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

1. Predictive Process Monitoring in Camunda
Nico Bartmann
Stefan Hill

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Speakers
Nico Bartmann
Stefan Hill

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Gitlab
https://gitlab.uni-koblenz.de/fg-bks/camunda-ppm-hpo
Forschungsgruppe Delfman
Institut für Wirtschafts- und Verwaltungsinformatik

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Agenda
 BPM
 Introduction to Machine Learning
 Live Demo & Hands-on
 Key Features
 Q&A

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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
?
?

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Introduction to Machine Learning
Artificial
Intelligenc
e
Machine
Learning
Deep
Learning
Process information Prediction
Model

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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

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Introduction to Machine Learning
Decision
tree
Regressi
on
Clusterin
g
Statistical
analysis
Neural
Network

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Architecture – shown on an evaluation example
Frontend
Request
Handler
ML
Algorithm
Train()
Eval()
Serialize()
History
Running
Dataset
Cammunda
Service
Datareader
Classification

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Key Features
 Direct integration of ML algorithms into Camunda
 Versatile solution for PPM
 Powerful visualization
 Low entry barrier

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Advertisement – Hyper Parameter Optimizer
CAiSE’21
28 JUNE 2021 – 2 JULY 2021
https://caise21.org/