].…”