12 research outputs found

    Pathfinder: XQuery - The Relational Way

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    Relational query processors are probably the best understood (as well as the best engineered) query engines available today. Although carefully tuned to process instances of the relational model (tables of tuples), these processors can also provide a foundation for the evaluation of "alien" (non-relational) query languages: if a relational encoding of the alien data model and its associated query language is given, the RDBMS may act like a special-purpose processor for the new language

    Overview of query optimization in XML database systems

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    Reasoning & Querying ā€“ State of the Art

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    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

    Let a Single FLWOR Bloom

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    To globally optimize execution plans for XQuery expressions, a plan generator must generate and compare plan alternatives. In proven compiler architectures, the unit of plan generation is the query block. Fewer query blocks mean a larger search space for the plan generator and lead to a generally higher quality of the execution plans. The goal of this paper is to provide a toolkit for developers of XQuery evaluators to transform XQuery expressions into expressions with as few query blocks as possible. Our toolkit takes the form of rewrite rules merging the inner and outer FLWOR expressions into single FLWORs. We focus on previously unpublished rewrite rules and on inner FLWORs occurring in the For, Let, and Return clauses in the outer FLWOR

    Web and Semantic Web Query Languages

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    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

    Level-based indexing for optimising XML queries

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    Many of the problems with native XML databases relate to query performance and subsequently, it can be difficult to convince traditional database users of the benefits of using semi- or unstructured databases. In particular, the ongoing development of the XQuery language requires that performance related issues are resolved. Presently, there still lacks an index structure providing efficient support for both navigational and structural queries and the traditional data-centric and content queries. This thesis presents a new extended index structure based on the preorder traversal rank and the level (or depth) rank of each node in a document tree. The extended index fully supports the navigation of all XPath axes while efficiently supporting data-centric queries. The ability to start path traversals from arbitrary nodes in a document tree also enables the extended index to support the evaluation of path traversals embedded in XQuery expressions. Furthermore, an encoding technique for this extended index structure is presented, whereby properties of a level ranking may be exploited to provide efficient and optimised path traversals and in certain cases, optimal solutions to path traversals

    Automaton Meet Algebra: A Hybrid Paradigm for Efficiently Processing XQuery over XML Stream

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    XML stream applications bring the challenge of efficiently processing queries on sequentially accessible token-based data streams. The automaton paradigm is naturally suited for pattern retrieval on tokenized XML streams, but requires patches for implementing the filtering or restructuring functionalities common for the XML query languages. In contrast, the algebraic paradigm is well-established for processing self-contained tuples. However, it does not traditionally support token inputs. This dissertation proposes a framework called Raindrop, which accommodates both the automaton and algebra paradigms to take advantage of both. First, we propose an architecture for Raindrop. Raindrop is an algebra framework that models queries at different abstraction levels. We represent the token-based automaton computations as an algebraic subplan at the high level while exposing the automaton details at the low level. The algebraic subplan modeling automaton computations can thus be integrated with the algebraic subplan modeling the non-automaton computations. Second, we explore a novel optimization opportunity. Other XML stream processing systems always retrieve all the patterns in a query in the automaton. In contrast, Raindrop allows a plan to retrieve some of the pattern retrieval in the automaton and some out of the automaton. This opens up an automaton-in-or-out optimization opportunity. We study this optimization in two types of run-time environments, one with stable data characteristics and one with fluctuating data characteristics. We provide search strategies catering to each environment. We also describe how to migrate from a currently running plan to a new plan at run-time. Third, we optimize the automaton computations using the schema knowledge. A set of criteria are established to decide what schema constraints are useful to a given query. Optimization rules utilizing different types of schema constraints are proposed based on the criteria. We design a rule application algorithm which ensures both completeness (i.e., no optimization is missed) and minimality (i.e., no redundant optimization is introduced). The experimentations on both real and synthetic data illustrate that these techniques bring significant performance improvement with little overhead

    Aspects of Record Linkage

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    This thesis is an exploration of the subject of historical record linkage. The general goal of historical record linkage is to discover relations between historical entities in a database, for any specific definition of relation, entity and database. Although this task originates from historical research, multiple disciplines are involved. Increasing volumes of data necessitate the use of automated or semi-automated linkage procedures, which is in the domain of computer science. Linkage methodologies depend heavily on the nature of the data itself, often requiring analysis based on onomastics (i.e., the study of person names) or general linguistics. To understand the dynamics of natural language one could be tempted to look at the source of language, i.e., humans, either on the individual cognitive level or as group behaviour. This further increases the multidisciplinarity of the subject by including cognitive psychology. Every discipline addresses a subset of problem aspects, all of which can contribute either to practical solutions for linkage problems or to further insights into the subject matter.Algorithms and the Foundations of Software technolog
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