Search Results for author:
Found 4 papers, 2 papers with code
A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences
The results show improvements up to four times compared to the state of the art in suffix and remaining time prediction of event sequences, specifically in the realm of business process executions.
Encoder-Decoder Generative Adversarial Nets for Suffix Generation and Remaining Time Prediction of Business Process Models
This paper proposes an encoder-decoder architecture grounded on Generative Adversarial Networks (GANs), that generates a sequence of activities and their timestamps in an end-to-end way.
Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction
Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp.
Business Process Variant Analysis based on Mutual Fingerprints of Event Logs
The cornerstone of this approach is a technique to learn a directly follows graph called mutual fingerprint from the event logs of the two variants.
