890 research outputs found
VAMANA : A High Performance, Scalable and Cost Driven XPath Engine
Many applications are migrating or beginning to make use native XML data. We anticipate that queries will emerge that emphasize the structural semantics of XML query languages like XPath and XQuery. This brings a need for an efficient query engine and database management system tailored for XML data similar to traditional relational engines. While mapping large XML documents into relational database systems while possible, poses difficulty in mapping XML queries to the less powerful relational query language SQL and creates a data model mismatch between relational tables and semi-structured XML data. Hence native solutions to efficiently store and query XML data are being developed recently. However, most of these systems thus far fail to demonstrate scalability with large document sizes, to provide robust support for the XPath query language nor to adequately address costing with respect to query optimization. In this thesis, we propose a novel cost-driven XPath engine to support the scalable evaluation of ad-hoc XPath expressions called VAMANA. VAMANA makes use of an efficient XML repository for storing and indexing large XML documents called the Multi-Axis Storage Structure (MASS) developed at WPI. VAMANA extensively uses indexes for query evaluation by considering index-only plans. To the best of our knowledge, it is the only XML query engine that supports an index plan approach for large XML documents. Our index-oriented query plans allow queries to be evaluated while reading only a fraction of the data, as all tuples for a particular context node are clustered together. The pipelined query framework minimizes the cost of handing intermediate data during query processing. Unlike other native solutions, VAMANA provides support for all 13 XPath axes. Our schema independent cost model provides dynamically calculated statistics that are then used for intelligent cost-based transformations, further improving performance. Our optimization strategy for increasing execution time performance is affirmed through our experimental studies on XMark benchmark data. VAMANA query execution is significantly faster than leading available XML query engines
Relational Approach to Logical Query Optimization of XPath
To be able to handle the ever growing volumes of XML documents, effective and efficient data management solutions are needed. Managing XML data in a relational DBMS has great potential. Recently, effective relational storage schemes and index structures have been proposed as well as special-purpose join operators to speed up querying of XML data using XPath/XQuery. In this paper, we address the topic of query plan construction and logical query optimization. The claim of this paper is that standard relational algebra extended with special-purpose join operators suffices for logical query optimization. We focus on the XPath accelerator storage scheme and associated staircase join operators, but the approach can be generalized easily
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
Pathfinder: XQuery - The Relational Way
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
On Region Algebras, XML Databases, and Information Retrieval
This paper describes some new ideas on developing a logical algebra for databases that manage textual data and support information retrieval functionality. We describe a first prototype of such a system
06472 Abstracts Collection - XQuery Implementation Paradigms
From 19.11.2006 to 22.11.2006, the Dagstuhl Seminar 06472 ``XQuery Implementation Paradigms'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available
A Database Approach to Content-based XML retrieval
This paper describes a rst prototype system for content-based retrieval from XML data. The system's design supports both XPath queries and complex information retrieval queries based on a language modelling approach to information retrieval. Evaluation using the INEX benchmark shows that it is beneficial if the system is biased to retrieve large XML fragments over small fragments
Staircase Join: Teach a Relational DBMS to Watch its (Axis) Steps
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 operates on an XML encoding which makes this knowledge available at the cost of simple integer operations (e.g., +, <=). We finally report on quite promising experiments with a staircase join enhanced main-memory database kernel
XML Reconstruction View Selection in XML Databases: Complexity Analysis and Approximation Scheme
Query evaluation in an XML database requires reconstructing XML subtrees
rooted at nodes found by an XML query. Since XML subtree reconstruction can be
expensive, one approach to improve query response time is to use reconstruction
views - materialized XML subtrees of an XML document, whose nodes are
frequently accessed by XML queries. For this approach to be efficient, the
principal requirement is a framework for view selection. In this work, we are
the first to formalize and study the problem of XML reconstruction view
selection. The input is a tree , in which every node has a size
and profit , and the size limitation . The target is to find a subset
of subtrees rooted at nodes respectively such that
, and is maximal.
Furthermore, there is no overlap between any two subtrees selected in the
solution. We prove that this problem is NP-hard and present a fully
polynomial-time approximation scheme (FPTAS) as a solution
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