Systematic mapping for big data stream processing frameworks

Mohammed Alayyoub, A. Yazici, Z. Karakaya
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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.
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大数据流处理框架的系统映射
关于为大数据选择一个流处理框架(SPF)已经有了很多讨论。每个spf在处理动态数据的步骤中都有不同的尖端技术,这使它们比其他spf具有更好的优势。尽管每个流处理框架中使用的尖端技术可能比它们更好,但在不同的场景和条件下,仍然很难说哪个框架比其他框架更好。在本研究中,我们的目标是通过使用系统映射(SM)方法来显示大数据的几个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(RQs),其中评估了2010年至2015年间进行的91项研究。我们通过对SPFs研究的分类来呈现趋势,这可以帮助研究人员获得该领域的概述。
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