Authors:
Theo Zschörnig
1
;
Jonah Windolph
1
;
Robert Wehlitz
1
and
Bogdan Franczyk
2
Affiliations:
1
Institute for Applied Informatics (InfAI), Goerdelerring 9, 04109 Leipzig, Germany
;
2
Information Systems Institute, Leipzig University, Grimmaische Str. 12, 04109 Leipzig, Germany, Business Informatics Institute, Wrocaw University of Economics, ul. Komandorska 118-120, 53-345 Wrocaw, Poland
Keyword(s):
Internet of Things, Stream Processing, Online Machine Learning, Kappa Architecture.
Abstract:
The increasing number of smart devices in private households has lead to a large quantity of smart homes worldwide. In order to gain meaningful insights into their generated data and offer extended information and added value for consumers, data analytics architectures are essential. In addition, the development and improvement of machine learning techniques and algorithms in the past years has lead to the availability of powerful analytics tools, which have the potential to allow even more sophisticated insights at the cost of changed challenges and requierements for analytics architectures. However, architectural solutions, which offer the ability to deploy flexible, machine learning-based analytics pipelines on streaming data, are missing in research as well as in industry. In this paper, we present the motivation and a concept for machine learning-based data processing on streaming data for consumer-centric Internet of Things domains, such as smart home. This approach was evaluat
ed in terms of its performance and may serve as a basis for further development and discussion.
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