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Found 6 papers, 4 papers with code
Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring
To better model the time dependencies between events, we propose a new PBPM technique based on time-aware LSTM (T-LSTM) cells.
Prescriptive Business Process Monitoring for Recommending Next Best Actions
We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI.
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making.
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks
Through annotations with metrics such as the frequency or duration of activities, these models provide generic information to the process analyst.
Conformance checking: A state-of-the-art literature review
Conformance checking is a set of process mining functions that compare process instances with a given process model.
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.
