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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:有关为大数据的流处理框架(SPF)的选择有很多讨论。每个SPF都具有不同的切削边缘技术,其步骤在他们以运动中处理数据的步骤,使其能够更好地优于其他方式。即使,每个流处理框架中使用的切削刃技术可能会更好,仍然很难说哪个框架在不同的场景和条件下最好的框架。在这项研究中,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SPFS的趋势和差异。为实现我们的目标,我们提出了6项研究问题(RQS),其中评估了2010年和2015年之间进行的91项研究。我们通过对拟议的RQ分类对SPF的研究进行分类,提出了趋势,这可以帮助研究人员获得该领域的概述。

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