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Predictive Process Monitoring in Camunda

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Predictive Process Monitoring in Camunda

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Learn about 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

Learn about 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

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Predictive Process Monitoring in Camunda

  1. 1. Predictive Process Monitoring in Camunda Nico Bartmann Stefan Hill
  2. 2. 2 Speakers Nico Bartmann Stefan Hill
  3. 3. 3 Gitlab https://gitlab.uni-koblenz.de/fg-bks/camunda-ppm-hpo Forschungsgruppe Delfman Institut für Wirtschafts- und Verwaltungsinformatik
  4. 4. 4 Agenda  BPM  Introduction to Machine Learning  Live Demo & Hands-on  Key Features  Q&A
  5. 5. 5 BPM Variables  Timestamp  Blood pressure  Heart rate treat with blue pill treat with red pill treat with placebo refill oil ignore mileage scrap car check car medical examination easy to integrate Variables  Timestamp  Mileage  Oil level ? ?
  6. 6. 6 Introduction to Machine Learning Artificial Intelligenc e Machine Learning Deep Learning Process information Prediction Model
  7. 7. 7 Introduction to Machine Learning Is this good? And how do we know if it is? Accuracy = 12/13 = 92,3% MAE = Mean Absolute Error= 3.8 Metrics! d
  8. 8. 8 Introduction to Machine Learning Decision tree Regressi on Clusterin g Statistical analysis Neural Network
  9. 9. 9
  10. 10. 10 Architecture – shown on an evaluation example Frontend Request Handler ML Algorithm Train() Eval() Serialize() History Running Dataset Cammunda Service Datareader Classification
  11. 11. 11 Key Features  Direct integration of ML algorithms into Camunda  Versatile solution for PPM  Powerful visualization  Low entry barrier
  12. 12. 12 Advertisement – Hyper Parameter Optimizer CAiSE’21 28 JUNE 2021 – 2 JULY 2021 https://caise21.org/

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