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Cardinality-Aware and Purely Relational XQuery Processor

About Cardinality-Aware and Purely Relational XQuery Processor

XQuery is the standard XML query language, significa t effort has been made in developing efficient implementations of XQuery query processor.This thesis describes a purely relational implementation of an XQuery processor which exploits the well-known maturity and performance acceleration techniques of the relational database technology to translate XQuery expressions into their equivalent SQL evaluation scripts. The generated SQL evaluation scripts can be executed on any conventional relational database management systems with no need for any changes in its kernel or any other special requirements. In addition, this thesis presents a novel framework for estimating the cardinality of XQuery expressions as well as its sub-expressions. Although cardinality size estimation is very important on its own, it is also very crucial for an effective query optimization process. This thesis presents an integrated framework for exploiting the available estimated cardinality information to provide the RDBMS query optimizers with hints for selecting the best alternative execution plan for the SQL evaluation scripts of the input XQuery expression.

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  • Language:
  • English
  • ISBN:
  • 9783844388978
  • Binding:
  • Paperback
  • Pages:
  • 212
  • Published:
  • June 14, 2011
  • Dimensions:
  • 152x229x12 mm.
  • Weight:
  • 318 g.
Delivery: 1-2 weeks
Expected delivery: December 12, 2024

Description of Cardinality-Aware and Purely Relational XQuery Processor

XQuery is the standard XML query language, significa t effort has been made in developing efficient implementations of XQuery query processor.This thesis describes a purely relational implementation of an XQuery processor which exploits the well-known maturity and performance acceleration techniques of the relational database technology to translate XQuery expressions into their equivalent SQL evaluation scripts. The generated SQL evaluation scripts can be executed on any conventional relational database management systems with no need for any changes in its kernel or any other special requirements. In addition, this thesis presents a novel framework for estimating the cardinality of XQuery expressions as well as its sub-expressions. Although cardinality size estimation is very important on its own, it is also very crucial for an effective query optimization process. This thesis presents an integrated framework for exploiting the available estimated cardinality information to provide the RDBMS query optimizers with hints for selecting the best alternative execution plan for the SQL evaluation scripts of the input XQuery expression.

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