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A Taxonomy and Survey of Stream Processing Systems


Zhao, X and Garg, S and Queiroz, C and Buyya, R, A Taxonomy and Survey of Stream Processing Systems, Software Architecture for Big Data and the Cloud, Morgan Kaufmann, I Mistrik, R Bahsoon, N Ali, M Heisel, B Maxim (ed), Burlington, United States, pp. 177-200. ISBN 978-0-12-805467-3 (2017) [Research Book Chapter]

Copyright Statement

Copyright 2017 Elsevier Inc.

DOI: doi:10.1016/B978-0-12-805467-3.00011-9


In the era of big data, an unprecedented amount of data is generated every second. The real time analytics has become a force for transforming organizations which are looking for increasing their consumer base and profit. Therefore, the real time stream processing systems have gained a lot of attention, particularly within social media companies such as Twitter and LinkedIn. To identify the open challenges in the area of stream processing and facilitate future advancements, it is essential to synthesize and categorize current stream processing systems. In this chapter, we propose a taxonomy that characterizes and classifies various stream systems. Based on the taxonomy we present a survey and comparison study of the state-of-the-art open source stream computing platforms. The taxonomy and survey is intended to help researchers by providing insights into capabilities of existing stream platforms and businesses by providing criteria that can be leveraged to identify the most suitable stream processing solution that can be adopted for developing their domain-specific applications.

Item Details

Item Type:Research Book Chapter
Keywords:cloud computing, stream computing
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Distributed systems and algorithms
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Zhao, X (Mr Xinliang Zhao)
UTAS Author:Garg, S (Dr Saurabh Garg)
ID Code:117417
Year Published:2017
Deposited By:Information and Communication Technology
Deposited On:2017-06-13
Last Modified:2019-09-23

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