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Partitioning XML data, towards distributed and parallel management

Noor Malla 1, 2
2 OAK - Database optimizations and architectures for complex large data
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : With the widespread diffusion of XML as a format for representing data generated and exchanged over the Web, main query and update engines have been designed and implemented in the last decade. A kind of engines that are playing a crucial role in many applications are main-memory systems, which distinguish for the fact that they are easy to manage and to integrate in a programming environment. On the other hand, main-memory systems have scalability issues, as they load the entire document in main-memory before processing. This Thesis presents an XML partitioning technique that allows main-memory engines to process a class of XQuery expressions (queries and updates), that we dub iterative, on arbitrarily large input documents. We provide a static analysis technique to recognize these expressions. The static analysis is based on paths extracted from the expression and does not need additional schema information. We provide algorithms using path information for partitioning the input documents, so that the query or update can be separately evaluated on each part in order to compute the final result. These algorithms admit a streaming implementation, whose effectiveness is experimentally validated. Besides enabling scalability, our approach is also characterized by the fact that it is easily implementable into a MapReduce framework, thus enabling parallel query/update evaluation on the partitioned data.
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Contributor : Dario Colazzo <>
Submitted on : Friday, November 30, 2012 - 10:51:38 AM
Last modification on : Monday, December 9, 2019 - 5:24:07 PM
Document(s) archivé(s) le : Friday, March 1, 2013 - 3:46:37 AM


  • HAL Id : tel-00759173, version 1


Noor Malla. Partitioning XML data, towards distributed and parallel management. Databases [cs.DB]. Université Paris Sud - Paris XI, 2012. English. ⟨tel-00759173v1⟩



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