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Dynamic integration of biological data sources using the data concierge.

Peng Gong1

  • 1Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, the University of Sydney, Sydney, NSW 2006 Australia ; Department of PET and Nuclear Medicine, RPA Hospital, Camperdown, NSW 2050 Australia.

Health Information Science and Systems
|April 1, 2015
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Summary
This summary is machine-generated.

The Data Concierge middleware dynamically integrates diverse bioinformatics data sources at runtime. This adaptive system significantly reduces costs and development time for incorporating new biological data, enhancing research flexibility.

Keywords:
BiologyData integrationMiddlewareOntologyState machine

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Integration

Background:

  • Dynamic integration of bioinformatics sources is crucial for evolving network environments and biological technologies.
  • Current integration methods are static and struggle with new or changing data sources and requirements.

Purpose of the Study:

  • To propose a novel semantics-based adaptive middleware, the Data Concierge, for dynamic integration of heterogeneous biological data sources.
  • To enable runtime integration without requiring traditional wrappers.

Main Methods:

  • Developed the Data Concierge middleware with an architecture for dynamic integration.
  • Introduced an API description mechanism for classifying, recognizing, locating, and invoking new data source functionalities.
  • Utilized XML-based state machines and unified semantic metadata for flexible configuration of complex operations.

Main Results:

  • The Data Concierge demonstrated dynamic integration capabilities, with reasonable performance trade-offs in knowledge model reasoning and API invocation.
  • Significantly lower overall costs were observed for integrating new biological data sources compared to existing methods.

Conclusions:

  • The Data Concierge facilitates rapid integration of new biological data sources into existing applications.
  • This approach offers a cost-effective solution, minimizing repetitive software development and labor-intensive tasks.