Predictive Process Monitoring Made Easy

Intermediate/Advanced Track

Stefan and Nico will present a plugin that equips Camunda with machine learning techniques for predictive process monitoring. Features include:

  • Display activity, time and risk prediction in the Cockpit view
  • Training, version control and parametrization of ML algorithms
  • Ensemble Learning – Easy expandability in means of predictions types, algorithms
  • Automated hyperparameter optimization
  
Resources

Speaker

StefanHill

Stefan Hill, University of Koblenz-Landau
Stefan studies MSc. Computer Science at University of Koblenz-Landau. While having a background in web development, his current interests include machine learning, interdisciplinary research and data science. At present, he conducts an internship at the European Space Agency doing multispectral image processing. When he does not code, he either grows his Urban Jungle or refines his scrambling egg cooking skills.

NicoBartmann

Nico Bartmann, University of Koblenz-Landau
Nico studies MSc. Computer Science at University of Koblenz-Landau. His goal is to use artificial intelligence to find innovative approaches to previously unsolvable problems and to implement them in practical applications. Currently, he is optimizing the railway wheel manufacturing processes with time series predictions. While his code is crispy crunchy his pork belly ist not yet.