With the advent of XML as a standard for data representation and exchange on the Internet, storing and querying XML data becomes more and more important. Several XML query languages have been proposed, and the common feature of the languages is the use of regular path expressions to query XML data. This poses a new challenge concerning indexing and searching XML data, because conventional approaches based on tree traversals may not meet the processing requirements under heavy access requests. In this paper, we propose a new system for indexing and storing XML data based on a numbering scheme for elements. This numbering scheme quickly determines the ancestor-descendant relationship between elements in the hierarchy of XML data. We also propose several algorithms for processing regular path expressions, namely, (1) ##-Join for searching paths from an element to another, (2) ##-Join for scanning sorted elements and attributes to find element-attribute pairs, and (3) ##-Join for finding Kleene-Closure on repeated paths or elements. The ##-Join algorithm is highly effective particularly for searching paths that are very long or whose lengths are unknown. Experimental results from our prototype system implementation show that the proposed algorithms can process XML queries with regular path expressions by up to an or- # This work was sponsored in part by National Science Foundation CAREER Award (IIS-9876037) and Research Infrastructure program EIA-0080123. The authors assume all responsibility for the contents of the paper. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its...

This paper describes the overall design and architecture of the Timber XML database system currently being implemented at the University of Michigan. The system is based upon a bulk algebra for manipulating trees, and natively stores XML. New access methods have been developed to evaluate queries in the XML context, and new cost estimation and query optimization techniques have also been developed. We present performance numbers to support some of our design decisions. We believe that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store, with all the standard components of relational query processing, including algebraic rewriting and a cost-based optimizer.

The W3C XQuery language recommendation, based on a hierarchical and ordered document model, supports a wide variety of constructs and use cases. There is a diversity of approaches and strategies for evaluating XQuery expressions, in many cases only dealing with limited subsets of the language. In this paper we describe an implementation approach that handles XQuery with arbitrarily-nested FLWR expressions, element constructors and built-in functions (including structural comparisons). Our proposal maps an XQuery expression to a single equivalent SQL query using a novel dynamic interval encoding of a collection of XML documents as relations, augmented with information tied to the query evaluation environment. The dynamic interval technique enables (suitably enhanced) relational engines to produce predictably good query plans that do not restrict the use of sort-merge join query operators. The benefits are realized despite the challenges presented by intermediate results that create arbitrary documents and the need to preserve document order as prescribed by semantics of XQuery. Finally, our experimental results demonstrate that (native or relational) XML systems can benefit from the above technique to avoid a quadratic scale up penalty that e#ectively prevents the evaluation of nested FLWR expressions for large documents.

Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-theart with a number of new technical contributions, such as looplifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11 GB. The performance section also provides an extensive comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met. 1.

Relational query processors derive much of their effectiveness from the awareness of specific table properties like sort order, size, or absence of duplicate tuples. This text applies (and adapts) this successful principle to database-supported XML and XPath processing: the relational system is made tree aware, i.e., tree properties like subtree size, intersection of paths, inclusion or disjointness of subtrees are made explicit. We propose a local change to the database kernel, the staircase join, which encapsulates the necessary tree knowledge needed to improve XPath performance. Staircase join

XML documents are typically queried with a combination of value search and structure search. While querying by values can leverage traditional database technologies, evaluating structural relationship, specifically parent-child or ancestor-descendant relationship, between XML element sets has imposed a great challenge on efficient XML query processing. This paper proposes XR-tree, namely, XML Region Tree, which is a dynamic external memory index structure specially designed for strictly nested XML data. The unique feature of XR-tree is that, for a given element, all its ancestors (or descendants) in an element set indexed by an XRtree can be identified with optimal worst case I/O cost. We then propose a new structural join algorithm that can evaluate the structural relationship between two XR-tree indexed element sets by effectively skipping ancestors and descendants that do not participate in the join. Our extensive performance study shows that the XR-tree based join algorithm significantly outperforms previous algorithms. 1.

Finding all the occurrences of a twig pattern specified by a selection predicate on multiple elements in an XML document is a core operation for e#cient evaluation of XML queries. Holistic twig join algorithms were proposed recently as an optimal solution when the twig pattern only involves ancestordescendant relationships. In this paper, we address the problem of e#cient processing of holistic twig joins on all/partly indexed XML documents. In particular, we propose an algorithm that utilizes available indices on element sets. While it can be shown analytically that the proposed algorithm is as e#cient as the existing state-of-the-art algorithms in terms of worst case I/O and CPU cost, experimental results on various datasets indicate that the proposed index-based algorithm performs significantly better than the existing ones, especially when binary structural joins in the twig pattern have varying join selectivities.

XML has become the lingua franca for data exchange and integration across administrative and enterprise boundaries. Nearly all data providers are adding XML import or export capabilities, and standard XML Schemas and DTDs are being promoted for all types of data sharing. The ubiquity of XML has removed one of the major obstacles to integrating data from widely disparate sources -- namely, the heterogeneity of data formats. However, general-purpose integration of data across the wide area also requires a query processor that can query data sources on demand, receive streamed XML data from them, and combine and restructure the data into new XML output --- while providing good performance for both batch-oriented and ad-hoc, interactive queries. This is the goal of the Tukwila data integration system, the first system that focuses on network-bound, dynamic XML data sources. In contrast to previous approaches, which must read, parse, and often store entire XML objects before querying them, Tukwila can return query results even as the data is streaming into the system. Tukwila is built with a new system architecture that extends adaptive query processing and relational-engine techniques into the XML realm, as facilitated by a pair of operators that incrementally evaluate a query's input path expressions as data is read. In this paper, we describe the Tukwila architecture and its novel aspects, and we experimentally demonstrate that Tukwila provides better overall query performance and faster initial answers than existing systems, and has excellent scalability.

This article is a proposal for a database index structure, the XPath accelerator, that has been specifically designed to support the evaluation of XPath path expressions. As such, the index is capable to support all XPath axes (including ancestor, following, preceding-sibling, descendant-or-self, etc.). This feature lets the index stand out among related work on XML indexing structures which had a focus on the child and descendant axes only. The index has been designed with a close eye on the XPath semantics as well as the desire to engineer its internals so that it can be supported well by existing relational database query processing technology: the index (a) permits set-oriented (or, rather, sequence-oriented) path evaluation, and (b) can be implemented and queried using well-established relational index structures, notably B-trees and R-trees

Finding all the occurrences of a twig pattern in an XML database is a core operation for efficient evaluation of XML queries. A number of algorithms have been proposed to process a twig query based on region encoding labeling scheme. While region encoding supports efficient determination of ancestor-descendant (or parent-child) relationship between two elements, we observe that the information within a single label is very limited. In this paper, we propose a new labeling scheme, called extended Dewey. This is a powerful labeling scheme, since from the label of an element alone, we can derive all the elements names along the path from the root to the element. Based on extended Dewey, we design a novel holistic twig join algorithm, called TJ-Fast. Unlike all previous algorithms based on region encoding, to answer a twig query, TJ-Fast only needs to access the labels of the leaf query nodes. Through this, not only do we reduce disk access, but we also support the efficient evaluation of queries with wildcards in branching nodes, which is very difficult to be answered by algorithms based on region encoding. Finally, we report our experimental results to show that our algorithms are superior to previous approaches in terms of the number of elements scanned, the size of intermediate results and query performance.