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Processing SPARQL queries with regular expressions in RDF databases.

Jinsoo Lee1, Minh-Duc Pham, Jihwan Lee

  • 1Department of Computer Engineering, Kyungpook National University, Daegu, Korea.

BMC Bioinformatics
|April 15, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient framework for SPARQL queries with regular expressions in RDF databases, significantly improving performance for bioinformatics knowledge bases.

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Area of Science:

  • Bioinformatics
  • Semantic Web Technologies
  • Database Querying

Background:

  • Resource Description Framework (RDF) is crucial for bioinformatics data sharing.
  • SPARQL is the standard query language for RDF databases.
  • Efficient regular expression processing in SPARQL is lacking for complex bioinformatics queries.

Purpose of the Study:

  • To develop a novel framework for efficient regular expression processing in SPARQL queries.
  • To integrate a cost model for query optimizers.
  • To demonstrate the framework's effectiveness and efficiency.

Main Methods:

  • Proposed a new framework for SPARQL regular expression processing.
  • Developed a cost model for query optimization.
  • Implemented a C++ prototype for experimental validation.

Main Results:

  • The proposed framework efficiently processes SPARQL queries with regular expressions.
  • Experimental results demonstrate significant performance improvements.
  • The cost model effectively integrates into existing query optimizers.

Conclusions:

  • The novel framework substantially enhances SPARQL query performance for RDF databases.
  • Outperforms existing methods by up to two orders of magnitude.
  • Provides an effective solution for complex bioinformatics data retrieval.