The document discusses several topics in process mining research including predictive process monitoring, prescriptive process monitoring, robotic process mining, data-driven simulation, and causal process mining. It provides references for further research on each topic, with links to relevant papers that outline techniques in each area.

1. AI4EPT workshop @ PAKDD’2021

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22. Given
• one or more event logs recording the
execution of one or more processes
• one or more performance measures that we
seek to maximize/minimize
• a process model, decision rules and resource
allocation rules
• a set of allowed changes to the process
model and associated rules
Find
• Possible sets of changes to the process to
optimize the performance measures

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36. Discover
Process
Model
Metaheuristics
Optimizer
(e.g. Genetic,
Hill Climbing)
Candidate
Changeset
Evaluator
Candidate
Changeset
Generator
New Pareto
front
Event log
Candidate
Change-
sets
Discover
Simulation
Model
Simulation Model
As-Is Process
Model
Current
Pareto front
Business
Process
Simulator
Allowed
Changes

37. • Natural Language Processing (NLP) for BPM
• Henrik Leopold: “Natural Language in Business Process Models - Theoretical
Foundations, Techniques, and Applications”. Lecture Notes in Business Information
Processing 168, Springer 2013
• Rule mining from event logs
• RuM: https://rulemining.org/
• Causal process mining
• https://www.linkedin.com/pulse/causal-process-mining-marlon-dumas/
• Goal-based synthesis of processes
• …

38. References
Predictive Process Monitoring
• Teinemaa et al. Outcome-Oriented Predictive Process Monitoring: Review and Benchmark, 2019
https://arxiv.org/abs/1707.06766
• Verenich et al. Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in
Business Process Monitoring, 2019 https://arxiv.org/abs/1805.02896
• Rama-Maneiro et al. Deep Learning for Predictive Business Process Monitoring: Review and Benchmark.
2020 https://arxiv.org/abs/2009.13251
Prescriptive Process Monitoring
• Fahrenkrog-Petersen et al. Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring,
2019 https://arxiv.org/abs/1905.09568
• Metzger et al. Triggering Proactive Business Process Adaptations via Online Reinforcement Learning.
http://shorturl.at/bgtKO
Robotic Process Mining
• Leno et al. Robotic Process Mining: Vision and Challenges. Bus Inf Syst Eng (2020).
https://doi.org/10.1007/s12599-020-00641-4
• Leno et al. Automated Discovery of Data Transformations for Robotic Process Automation, 2020
https://arxiv.org/abs/2001.01007
• Agostinelli et al. Automated Generation of Executable RPA Scripts from User Interface Logs. Blockchain
and RPA Forum 2020

39. References
Data-Driven Simulation (discovering simulation models from logs)
• Camargo et al. Automated discovery of business process simulation models from event
logs. Decis. Support Syst. 134:113284, 2020 https://arxiv.org/abs/2009.03567
• Camargo et al. Discovering Generative Models from Event Logs: Data-driven Simulation vs
Deep Learning, 2020 https://arxiv.org/abs/2009.03567
Causal Process Mining
• Narendra, P. Agarwal, M. Gupta, and S. Dechu. Counterfactual reasoning for process
optimization using structural causal models. In Proceedings of the BPM Forum 2019. LNBIP,
vol. 360. Springer, 2019.
• Zahra Dasht Bozorgi, Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy.
Process Mining Meets Causal Machine Learning:Discovering Causal Rules from Event
Logs. In Proceedings of ICPM'2020.
https://arxiv.org/pdf/2009.01561.pdf