Process Mining 2.0: From Insights to Actions
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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.
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Process Mining 2.0: From Insights to Actions
- 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
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- Lost Make example of risk objectives
- https://www.if4it.com/core-domain-knowledge-critical-foundation-successful-design-thinking/ https://towardsdatascience.com/minimum-viable-domain-knowledge-in-data-science-5be7bc99eca9
- V. Leno, A. Polyvyanyy, M. La Rosa, M. Dumas and F. Maria Maggi. Action logger: Enabling process mining for robotic process automation. In Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019, 124–128, 2019 Available recording tools (e.g., WinParrot, JitBit) record low-level action only – clickstreams, keystrokes Although RPA tools (e.g., UI Path, Automation Anywhere) provide recording capabilities they are focused on manual programming of scripts. They do not record values of involved fields, do not capture timestamps, etc. In UI Path Studio, however, there is a component called UI Explorer, that is similar to our Action Logger, but it works only for Web (supports limited amount of actions), while our tool covers also Excel spreadsheet
- Use case inspired by a real-life scenario at the University of Melbourne
- V. Leno, A. Polyvyanyy, M. La Rosa, M. Dumas and F. Maria Maggi. Action logger: Enabling process mining for robotic process automation. In Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019, 124–128, 2019 Available recording tools (e.g., WinParrot, JitBit) record low-level action only – clickstreams, keystrokes Although RPA tools (e.g., UI Path, Automation Anywhere) provide recording capabilities they are focused on manual programming of scripts. They do not record values of involved fields, do not capture timestamps, etc. In UI Path Studio, however, there is a component called UI Explorer, that is similar to our Action Logger, but it works only for Web (supports limited amount of actions), while our tool covers also Excel spreadsheet
- https://www.if4it.com/core-domain-knowledge-critical-foundation-successful-design-thinking/ https://towardsdatascience.com/minimum-viable-domain-knowledge-in-data-science-5be7bc99eca9
- V. Leno, A. Polyvyanyy, M. La Rosa, M. Dumas and F. Maria Maggi. Action logger: Enabling process mining for robotic process automation. In Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019, 124–128, 2019 Available recording tools (e.g., WinParrot, JitBit) record low-level action only – clickstreams, keystrokes Although RPA tools (e.g., UI Path, Automation Anywhere) provide recording capabilities they are focused on manual programming of scripts. They do not record values of involved fields, do not capture timestamps, etc. In UI Path Studio, however, there is a component called UI Explorer, that is similar to our Action Logger, but it works only for Web (supports limited amount of actions), while our tool covers also Excel spreadsheet
- V. Leno, A. Polyvyanyy, M. La Rosa, M. Dumas and F. Maria Maggi. Action logger: Enabling process mining for robotic process automation. In Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019, 124–128, 2019 Available recording tools (e.g., WinParrot, JitBit) record low-level action only – clickstreams, keystrokes Although RPA tools (e.g., UI Path, Automation Anywhere) provide recording capabilities they are focused on manual programming of scripts. They do not record values of involved fields, do not capture timestamps, etc. In UI Path Studio, however, there is a component called UI Explorer, that is similar to our Action Logger, but it works only for Web (supports limited amount of actions), while our tool covers also Excel spreadsheet