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DETEXA: declarative extensible text exploration and analysis through SQL.

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This study introduces a novel text analysis framework using extended SQL for digital libraries. The framework accelerates metadata enrichment and text mining, proving up to three times faster than existing methods.

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Area of Science:

  • Digital Libraries
  • Text Mining
  • Database Management Systems

Background:

  • Metadata enrichment is crucial for digital libraries, facing challenges from increasing open access publications.
  • Unstructured, large-scale, and heterogeneous data sources pose significant hurdles for traditional text mining.
  • Existing approaches often lack efficiency and integration for end-to-end text mining pipelines.

Purpose of the Study:

  • To introduce a scalable text analysis framework implemented in extended SQL.
  • To enable performant, end-to-end text mining pipelines, including data harvesting, cleaning, processing, and analysis.
  • To leverage the declarative nature of SQL for rapid experimentation and API development for domain experts.

Main Methods:

  • Developed a text analysis framework within extended SQL.
  • Utilized the scalability of modern database management systems.
  • Integrated data harvesting, cleaning, processing, and text analysis into a unified pipeline.

Main Results:

  • The proposed framework demonstrates significant speedup, achieving up to three times faster performance in common use cases.
  • Experimental analysis confirms the framework's effectiveness in handling large, unstructured datasets.
  • The SQL-based approach facilitates easier workflow modification and API integration.

Conclusions:

  • The extended SQL framework offers an effective and performant solution for metadata enrichment in digital libraries.
  • This approach addresses the challenges posed by the exponential growth of open access publications.
  • The system enables domain experts to manage text mining workflows efficiently through graphical interfaces.