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Systematic mapping for big data stream processing frameworks

机译:大数据流处理框架的系统映射

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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.
机译:关于为大数据选择流处理框架(SPF)的讨论很多。每个SPF在处理运动数据的步骤中都具有不同的前沿技术,这使它们比其他SPF具有更好的优势。即使在每个流处理框架中使用的最先进的技术可能会使其更胜一筹,但仍然很难说哪个框架在不同的场景和条件下能使其余的框架最佳。在本研究中,我们旨在通过使用系统映射(SM)方法来显示几种大数据SPF的趋势和差异。为了实现我们的目标,我们提出了6个研究问题(RQ),其中对2010年至2015年进行的91项研究进行了评估。通过对SPF的研究相对于建议的RQ进行分类,我们可以提供趋势,这可以帮助研究人员获得该领域的概述。

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