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Related Experiment Videos

Solutions for complex, multi data type and multi tool analysis: principles and applications of using workflow and

Robin E J Munro¹, Yike Guo

¹InforSense Ltd., London, UK.

Methods in Molecular Biology (Clifton, N.J.)

|July 15, 2009

Summary

This summary is machine-generated.

Analytical workflow technology, also known as data pipelining, enables rapid development and deployment of analytical applications. This approach facilitates data integration and analysis across diverse sources without coding, supporting scientific data challenges.

Related Experiment Videos

Area of Science:

Computer Science
Bioinformatics
Data Science

Background:

Analytical workflow technology, or data pipelining, is crucial for scalable analytical middleware.
It enables the rapid creation and deployment of analytical applications for researchers and analysts.

Purpose of the Study:

To describe an 'Embedded Analytics' methodology using analytical workflow technology.
To demonstrate its application in solving diverse scientific data analysis problems.

Main Methods:

Utilizing analytical workflow technology for data integration and tool access.
Visually constructing analytical workflows without coding for application development.
Deploying workflows as web services within a service-oriented architecture (SOA).

Main Results:

Facilitates integration of structured and non-structured data across information silos.
Enables composition of multiple analytical methods and data transformations.
Supports rapid application development with easy revision and domain-specific extensions (e.g., genetics, patient analytics).

Conclusions:

Analytical workflow technology simplifies the design and publication of data analysis processes as web services.
The 'Embedded Analytics' methodology offers a powerful approach for scientific data analysis challenges.