Predictive Monitoring of Business Processes

Abstract

Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business goals during business process execution. At any point during an execution of a process, the user can define business goals in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business goal. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business goals. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital.

Cited by in corpus (27)

• Predictive Business Process Monitoring with LSTM Neural Networks
• Predictive Process Monitoring Methods: Which One Suits Me Best?
• A systematic literature review on state-of-the-art deep learning methods for process prediction
• Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
• Temporal Stability in Predictive Process Monitoring
• Prescriptive Business Process Monitoring for Recommending Next Best Actions
• Privacy and Confidentiality in Process Mining -- Threats and Research Challenges
• How do I update my model? On the resilience of Predictive Process Monitoring models to change
• XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
• Counterfactual Explanations for Predictive Business Process Monitoring
• Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances
• An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs
• Learning to act: a Reinforcement Learning approach to recommend the best next activities
• Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring
• Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring
• Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
• Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
• A Systematic Literature Review on Process-Aware Recommender Systems
• Explainable Artificial Intelligence for Improved Modeling of Processes
• Addressing Cognitive Biases in Augmented Business Decision Systems
• Extending predictive process monitoring for collaborative processes
• What Automated Planning can do for Business Process Management
• Specification-Driven Multi-Perspective Predictive Business Process Monitoring (Extended Version)
• An empirical investigation of different classifiers, encoding and ensemble schemes for next event prediction using business process event logs
• A User Evaluation of Automated Process Discovery Algorithms
• Alarm-Based Prescriptive Process Monitoring
• Predictive Process Model Monitoring using Recurrent Neural Networks