Comparison of Distributed Data-Parallelization Patterns for Big Data Analysis: A Bioinformatics Case Study

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dc.contributor.authorWang, Jianwu
dc.contributor.authorCrawl, Daniel
dc.contributor.authorAltintas, Ilkay
dc.contributor.authorTzoumas, Kostas
dc.contributor.authorMarkl, Volker
dc.date.accessioned2024-02-14T20:10:23Z
dc.date.available2024-02-14T20:10:23Z
dc.date.issued2013
dc.descriptionDataCloud’13, Nov. 17, 2013, Denver, CO, U.S.A
dc.description.abstractAs a distributed data-parallelization (DDP) pattern, MapReduce has been adopted by many new big data analysis tools to achieve good scalability and performance in Cluster or Cloud environments. This paper explores how two binary DDP patterns, i.e., CoGroup and Match, could also be used in these tools. We reimplemented an existing bioinformatics tool,called CloudBurst, with three different DDP pattern combinations. We identify two factors, namely, input data balancing and value sparseness, which could greatly affect the performances using different DDP patterns. Our experiments show: (i) a simple DDP pattern switch could speed up performance by almost two times; (ii) the identified factors can explain the differences well.
dc.description.sponsorshipThis work was supported by NSF ABI Award DBI-1062565 for bioKepler. The authors would like to thank the rest of bioKepler and Stratosphere teams for their collaboration. We also thank the FutureGrid project for experiment environment support.
dc.description.urihttps://users.sdsc.edu/~jianwu/JianwuWang_files/Comparison_of_Distributed_Data-Parallelization_Patterns_for_Big_Data_Analysis_A_Bioinformatics_Case_Study(2013).pdf
dc.format.extent5 pages
dc.genreconference papers and proceedings
dc.genrepresentations (communicative events)
dc.genrepreprints
dc.identifierdoi:10.13016/m217bs-e5vh
dc.identifier.urihttp://hdl.handle.net/11603/31627
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Center for Accelerated Real Time Analysis
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Data Science
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Center for Real-time Distributed Sensing and Autonomy
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectUMBC Big Data Analytics Lab
dc.titleComparison of Distributed Data-Parallelization Patterns for Big Data Analysis: A Bioinformatics Case Study
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170

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