Big Data Applications Using Workflows for Data Parallel Computing

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https://ieeexplore.ieee.org/document/6799151

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https://doi.org/10.1109/MCSE.2014.50
 http://hdl.handle.net/11603/31619

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UMBC Information Systems Department
UMBC Center for Accelerated Real Time Analysis
UMBC Center for Real-time Distributed Sensing and Autonomy
UMBC Computer Science and Electrical Engineering Department
UMBC Data Science
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Author/Creator ORCID

https://orcid.org/0000-0002-9933-1170

Date

2014-04-16

Type of Work

journal articles
postprints
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Citation of Original Publication

J. Wang, D. Crawl, I. Altintas and W. Li, "Big Data Applications Using Workflows for Data Parallel Computing," in Computing in Science & Engineering, vol. 16, no. 4, pp. 11-21, July-Aug. 2014, doi: 10.1109/MCSE.2014.50.

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© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Subjects

UMBC Big Data Analytics Lab

Abstract

In the Big Data era, workflow systems need to embrace data parallel computing techniques for efficient data analysis and analytics. Here, the authors present an easy-to-use, scalable approach to build and execute Big Data applications using actor-oriented modeling in data parallel computing. They use two bioinformatics use cases for next-generation sequencing data analysis to verify the feasibility of their approach.