Predictive process monitoring: concepts, challenges, and future research directions
Abstract Predictive Process Monitoring (PPM) extends classical process mining techniques
by providing predictive models that can be applied at runtime during the execution of a …
by providing predictive models that can be applied at runtime during the execution of a …
Uncovering patterns for local explanations in outcome-based predictive process monitoring
Abstract Explainable Predictive Process Monitoring aims at deriving explanations of the
inner workings of black-box classifiers used to predict the continuation of ongoing process …
inner workings of black-box classifiers used to predict the continuation of ongoing process …
Validation set sampling strategies for predictive process monitoring
Previous studies investigating the efficacy of long short-term memory (LSTM) recurrent
neural networks in predictive process monitoring and their ability to capture the underlying …
neural networks in predictive process monitoring and their ability to capture the underlying …
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 …
Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing …
Nirdizati: an advanced predictive process monitoring toolkit
Abstract Predictive Process Monitoring (PPM) is a field of Process Mining that aims at
predicting how an ongoing execution of a business process will develop in the future using …
predicting how an ongoing execution of a business process will develop in the future using …
Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes
In recent years, various machine and deep learning architectures have been successfully
introduced to the field of predictive process analytics. Nevertheless, the inherent opacity of …
introduced to the field of predictive process analytics. Nevertheless, the inherent opacity 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 …
change the outcome of an AI system. When dealing with counterfactual explanations in the …
Uncovering the Hidden Significance of Activities Location in Predictive Process Monitoring
M Vazifehdoostirani, M Abbaspour Onari, I Grau… - … Conference on Process …, 2023 - Springer
Predictive process monitoring methods predict ongoing case outcomes by analyzing
historical process data. Recent studies highlighted the increasing need to enhance the …
historical process data. Recent studies highlighted the increasing need to enhance the …
Novel Conformance Checking Methods and Validation Strategies for Deep Learning in Process Mining
J Peeperkorn, J De Weerdt - 2023 - lirias.kuleuven.be
This research project focusses on the development of representation learning-based
techniques for business processes. More specifically, both the architectural design as well …
techniques for business processes. More specifically, both the architectural design as well …
Nirdizati Light: A Modular Framework for Explainable Predictive Process Monitoring
A Buliga, R Graziosi, C Di Francescomarino… - Proceedings of the Best …, 2024 - bia.unibz.it
Nirdizati Light is an innovative Python package designed for Explainable Predictive Process
Monitoring (XPPM). It addresses the need for a modular, flexible tool to compare predictive …
Monitoring (XPPM). It addresses the need for a modular, flexible tool to compare predictive …