Answering XML queries using materialized views revisited

  • Authors:

  • Xiaoying Wu;Dimitri Theodoratos;Wendy Hui Wang

  • Affiliations:

  • New Jersey Institute of Technology, Newark, USA;New Jersey Institute of Technology, Newark, USA;Stevens Institute of Technology, Hoboken, USA

  • Venue:
  • Proceedings of the 18th ACM conference on Information and knowledge management
  • Year:
  • 2009

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Abstract

Answering queries using views is a well-established technique in databases. In this context, two outstanding problems can be formulated. The first one consists in deciding whether a query can be answered exclusively using one or multiple materialized views. Given the many alternative ways to compute the query from the materialized views, the second problem consists in finding the best way to compute the query from the materialized views. In the realm of XML, there is a restricted number of contributions in the direction of these problems due to the many limitations associated with the use of materialized views in traditional XML query evaluation models. In this paper, we adopt a recent evaluation model, called inverted lists model, and holistic algorithms which together have been established as the prominent technique for evaluating queries on large persistent XML data, and we address the previous two problems. This new context revises these problems since it requires new conditions for view usability and new techniques for computing queries from materialized views. We suggest an original approach for materializing views which stores for every view node only the list of XML nodes necessary for computing the answer of the view. We specify necessary and sufficient conditions for answering a tree-pattern query using one or multiple materialized views in terms of homomorphisms from the views to the query. In order to efficiently answer queries using materialized views, we design a stack-based algorithm which compactly encodes in polynomial time and space all the homomorphisms from a view to a query. We further propose space and time optimizations by using bitmaps to encode view materializations and by employing bitwise operations to minimize the evaluation cost of the queries. Finally, we conducted an extensive experimentation which demonstrates that our approach yields impressive query hit rates in the view pool, achieves significant time and space savings and shows smooth scalability.