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2020 | OriginalPaper | Chapter

Dealing with Data Streams: Complex Event Processing vs. Data Stream Mining

Authors : Moritz Lange, Arne Koschel, Irina Astrova

Published in: Computational Science and Its Applications – ICCSA 2020

Publisher: Springer International Publishing

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Abstract

Recently, data generation rates are getting higher than ever before. Plenty of different sources like smartphones, social networking services and the Internet of Things (IoT) are continuously producing massive amounts of data. Due to limited resources, it is no longer feasible to persistently store all that data which leads to massive data streams. In order to meet the requirements of modern businesses, techniques have been developed to deal with these massive data streams. These include complex event processing (CEP) and data stream mining, which are covered in this article. Along with the development of these techniques, many terms and semantic overloads have occurred, making it difficult to clearly distinguish techniques for processing massive data streams. In this article, CEP and data stream mining are distinguished and compared to clarify terms and semantic overloads.

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next chapter Anomaly Detection for Data Streams Based on Isolation Forest Using Scikit-Multiflow
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Metadata
Title
Dealing with Data Streams: Complex Event Processing vs. Data Stream Mining
Authors
Moritz Lange
Arne Koschel
Irina Astrova
Copyright Year
2020
Book
Computational Science and Its Applications – ICCSA 2020

Print ISBN: 978-3-030-58810-6

Electronic ISBN: 978-3-030-58811-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-58811-3_1

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