Anomaly Detection Algorithms In Business Process Logs

dc.creatorBezerra F.
dc.creatorWainer J.
dc.date2008
dc.date2015-06-30T19:20:41Z
dc.date2015-11-26T14:42:38Z
dc.date2015-06-30T19:20:41Z
dc.date2015-11-26T14:42:38Z
dc.date.accessioned2018-03-28T21:50:21Z
dc.date.available2018-03-28T21:50:21Z
dc.identifier9789898111388; 9789898111371
dc.identifierIceis 2008 - Proceedings Of The 10th International Conference On Enterprise Information Systems. , v. AIDSS, n. , p. 11 - 18, 2008.
dc.identifier
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-55349121225&partnerID=40&md5=6e8cc419a325b3fa61e0c53b826c16f9
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/105846
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/105846
dc.identifier2-s2.0-55349121225
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1251357
dc.descriptionIn some domains of application, like software development and health care processes, a normative business process system (e.g. workflow management system) is not appropriate because a flexible support is needed to the participants. On the other hand, while it is important to support flexibility of execution in these domains, security requirements can not be met whether these systems do not offer extra control, which characterizes a trade off between flexibility and security in such domains. This work presents and assesses a set of anomaly detection algorithms in logs of Process Aware Systems (PAS). The detection of an anomalous instance is based on the "noise" which an instance makes in a process model discovered by a process mining algorithm. As a result, a trace that is an anomaly for a discovered model will require more structural changes for this model fit it than a trace that is not an anomaly. Hence, when aggregated to PAS, these methods can support the coexistence of security and flexibility.
dc.descriptionAIDSS
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dc.description11
dc.description18
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dc.languageen
dc.publisher
dc.relationICEIS 2008 - Proceedings of the 10th International Conference on Enterprise Information Systems
dc.rightsfechado
dc.sourceScopus
dc.titleAnomaly Detection Algorithms In Business Process Logs
dc.typeActas de congresos


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