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dc.contributor.authorvan Zelst, SJen_US
dc.contributor.authorSani, MFen_US
dc.contributor.authorOstovar, Aen_US
dc.contributor.authorConforti, Ren_US
dc.contributor.authorLa Rosa, Men_US
dc.date.available2018-02-27T03:25:08Z
dc.date.available2018-02-23en_US
dc.date.available2018-02-23en_US
dc.date.available2018-02-23en_US
dc.date.issued2018-01-01en_US
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000460420000003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1cen_US
dc.identifier.citationvan Zelst, SJ; Sani, MF; Ostovar, A; Conforti, R; La Rosa, M, Filtering Spurious Events from Event Streams of Business Processes, ADVANCED INFORMATION SYSTEMS ENGINEERING, CAISE 2018, 2018, 10816 pp. 35 - 52en_US
dc.identifier.isbn978-3-319-91562-3en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/11343/198393
dc.description.abstractProcess mining aims at gaining insights into business processes by analysing event data recorded during process execution. The majority of existing process mining techniques works offline, i.e. using static, historical data stored in event logs. Recently, the notion of online process mining has emerged, whereby techniques are applied on live event streams, as process executions un- fold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviour. Hence, applying these techniques on real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to effectively filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results.
dc.languageEnglishen_US
dc.publisherSPRINGER INTERNATIONAL PUBLISHING AGen_US
dc.source30th International Conference on Advanced Information Systems Engineering (CAiSE)en_US
dc.titleFiltering Spurious Events from Event Streams of Business Processesen_US
dc.typeConference Proceeding
dc.identifier.doi10.1007/978-3-319-91563-0_3en_US
melbourne.affiliation.departmentComputing and Information Systems
melbourne.source.titleADVANCED INFORMATION SYSTEMS ENGINEERING, CAISE 2018en_US
melbourne.source.volume10816en_US
melbourne.source.pages35 - 52en_US
melbourne.elementsid1306461
pubs.publication-statusPublisheden_US
melbourne.contributor.authorLa Rosa, Marcello
melbourne.contributor.authorConforti, Raffaele
melbourne.contributor.orcidConforti, Raffaele [0000-0002-4980-3192]
melbourne.contributor.orcidLa Rosa, Marcello [0000-0001-9568-4035]
melbourne.accessrightsOpen Access


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