Systematic Mapping for Big Data Stream Processing Frameworks

被引:0
作者
Alayyoub, Mohammed [1 ]
Yazici, Ali [2 ]
Karakaya, Ziya [2 ]
机构
[1] Atilim Univ, Inst Nat & Appl Sci, Ankara, Turkey
[2] Atilim Univ, Fac Engn, Ankara, Turkey
来源
2016 ELEVENTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT (ICDIM 2016) | 2016年
关键词
Big Data; Streaming; Storm; Flink; Spark; S4; InfoSphere;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:31 / 36
页数:6
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