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omniClassifier: a Desktop Grid Computing System for Big Data Prediction Modeling.

John H Phan¹, Sonal Kothari¹, May D Wang¹

¹Department of Biomedical, Engineering, Georgia Institute of, Technology and Emory University, Atlanta, GA, USA, 30332.

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|August 18, 2016

Summary

This summary is machine-generated.

OmniClassifier offers a solution for complex prediction modeling in science and engineering. This desktop grid computing application efficiently handles Big Data challenges, enabling robust machine learning research.

Keywords:

Prediction modeling big data desktop grid computing nested cross validation

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Area of Science:

Computational science
Bioinformatics
Machine learning

Background:

Robust prediction models are crucial for science, engineering, and biomedical fields.
Optimizing these models is computationally complex, especially with Big Data.
Existing computational methods struggle to keep pace with data growth.

Purpose of the Study:

To develop a computationally efficient and scalable prediction modeling application.
To provide researchers with a tool for machine learning research adhering to best practices.
To leverage desktop grid computing for enhanced access to computational resources.

Main Methods:

Developed omniClassifier, a multi-purpose prediction modeling application.
Implemented omniClassifier using the Berkeley Open Infrastructure for Network Computing (BOINC) middleware.
Utilized a desktop grid computing approach combining idle desktop resources and cloud computing.

Main Results:

Demonstrated the scalability of omniClassifier using gene expression datasets.
Showcased omniClassifier's capability for efficient and robust Big Data prediction modeling.
Provided a prototype accessible for research use.

Conclusions:

OmniClassifier effectively addresses computational challenges in Big Data prediction modeling.
Desktop grid computing offers a cost-effective solution for accessing vast computational resources.
The application supports best-practice machine learning research in data-intensive fields.