Constraint-based Query Distribution Framework for an Integrated Global Schema

Distributed heterogeneous data sources need to be queried uniformly using global schema. Query on global schema is reformulated so that it can be executed on local data sources. Constraints in global schema and mappings are used for source selection, query optimization,and querying partitioned and replicated data sources. The provided system is all XML-based which poses query in XML form, transforms, and integrates local results in an XML document. Contributions include the use of constraints in our existing global schema which help in source selection and query optimization, and a global query distribution framework for querying distributed heterogeneous data sources.Comment: The Proceedings of the 13th INMIC 2009), Dec. 14-15, 2009, Islamabad, Pakistan. Pages 1 - 6 Print ISBN: 978-1-4244-4872-2 INSPEC Accession Number: 11072575 Date of Current Version : 15 January 201

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

AMaχoS—Abstract Machine for Xcerpt

Web query languages promise convenient and efficient access to Web data such as XML, RDF, or Topic Maps. Xcerpt is one such Web query language with strong emphasis on novel high-level constructs for effective and convenient query authoring, particularly tailored to versatile access to data in different Web formats such as XML or RDF. However, so far it lacks an efficient implementation to supplement the convenient language features. AMaχoS is an abstract machine implementation for Xcerpt that aims at efficiency and ease of deployment. It strictly separates compilation and execution of queries: Queries are compiled once to abstract machine code that consists in (1) a code segment with instructions for evaluating each rule and (2) a hint segment that provides the abstract machine with optimization hints derived by the query compilation. This article summarizes the motivation and principles behind AMaχoS and discusses how its current architecture realizes these principles

Optimizing XML Compression in XQueC

We present our approach to the problem of optimizing compression choices in the context of the XQueC compressed XML database system. In XQueC, data items are aggregated into containers, which are further grouped to be compressed together. This way, XQueC is able to exploit data commonalities and to perform query evaluation in the compressed domain, with the aim of improving both compression and querying performance. However, different compression algorithms have different performance and support different sets of operations in the compressed domain. Therefore, choosing how to group containers and which compression algorithm to apply to each group is a challenging issue. We address this problem through an appropriate cost model and a suitable blend of heuristics which, based on a given query workload, are capable of driving appropriate compression choices

Development of Use Cases, Part I

For determining requirements and constructs appropriate for a Web query language, or in fact any language, use cases are of essence. The W3C has published two sets of use cases for XML and RDF query languages. In this article, solutions for these use cases are presented using Xcerpt. a novel Web and Semantic Web query language that combines access to standard Web data such as XML documents with access to Semantic Web metadata such as RDF resource descriptions with reasoning abilities and rules familiar from logicprogramming. To the best knowledge of the authors, this is the first in depth study of how to solve use cases for accessing XML and RDF in a single language: Integrated access to data and metadata has been recognized by industry and academia as one of the key challenges in data processing for the next decade. This article is a contribution towards addressing this challenge by demonstrating along practical and recognized use cases the usefulness of reasoning abilities, rules, and semistructured query languages for accessing both data (XML) and metadata (RDF)

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author