Leveraging Anomaly Detection in Business Process with Data Stream Mining

dc.creatorTavares, Gabriel Marques
dc.creatorTurrisi da Costa, Victor Guilherme
dc.creatorMartins, Vinicius Eiji
dc.creatorCeravolo, Paolo
dc.creatorBarbon Jr., Sylvio
dc.date2019-04-17
dc.date.accessioned2023-06-16T20:46:05Z
dc.date.available2023-06-16T20:46:05Z
dc.identifierhttp://seer.unirio.br/isys/article/view/7877
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6686612
dc.descriptionIdentifying fraudulent or anomalous business procedures is today a key challenge for organisations of any dimension. Nonetheless, the continuous nature of business activities conveys to the continuous acquisition of data in support of business process monitoring. In light of this, we propose a method for online anomaly detection in business processes. From a stream of events, our approach extract cases descriptors and applies a density-based clustering technique to detect outliers. We applied our method to a real-life dataset, and we used streaming clustering measures for evaluating performances. Exploring different combinations of parameters, we obtained promising performance metrics, showing that our method is capable of finding anomalous process instances in a vast complexity of scenarios.en-US
dc.formatapplication/pdf
dc.languageeng
dc.publisherUniriopt-BR
dc.relationhttp://seer.unirio.br/isys/article/view/7877/7726
dc.rightsCopyright (c) 2019 Gabriel Marques Tavares, Victor Guilherme Turrisi da Costa, Vinicius Eiji Martins, Paolo Ceravolo, Sylvio Barbon Jr.pt-BR
dc.sourceiSys - Brazilian Journal of Information Systems; Vol. 12 No. 1 (2019); 54-75en-US
dc.sourceiSys - Brazilian Journal of Information Systems; v. 12 n. 1 (2019); 54-75pt-BR
dc.source1984-2902
dc.subjectProcess Miningen-US
dc.subjectBusiness Process Modellingen-US
dc.subjectOnlineen-US
dc.subjectFrauden-US
dc.subjectClusteringen-US
dc.titleLeveraging Anomaly Detection in Business Process with Data Stream Miningen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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