Identifying fraudulent or anomalous business procedures is today a key challenge for organisations of any dimension. Nevertheless, the continuous nature of business 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. In particular, we obtained Cluster Mapping Measure of 95.3% and Homogeneity of 98.1% discovering anomalous cases in real-time.

Anomaly Detection in Business Process based on Data Stream Mining / G.M. Tavares, V.G.T. da Costa, V.E. Martins, P. Ceravolo, S. Barbon (INTERNATIONAL WORLD WIDE WEB CONFERENCE). - In: SBSI'18 Proceedings of the XIV Brazilian Symposium on Information Systems[s.l] : Association for Computing Machinery (ACM), 2018 Jun 04. - ISBN 9781450365598. - pp. 1-8 (( convegno Brazilian Symposium on Information Systems tenutosi a Caxias do Sul nel 2018 [10.1145/3229345.3229362].

Anomaly Detection in Business Process based on Data Stream Mining

P. Ceravolo;
2018-06-04

Abstract

Identifying fraudulent or anomalous business procedures is today a key challenge for organisations of any dimension. Nevertheless, the continuous nature of business 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. In particular, we obtained Cluster Mapping Measure of 95.3% and Homogeneity of 98.1% discovering anomalous cases in real-time.
Process Mining; Business Process Modelling; Online; Fraud; Clustering
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/609357
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