Rogge-Solti, Andreas, Kasneci, Gjergji. 2014. Temporal Anomaly Detection in Business Processes. In Business Process Management, Hrsg. Shazia Sadiq, Pnina Soffer, Hagen Völzer, 234-249. Haifa, Israel: Springer Lecture Notes in Computer Science (LNCS).

RIS

@INPROCEEDINGS{Solti2014,
title = {Temporal Anomaly Detection in Business Processes},
author = {Andreas Solti and Gjergji Kasneci},
publisher = {Springer Lecture Notes in Computer Science (LNCS)},
year = {2014},
isbn = {978-3-319-10171-2},
address = {Haifa, Israel},
url = {http://link.springer.com/chapter/10.1007/978-3-319-10172-9_15},
publisher = {Springer Lecture Notes in Computer Science (LNCS)},
language = {EN},
pages = {234-249},
series = {Business Process Management},
abstract = {The analysis of business processes is often challenging not only because of intricate dependencies between process activities but also because of various sources of faults within the activities. The automated detection of potential business process anomalies could immensely help business analysts and other process participants detect and understand the causes of process errors. This work focuses on temporal anomalies, i.e., anomalies concerning the runtime of activities within a process. To detect such anomalies, we propose a Bayesian model that can be automatically inferred form the Petri net representation of a business process. Probabilistic inference on the above model allows the detection of non-obvious and interdependent temporal anomalies.},
}