Data Mining and Knowledge Discovery
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Early detection of insider trading in option markets
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
An Empirical Bayes Approach to Detect Anomalies in Dynamic Multidimensional Arrays
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Netprobe: a fast and scalable system for fraud detection in online auction networks
Proceedings of the 16th international conference on World Wide Web
Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance
Electronic Notes in Theoretical Computer Science (ENTCS)
A workflow mining method through model rewriting
CRIWG'05 Proceedings of the 11th international conference on Groupware: design, Implementation, and Use
Fraud detection in process aware systems
Companion Proceedings of the XIV Brazilian Symposium on Multimedia and the Web
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Today, there is a variety of systems that support business process as a whole or partially. However, normative systems are not appropriate for some domains (e.g. health care) as a flexible support is needed to the participants. On the other hand, while it is important to support flexibility in these systems, security requirements can not be met whether these systems do not offer extra control. This paper presents and assesses two anomaly detection algorithms in logs of Process Aware Systems (PAS). The detection of an anomalous trace is based on the "noise" which a trace makes in a process model discovered by a process mining algorithm. This paper argues that these methods can support the coexistence of security and flexibility when aggregated to a PAS.