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Related Experiment Video

Updated: Apr 28, 2026

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CloudDOE: a user-friendly tool for deploying Hadoop clouds and analyzing high-throughput sequencing data with

Wei-Chun Chung1, Chien-Chih Chen2, Jan-Ming Ho3

  • 1Institute of Information Science, Academia Sinica, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan.

Plos One
|June 5, 2014
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Summary
This summary is machine-generated.

CloudDOE simplifies deploying Hadoop clouds for bioinformatics. This user-friendly tool reduces complexity and cost for big data analysis in genomics.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Next-generation sequencing generates massive datasets, posing computational challenges.
  • Cloud computing offers scalable solutions for large-scale data analysis.
  • Hadoop/MapReduce is valuable for bioinformatics tasks but challenging to deploy.

Purpose of the Study:

  • To present CloudDOE, a user-friendly platform for deploying Hadoop clouds.
  • To simplify the deployment and management of Hadoop MapReduce for bioinformatics applications.
  • To enable scientists to leverage cloud computing for big data analysis without complex procedures.

Main Methods:

  • CloudDOE is a platform-independent Java software package.
  • It features a graphical user interface with three wizards: Deploy, Operate, and Extend.
  • The Deploy wizard automates Hadoop cloud setup, while Operate and Extend manage applications.

Main Results:

  • CloudDOE provides a user-friendly interface for deploying Hadoop clouds in-house or on public clouds.
  • It supports bioinformatics applications like CloudBurst, CloudBrush, and CloudRS.
  • The wizards streamline deployment, execution, and management of MapReduce jobs.

Conclusions:

  • CloudDOE significantly reduces the complexity and cost of Hadoop cloud deployment and management.
  • It empowers scientists to perform big data analysis more efficiently.
  • Future development can incorporate more bioinformatics tools and support open-source big data technologies.