Generating Counterfactual Explanations Under Temporal Constraints
Counterfactual explanations are one of the prominent eXplainable Artificial Intelligence (XAI) techniques, and suggest changes to input data that could alter predictions, leading to more...
Generating the Traces You Need: A Conditional Generative Model for Process Mining Data
In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing the features of...
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
Counterfactual explanations suggest what should be different in the input instance to change the outcome of an AI system. When dealing with counterfactual explanations in the field of Predictive...
Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns
Prescriptive Process Monitoring systems recommend, during the execution of a business process, interventions that, if followed, prevent a negative outcome of the process. Such interventions have...
Explainable Predictive Process Monitoring: A User Evaluation
Explainability is motivated by the lack of transparency of black-box Machine Learning approaches, which do not foster trust and acceptance of Machine Learning algorithms. This also happens in the...
How do I update my model? On the resilience of Predictive Process Monitoring models to change
Existing well investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions, and then use it to predict the future of new ongoing...
Learning to act: a Reinforcement Learning approach to recommend the best next activities
The rise of process data availability has recently led to the development of data-driven learning approaches. However, most of these approaches restrict the use of the learned model to predict the...
Nirdizati: an Advanced Predictive Process Monitoring Toolkit
Predictive Process Monitoring is a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded...
Computing unsatisfiable cores for LTLf specifications
Linear-time temporal logic on finite traces (LTLf) is rapidly becoming a de-facto standard to produce specifications in many application domains (e.g., planning, business process management,...
Exploring Business Process Deviance with Sequential and Declarative Patterns
Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to {their} expected or desirable...
Predictive Process Monitoring Methods: Which One Suits Me Best?
Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate...
Process discovery on deviant traces and other stranger things
As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two...
What is a Process Model Composed of? A Systematic Literature Review of Meta-Models in BPM
Business process modelling languages typically enable the representation of business process models by employing (graphical) symbols. These symbols can vary depending upon the verbosity of the...
Solving reachability problems on data-aware workflows
Recent advances in the field of Business Process Management have brought about several suites able to model complex data objects along with the traditional control flow perspective. Nonetheless,...
What's My Process Model Composed of? A Systematic Literature Review of Meta-Models in BPM
Business process modelling languages typically enable the representation of business process models by employing (graphical) symbols. These symbols can vary depending upon the verbosity of the...
Clustering-Based Predictive Process Monitoring
Business process enactment is generally supported by information systems that record data about process executions, which can be extracted as event logs. Predictive process monitoring is concerned...
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new...
Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence
Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal...