MonetDB/XQuery: a fast XQuery processor powered by a relational engine

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-the-art with a number of new technical contributions, such as loop-lifted 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 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

An Empirical Evaluation of XQuery Processors

This paper presents an extensive and detailed experimental evaluation of XQuery processors. The study consists of running five publicly available XQuery benchmarks --- the Michigan benchmark (MBench), XBench, XMach-1, XMark and X007 --- on six XQuery processors, three stand-alone (file-based) XQuery processors (Galax, Qizx/Open, Saxon-B) and three XML/XQuery database systems (BerkeleyDB/XML, MonetDB/XQuery, X-Hive/DB). Next to assessing and comparing the functionality, performance and scalability for the various systems, the major focus of this work is to report in detail about the experiences made while performing such an exhaustive study, to discuss all the problems that we encountered and how we solved them, and hence to hopefully provide some guidelines (or even a recipe) for performing reproducible large-scale experime

Web and Semantic Web Query Languages

A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion

Analyzing Fuzzy Logic Computations with Fuzzy XPath

Implemented with a fuzzy logic language by using the FLOPER tool developed in our research group, we have recently designed a fuzzy dialect of the popular XPath language for the flexible manipulation of XML documents. In this paper we focus on the ability of Fuzzy XPath for exploring derivation trees generated by FLOPER once they are exported in XML format, which somehow serves as a debugging/analizing tool for discovering the set of fuzzy computed answers for a given goal, performing depth/breadth-first traversals of its associated derivation tree, finding non fully evaluated branches, etc., thus reinforcing the bi-lateral synergies between Fuzzy XPath and FLOPER

A Prototype for Translating XQuery Expressions into XSLT Stylesheets

Abstract. The need for a user-friendly query language becomes increasingly important since the introduction of XML. The W3C developed XQuery for the purpose of querying XML data, but XQuery is not available in every tool. Because of historical reasons, many tools only support processing XSLT stylesheets. It is desirable to use tools with XQuery, the design goals of which are, among other goals, to be more human readable and to be less error-prone than XSLT. Instead of implementing XQuery support for every tool, we propose to use an XQuery to XSLT translator. Following this idea, XQuery will be available for all tools, which currently support XSLT stylesheets. In this paper, we propose a translator which transforms XQuery expressions into XSLT stylesheets and we analyze the performance of the translation and XSLT processing in comparison to native XQuery processing

Razvoj proširenja xquery interpretera baziran na fazi logici sa prioritetima

In many real-world applications, information is often imprecise and uncertain. With the popularity of web-based applications, huge amounts of data are available on the web, and XML (eXtensible Markup Language) has become the de facto standard for data exchange over the internet. The XQuery is the language for querying XML data. However, XML and XQuery suffer from incapability of representing and manipulating imprecise and uncertain data. Consequently, this work represents fuzzy data in XML documents and extends XQuery language as providing a more flexible XQuery language by using the fuzzy set theory. In this thesis, an extension of the XQuery query, called Fuzzy XQuery is described. It allows users to define priority, threshold and fuzzy expressions in their queries. Users also can predefine linguistic terms to use them in querying. An algorithm for calculating the global constraint satisfaction degree using the Generalized Prioritized Fuzzy Constraint Satisfaction Problem (GPFCSP) is introduced. Furthermore, Fuzzy XQuery Interpreter (FXI) is implemented allowing execution of fuzzy XQuery queries based on open source technologies and native XML open- source database. Additionally, innovative methods for computing fuzzy set compatibility and introducing order over fuzzy sets have been implemented, which give serious improvements in computational performance compared to previous implementations

Extending XQuery for Semantic Web Reasoning

Abstract. In this paper we investigate an extension of the XQuery language for querying and reasoning with OWL-style ontologies. The proposed extension incorporates new primitives (i.e. boolean operators) in XQuery for the querying and reasoning with OWL-style triples in such a way that XQuery can be used as query language for the Semantic Web. In addition, we propose a Prolog-based implementation of the extension

An Empirical Evaluation of XQuery Processors

This paper presents an extensive and detailed experimental evaluation of XQuery processors. The study consists of running five publicly available XQuery benchmarks --- the Michigan benchmark (MBench), XBench, XMach-1, XMark and X007 --- on six XQuery processors, three stand-alone (file-based) XQuery processors (Galax, Qizx/Open, Saxon-B) and three XML/XQuery database systems (BerkeleyDB/XML, MonetDB/XQuery, X-Hive/DB). Next to assessing and comparing the functionality, performance and scalability for the various systems, the major focus of this work is to report in detail about the experiences made while performing such an exhaustive study, to discuss all the problems that we encountered and how we solved them, and hence to hopefully provide some guidelines (or even a recipe) for performing reproducible large-scale experimental research and system evaluation