Towards a stream based machine learning testbed

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review


With the rise of data analytics in industrial applications a heterogeneous tool landscape developed over the past few years. To cope with highly dynamic and domain-specific requirements of such applications, scripting programming languages and frameworks, which offer ecosystems comprising numerous publicly available plugin libraries, are gaining more and more attraction as a starting point, since they enable rapid prototyping and thus, quick results. At the other end, cloud service providers are continuously extending their data analysis product palette in order to support large enterprise solutions for real-world deployments. As scripted prototypes and cloud based solutions have their strengths in different phases of an analytics project, we identified several pitfalls in recent case studies when moving a prototypic approach to release. In this work, we present a software design for a data stream analysis testbed, with the aim to address some of these challenges. Therefore, an interaction pattern for common analysis steps (data acquisition, visualization, preprocessing and machine learning model evaluation) is detailed, results gained from a sample case study are summarized and future leads are discussed.

Original languageEnglish
Title of host publication32nd European Modeling and Simulation Symposium, EMSS 2020
EditorsMichael Affenzeller, Agostino G. Bruzzone, Francesco Longo, Antonella Petrillo
Number of pages6
ISBN (Electronic)9788885741454
Publication statusPublished - 2020
Event32nd European Modeling and Simulation Symposium, EMSS 2020 - Virtual, Online
Duration: 16 Sept 202018 Sept 2020

Publication series

Name32nd European Modeling and Simulation Symposium, EMSS 2020


Conference32nd European Modeling and Simulation Symposium, EMSS 2020
CityVirtual, Online


  • Data Stream Analysis
  • Machine Learning
  • Predictive Maintenance
  • Software Framework


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