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dc.contributor.authorVerenich, Ien_US
dc.contributor.authorMõškovski, Sen_US
dc.contributor.authorRaboczi, Sen_US
dc.contributor.authorDumas, Men_US
dc.contributor.authorLa Rosa, Men_US
dc.contributor.authorMaggi, FMen_US
dc.date.available2018-04-11T02:37:44Z
dc.date.available2018-04-06en_US
dc.date.issued2018-06-15en_US
dc.identifier.citationVerenich, I; Mõškovski, S; Raboczi, S; Dumas, M; La Rosa, M; Maggi, FM, Predictive Process Monitoring in Apromore, Proceedings of the Forum and Doctoral Consortium Papers Presented at the 29th International Conference on Advanced Information Systems Engineering, CAiSE 2018, 2018en_US
dc.identifier.issn1865-1348en_US
dc.identifier.urihttp://hdl.handle.net/11343/210450
dc.description.abstractThis paper discusses the integration of Nirdizati, a tool for predictive process monitoring, into the Web-based process analytics platform Apromore. Through this integration, Apromore’s users can use event logs stored in the Apromore repository to train a range of predictive models, and later use the trained models to predict various performance indicators of running process cases from a live event stream. For example, one can predict the remaining time or the next events until case completion, the case outcome, or the violation of compliance rules or internal policies. The predictions can be presented graphically via a dashboard that offers multiple visualization options, including a range of summary statistics about ongoing and past process cases. They can also be exported into CSV for periodic reporting or to be visualized in third-parties business intelligence tools. Based on these predictions, operations managers may identify potential issues early on, and take remedial actions in a timely fashion, e.g. reallocating resources from one case onto another to avoid that the case runs overtime.
dc.publisherSpringer-Verlag, Journalsen_US
dc.sourceInternational Conference on Advanced Information Systems Engineering (CAiSE)en_US
dc.titlePredictive Process Monitoring in Apromoreen_US
dc.typeConference Proceeding
melbourne.affiliation.departmentComputing and Information Systems
melbourne.source.titleProceedings of the Forum and Doctoral Consortium Papers Presented at the 29th International Conference on Advanced Information Systems Engineering, CAiSE 2018en_US
melbourne.elementsid1311409
dc.identifier.orcid0000-0002-8782-2407
dc.identifier.orcid0000-0001-9568-4035
melbourne.internal.proprietaryauthorid806849A
melbourne.internal.proprietaryauthorid822254A
melbourne.contributor.authorLa Rosa, Marcello
melbourne.contributor.authorVerenich, Ilya
melbourne.contributor.orcidVerenich, I [0000-0002-8782-2407]
melbourne.contributor.orcidLa Rosa, M [0000-0001-9568-4035]
melbourne.accessrightsOpen Access


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