Applying machine learning to big data streams : An overview of challenges

Christoph Augenstein , Norman Spangenberg , Bogdan Franczyk

Abstract

The importance of processing stream data increases with new technologies and new use cases. Applying machine learning to stream data and process them in real time leads to challenges in different ways. Model changes, concept drift or insufficient time to train models are a few examples. We illustrate big data characteristics and machine learning techniques derived from literature and conclude with available approaches and drawbacks
Author Christoph Augenstein
Christoph Augenstein,,
-
, Norman Spangenberg
Norman Spangenberg,,
-
, Bogdan Franczyk (MISaF / IBI / DISD)
Bogdan Franczyk,,
- Department of Information Systems Design
Pages25-29
Publication size in sheets0.5
Book 4th International Conference on Soft Computing and Machine Intelligence (ISCMI), 2017, IEEE , ISBN 9781538613146, [9781538613139], 205 p.
DOIDOI:10.1109/ISCMI.2017.8279592
URL http://ieeexplore.ieee.org/document/8279592/
Languageen angielski
Score (nominal)15
ScoreMinisterial score = 15.0, 02-07-2019, ChapterFromConference
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