ENBIS-22 Trondheim Conference

Session

CONTRIBUTED Process 1

27 Jun 2022, 14:40

EL1

Conveners

CONTRIBUTED Process 1

• Bjarne Bergquist (Luleå University of Technology)

7. Distributed Statistical Process Monitoring based on Causal Network Decomposition

Marco P. Seabra dos Reis (Department of Chemical Engineering, University of Coimbra)

27/06/2022, 14:40

Process

As data collection systems grow in size, multivariate Statistical Process Monitoring (SPM) methods begin to experiment difficulties to detect localized faults, the occurrence of which is masked by the background noise of the process associated to the many sources of unstructured variability. Moreover, these methods are primarily non-causal and do not consider or take advantage of the...

19. Monitoring time to event in registry data using CUSUMs based on excess risk models

Jan Terje Kvaløy (University of Stavanger)

27/06/2022, 15:00

Process

In health registries, like cancer registries, patient outcomes are registered over time. It is then often of interest to monitor whether the distribution of the time to an event of interest changes over time – for instance if the survival time of cancer patients changes over time. A common challenge in monitoring survival times based on registry data is that time to death, but not cause of...

82. Adding Covariables and Learning Rules to GAN for Process Units' Suffix Predictions

Yoann Valero (Your Data Consulting)

27/06/2022, 15:20

Process

A unit circulating in a business process is characterized by a unique identifier, a sequence of activities, and timestamps to record the time and date at which said activities have started. This triplet constitutes an individual journey. The aim of predictive Business Process Monitoring is to predict the next or remaining activities of an ongoing journey, and/or its remaining time, be it until...