Show simple item record

dc.contributor.authorvan Zelst, SJ
dc.contributor.authorSani, MF
dc.contributor.authorOstovar, A
dc.contributor.authorConforti, R
dc.contributor.authorLa Rosa, M
dc.contributor.editorKrogstie, J
dc.contributor.editorReijers, HA
dc.date.available2018-02-27T03:25:08Z
dc.date.available2018-02-23
dc.date.available2018-02-23
dc.date.available2018-02-23
dc.date.issued2018-06-04
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000460420000003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationvan Zelst, S. J., Sani, M. F., Ostovar, A., Conforti, R. & La Rosa, M. (2018). Filtering Spurious Events from Event Streams of Business Processes. Krogstie, J (Ed.) Reijers, HA (Ed.) ADVANCED INFORMATION SYSTEMS ENGINEERING, CAISE 2018, 10816, pp.35-52. Springer. https://doi.org/10.1007/978-3-319-91563-0_3.
dc.identifier.isbn978-3-319-91562-3
dc.identifier.issn0302-9743
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.languageEnglish
dc.publisherSpringer
dc.source30th International Conference on Advanced Information Systems Engineering
dc.titleFiltering Spurious Events from Event Streams of Business Processes
dc.typeConference Paper
dc.identifier.doi10.1007/978-3-319-91563-0_3
melbourne.affiliation.departmentComputing and Information Systems
melbourne.source.titleADVANCED INFORMATION SYSTEMS ENGINEERING, CAISE 2018
melbourne.source.volume10816
melbourne.source.pages35-52
melbourne.elementsid1306461
melbourne.contributor.authorLa Rosa, Marcello
melbourne.contributor.authorConforti, Raffaele
dc.identifier.eissn1611-3349
pubs.acceptance.date2018-02-23
melbourne.event.locationTallinn, Estonia
melbourne.accessrightsOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record