Predictive Process Monitoring
22 papers with code • 0 benchmarks • 1 datasets
A branch of predictive analysis that attempts to predict some future state of a business process.
Benchmarks
These leaderboards are used to track progress in Predictive Process Monitoring
Ultra-processed Food Dataset
Latest papers with no code
Counterfactual Explanations for Predictive Business Process Monitoring
We thus see growing interest in explainable predictive business process monitoring, which aims to increase the interpretability of prediction models.
Explainable Predictive Process Monitoring: A User Evaluation
The results of the user evaluation show that, although explanation plots are overall understandable and useful for decision making tasks for Business Process Management users -- with and without experience in Machine Learning -- differences exist in the comprehension and usage of different plots, as well as in the way users with different Machine Learning expertise understand and use them.
ProcK: Machine Learning for Knowledge-Intensive Processes
We present a novel methodology to build powerful predictive process models.
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 cases, without the possibility of updating it with new cases when they complete their execution.
Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring
Often the training and test sets are not completely separated, a data leakage problem particular to predictive process monitoring.
Predictive Process Model Monitoring using Recurrent Neural Networks
To achieve this, Processes-As-Movies (PAM) is introduced, i. e., a novel technique capable of jointly mining and predicting declarative process constraints between activities in various windows of a process' execution.
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing
This study proposes an innovative explainable predictive quality analytics solution to facilitate data-driven decision-making for process planning in manufacturing by combining process mining, machine learning, and explainable artificial intelligence (XAI) methods.
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
Consequently, with regard to the theoretical and practical implications of the framework, this study proposes a novel local post-hoc explanation approach for a deep learning classifier that is expected to facilitate the domain experts in justifying the model decisions.
Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances
Predicting undesirable events during the execution of a business process instance provides the process participants with an opportunity to intervene and keep the process aligned with its goals.
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting.
