Anomaly detection using process mining

F. Bezerra, J. Wainer, W.M.P. Aalst, van der

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

45 Citations (Scopus)

Abstract

Recently, several large companies have been involved in financial scandals related to mismanagement, resulting in financial damages for their stockholders. In response, certifications and manuals for best practices of governance were developed, and in some cases, tougher federal laws were implemented (e.g. the Sarboness Oxley Act). Companies adhered to these changes adopting the best practices for corporate governance by deploying Process Aware Information Systems (PAISs) to automate their business processes. However, these companies demand a rapid response to strategic changes, so the adoption of normative PAISs may compromise their competitiveness. On one hand companies need flexible PAISs for competitiveness reasons. On the other hand flexibility may compromise security of system because users can execute tasks that could result into violation of financial loses. In order to re-balance this trade-off, we present in this work how ProM tools can support anomaly detection in logs of PAIS. Besides, we present the results of the application of our approach with a real case.
Original languageEnglish
Title of host publicationEnterprise, Business-Process and Information Systems Modeling (10th International Workshop, BPMDS 2009, and 14th International Conference, EMMSAD 2009, held at CAiSE 2009, Amsterdam, The Netherlands, June 8-9, 2009. Proceedings)
EditorsT. Halpin, J. Krogstie, S. Nurcan, E. Proper, R. Schmidt, P. Soffer, R. Ukor
Place of PublicationBerlin
PublisherSpringer
Pages149-161
ISBN (Print)978-3-642-01861-9
DOIs
Publication statusPublished - 2009

Publication series

NameLecture Notes in Business Information Processing
Volume29
ISSN (Print)1865-1348

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