Abstract is missing.
- Everything there is to Know about Stochastically Known LogsAvigdor Gal. [doi]
- Discovery and Simulation of Business Processes with Probabilistic Resource Availability CalendarsOrlenys López-Pintado, Marlon Dumas. 1-8 [doi]
- Runtime Integration of Machine Learning and Simulation for Business ProcessesFrancesca Meneghello, Chiara Di Francescomarino, Chiara Ghidini. 9-16 [doi]
- Manifold Learning for Adversarial Robustness in Predictive Process MonitoringAlexander Stevens, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt. 17-24 [doi]
- Data-Driven Goal Recognition in Transhumeral Prostheses Using Process Mining TechniquesZihang Su, Tianshi Yu, Nir Lipovetzky, Alireza Mohammadi 0002, Denny Oetomo, Artem Polyvyanyy, Sebastian Sardiña, Ying Tan 0001, Nick van Beest. 25-32 [doi]
- Plan Recognition as Probabilistic Trace AlignmentJonghyeon Ko, Fabrizio Maria Maggi, Marco Montali, Rafael Peñaloza, Ramon Fraga Pereira. 33-40 [doi]
- Repairing Soundness Properties in Data-Aware ProcessesPaolo Felli, Marco Montali, Sarah Winkler. 41-48 [doi]
- SKTR: Trace Recovery from Stochastically Known LogsEli Bogdanov, Izack Cohen, Avigdor Gal. 49-56 [doi]
- A Window of Opportunity: Active Window Tracking for Mining Work PracticesIris Beerepoot, Daniël Barenholz, Stijn Beekhuis, Jens Gulden, Suhwan Lee, Xixi Lu 0001, Sietse Overbeek, Inge van de Weerd, Jan Martijn E. M. van der Werf, Hajo A. Reijers. 57-64 [doi]
- Addressing the Log Representativeness Problem using Species DiscoveryMartin Kabierski, Markus Richter, Matthias Weidlich 0001. 65-72 [doi]
- State Snapshot Process Discovery on Career Paths of Qing Dynasty Civil ServantsAdam T. Burke, Sander J. J. Leemans, Moe Thandar Wynn, Cameron D. Campbell. 73-80 [doi]
- Discovering Object-Centric Process Simulation ModelsBenedikt Knopp, Mahsa Pourbafrani, Wil M. P. van der Aalst. 81-88 [doi]
- Scalable Discovery of Partially Ordered Workflow Models with Formal GuaranteesHumam Kourani, Daniel Schuster 0001, Wil M. P. van der Aalst. 89-96 [doi]
- A Fresh Approach to Analyze Process OutcomesHagen Völzer, Francesca Zerbato, Timothy Sulzer, Barbara Weber. 97-104 [doi]
- Measuring the Stability of Process Outcome Predictions in Online SettingsSuhwan Lee, Marco Comuzzi, Xixi Lu 0001, Hajo A. Reijers. 105-112 [doi]
- Business Process Deviation Prediction: Predicting Non-Conforming Process BehaviorMichael Grohs, Peter Pfeiffer, Jana-Rebecca Rehse. 113-120 [doi]
- Enhancing the Applicability of the eST-Miner: Efficient Precision-Guided Implicit Place AvoidanceFelix C. Groß, Lisa Luise Mannel, Wil M. P. van der Aalst. 121-128 [doi]
- An Approximate Inductive MinerJan Niklas van Detten, Pol Schumacher, Sander J. J. Leemans. 129-136 [doi]