Systematic Mapping for Big Data Stream Processing Frameworks

dc.contributor.author Alayyoub,M.
dc.contributor.author Yazıcı, Ali
dc.contributor.author Yazici,A.
dc.contributor.author Karakaya,Z.
dc.contributor.author Karakaya, Ziya
dc.contributor.author Yazıcı, Ali
dc.contributor.author Karakaya, Ziya
dc.contributor.other Software Engineering
dc.contributor.other Computer Engineering
dc.contributor.other Software Engineering
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:44:36Z
dc.date.available 2024-07-05T15:44:36Z
dc.date.issued 2016
dc.description.abstract There has been lots of discussions about the choice of a stream processing framework (SPF) for Big Data. Each of the SPFs has different cutting edge technologies in their steps of processing the data in motion that gives them a better advantage over the others. Even though, the cutting edge technologies used in each stream processing framework might better them, it is still hard to say which framework bests the rest under different scenarios and conditions. In this study, we aim to show trends and differences about several SPFs for Big Data by using the Systematic Mapping (SM) approach. To achieve our objectives, we raise 6 research questions (RQs), in which 91 studies that conducted between 2010 and 2015 were evaluated. We present the trends by classifying the research on SPFs with respect to the proposed RQs which can help researchers to obtain an overview of the field. © 2016 IEEE. en_US
dc.identifier.doi 10.1109/ICDIM.2016.7829760
dc.identifier.isbn 978-150902640-1
dc.identifier.scopus 2-s2.0-85014331330
dc.identifier.uri https://doi.org/10.1109/ICDIM.2016.7829760
dc.identifier.uri https://hdl.handle.net/20.500.14411/3793
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 2016 11th International Conference on Digital Information Management, ICDIM 2016 -- 2016 11th International Conference on Digital Information Management, ICDIM 2016 -- 19 September 2016 through 21 September 2016 -- Porto -- 126084 en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Big Data en_US
dc.subject Flink en_US
dc.subject InfoSphere en_US
dc.subject S4 en_US
dc.subject Spark en_US
dc.subject Storm en_US
dc.subject Streaming en_US
dc.title Systematic Mapping for Big Data Stream Processing Frameworks en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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gdc.description.department Atılım University en_US
gdc.description.departmenttemp Alayyoub M., Atilim University, Institute of Natural and Applied Sciences, Ankara, Turkey; Yazici A., Atilim University, Faculty of Engineering, Ankara, Turkey; Karakaya Z., Atilim University, Faculty of Engineering, Ankara, Turkey en_US
gdc.description.endpage 36 en_US
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
gdc.description.startpage 31 en_US
gdc.identifier.openalex W2580250126
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gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
gdc.oaire.sciencefields 02 engineering and technology
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gdc.opencitations.count 1
gdc.plumx.crossrefcites 1
gdc.plumx.mendeley 15
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gdc.scopus.citedcount 2
gdc.virtual.author Yazıcı, Ali
gdc.virtual.author Karakaya, Ziya
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