Our experimental analysis of several popular XPath processors reveals a striking fact: Query evaluation in each of the systems requires time exponential in the size of queries in the worst case. We show that XPath can be processed much more efficiently, and propose main-memory algorithms for this problem with polynomial-time combined query evaluation complexity. Moreover, we present two fragments of XPath for which linear-time query processing algorithms exist. 1

Queries on XML documents typically combine selections on element contents, and, via path expressions, the structural relationships between tagged elements. Structural joins are used to find all pairs of elements satisfying the primitive structural relationships specified in the query, namely, parent child and ancestor descendant relationships. Efficient support for structural joins is thus the key to efficient implementations of XML queries. Recently proposed node numbering schemes enable the capturing of the XML document structure us- ing traditional indices (such as B+-trees or R-trees). This paper proposes efficient struc- tural join algorithms in the presence of tag indices. We first concentrate on using B+- trees and show how to expedite a structural join by avoiding collections of elements that do not participate in the join. We then intro- duce an enhancement (based on sibling pointers) that further improves performance. Such sibling pointers are easily implemented and dynamically maintainable. We also present a structural join algorithm that utilizes R-trees.

Contemporary XPath query engines evaluate queries in time exponential in the sizes of input queries, a fact that has gone unnoticed for a long time. Recently, the first mainmemory evaluation algorithm for XPath 1.0 with polynomial time combined complexity, i.e., which runs in polynomial time both with respect to the size of the data and the queries, has been published (cf. [11]). In this paper

As a simple XML query language but with enough expressive power, XPath has become very popular. To expedite evaluation of XPath queries, we consider the problem of rewriting XPath queries using materialized XPath views. This problem is very important and arises not only from query optimization in server side but also from semantic caching in client side. We consider the problem of deciding whether there exists a rewriting of a query using XPath views and the problem of finding minimal rewritings. We first consider those two problems for a very practical XPath fragment containing the descendent, child, wildcard and branch features. We show that the rewriting existence problem is coNP-hard and the problem of finding minimal rewritings is 3 . We also consider those two rewriting problems for three subclasses of this XPath fragment, each of which contains child feature and two of descendent, wildcard and branch features, and show that both rewriting problems can be polynomially solved. Finally, we give an algorithm for finding minimal rewritings, which is sound for the XPath fragment, but is also complete and runs in polynomial time for its three subclasses.

Abstract—Recent works have shown the benefits of keyword proximity search in querying XML documents in addition to text documents. For example, given query keywords over Shakespeare’s plays in XML, the user might be interested in knowing how the keywords cooccur. In this paper, we focus on XML trees and define XML keyword proximity queries to return the (possibly heterogeneous) set of minimum connecting trees (MCTs) of the matches to the individual keywords in the query. We consider efficiently executing keyword proximity queries on labeled trees (XML) in various settings: 1) when the XML database has been preprocessed and 2) when no indices are available on the XML database. We perform a detailed experimental evaluation to study the benefits of our approach and show that our algorithms considerably outperform prior algorithms and other applicable approaches.

Recently, the database research literature has seen an explosion of publications with the goal of using an RDBMS to store and/or query XML data. The problems addressed and solved in this area are diverse. This diversity renders it di#cult to know how the various results presented fit together, and even makes it hard to know what open problems remain. As a first step to rectifying this situation, we present a classification of the problem space and discuss how almost 40 papers fit into this classification. As a result of this study, we find that some basic questions are still open. In particular, for the XML publishing of relational data and for "schema-based" shredding of XML documents into relations, there is no published algorithm for translating even simple path expression queries (with the // axis) into SQL when the XML schema is recursive.

This work may be seen as a further proof of the versatility of the relational database model. Here, we add XQuery to the catalog of languages which RDBMSs are able to "speak" fluently. Given suitable relational encodings of sequences and ordered, unranked trees

There are more spots than immediately obvious in XQuery expressions where order is immaterial for evaluation— this affects most notably, but not exclusively, expressions in the scope of unordered { } and the argument of fn:unordered (). Clearly, performance gains are lurking behind such expression contexts but the prevalent impact of order on the XQuery semantics reaches deep into any compliant XQuery processor, making it non-trivial to set this potential free. Here, we describe how the relational XQuery compiler Pathfinder uniformly exploits such order indifference in a purely algebraic fashion: Pathfinder-emitted plans faithfully implement the required XQuery order semantics but (locally) ignore order wherever this is admitted.

Abstract. XML indices are essential for efficiently processing XML queries which typically have predicates on both structures and values. Since the number of all possible structural and value indices is large even for a small XML document with a simple structure, XML DBMSs must carefully choose which indices to build. In this paper, we propose a tool, called XIST, that can be used by an XML DBMS as an index selection tool. XIST exploits XML structural information, data statistics, and query workload to select the most beneficial indices. XIST employs a technique that organizes paths that are evaluated to the same result into equivalence classes and uses this concept to reduce the number of paths considered as candidates for indexing. XIST selects a set of candidate paths and evaluates the benefit of an index on each candidate path on the basis of performance gains for non-update queries and penalty for update queries. XIST also recognizes that an index on a path can influence the benefit of an index on another path and accounts for such index interactions. We present an experimental evaluation of XIST and current XML index selection techniques, and show that the indices selected by XIST result in greater overall improvements in query response times and often require less disk space. 1

XML is becoming the standard for exchanging and querying information across enterprises. Furthermore, much of e-business XML data increasingly relies on accompanying XSD schema specifications