DocumentCode :
3186186
Title :
Cloudflow - A framework for MapReduce pipeline development in Biomedical Research
Author :
Forer, Lukas ; Afgan, Enis ; Weissensteiner, Hansi ; Davidovic, Davor ; Specht, Gunther ; Kronenberg, Florian ; Schonherr, Sebastian
Author_Institution :
Div. of Genetic Epidemiology, Med. Univ. of Innsbruck, Innsbruck, Austria
fYear :
2015
fDate :
25-29 May 2015
Firstpage :
172
Lastpage :
177
Abstract :
The data-driven parallelization framework Hadoop MapReduce allows analysing large data sets in a scalable way. Since the development of MapReduce programs can be a time-intensive and challenging task, the application and usage of Hadoop in Biomedical Research is still limited. Here we present Cloudflow, a high-level framework to hide the implementation details of Hadoop and to provide a set of building blocks to create biomedical pipelines in a more intuitive way. We demonstrate the benefit of Cloudflow on three different genetic use cases. It will be shown how the framework can be combined with the Hadoop workflow system Cloudgene and the cloud orchestration platform CloudMan to provide Hadoop pipelines as a service to everyone.
Keywords :
cloud computing; data handling; medical computing; parallel processing; CloudMan; Cloudflow; Cloudgene; Hadoop MapReduce; biomedical research; cloud orchestration platform; data-driven parallelization framework; genetic use cases; high-level framework; pipeline development; workflow system; Bioinformatics; Genomics; Information filters; Pipeline processing; Pipelines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2015 38th International Convention on
Conference_Location :
Opatija
Type :
conf
DOI :
10.1109/MIPRO.2015.7160259
Filename :
7160259
Link To Document :
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