Systematic Mapping for Big Data Stream Processing Frameworks

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Date

2016

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Institute of Electrical and Electronics Engineers Inc.

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Green Open Access

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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. © 2016 IEEE.

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Keywords

Big Data, Flink, InfoSphere, S4, Spark, Storm, Streaming

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Fields of Science

0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

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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

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31

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36

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Scopus : 2

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