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Found 12 papers, 0 papers with code
Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review
This paper presents a systematic literature review (SLR) on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining, using the PRISMA framework.
A Discussion on Generalization in Next-Activity Prediction
Next activity prediction aims to forecast the future behavior of running process instances.
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research.
Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring
As data-driven intelligent systems advance, the need for reliable and transparent decision-making mechanisms has become increasingly important.
Towards Automated Process Planning and Mining
AI Planning, Machine Learning and Process Mining have so far developed into separate research fields.
Discrete models of continuous behavior of collective adaptive systems
Artificial ants are "small" units, moving autonomously on a shared, dynamically changing "space", directly or indirectly exchanging some kind of information.
Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks
Learning meaningful representations of data is an important aspect of machine learning and has recently been successfully applied to many domains like language understanding or computer vision.
A systematic literature review on state-of-the-art deep learning methods for process prediction
Additionally, the set of log-data, evaluation metrics and baselines used by the authors diverge, making the results hard to compare.
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.
XES Tensorflow - Process Prediction using the Tensorflow Deep-Learning Framework
This demo paper focuses on the software implementation and describes the architecture and user interface.
Predicting Process Behaviour using Deep Learning
Predicting business process behaviour is an important aspect of business process management.
