University Library
  • Login
A gateway to Melbourne's research publications
Minerva Access is the University's Institutional Repository. It aims to collect, preserve, and showcase the intellectual output of staff and students of the University of Melbourne for a global audience.
View Item 
  • Minerva Access
  • Engineering and Information Technology
  • Computing and Information Systems
  • Computing and Information Systems - Research Publications
  • View Item
  • Minerva Access
  • Engineering and Information Technology
  • Computing and Information Systems
  • Computing and Information Systems - Research Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

    Filtering Spurious Events from Event Streams of Business Processes

    Thumbnail
    Download
    Submitted version (595.0Kb)

    Citations
    Scopus
    Web of Science
    Altmetric
    9
    5
    Author
    van Zelst, SJ; Sani, MF; Ostovar, A; Conforti, R; La Rosa, M
    Editor
    Krogstie, J; Reijers, HA
    Date
    2018-06-04
    Source Title
    ADVANCED INFORMATION SYSTEMS ENGINEERING, CAISE 2018
    Publisher
    Springer
    University of Melbourne Author/s
    La Rosa, Marcello; Conforti, Raffaele
    Affiliation
    Computing and Information Systems
    Metadata
    Show full item record
    Document Type
    Conference Paper
    Citations
    van 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.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/198393
    DOI
    10.1007/978-3-319-91563-0_3
    Abstract
    Process 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.

    Export Reference in RIS Format     

    Endnote

    • Click on "Export Reference in RIS Format" and choose "open with... Endnote".

    Refworks

    • Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References


    Collections
    • Minerva Elements Records [53039]
    • Computing and Information Systems - Research Publications [1580]
    Minerva AccessDepositing Your Work (for University of Melbourne Staff and Students)NewsFAQs

    BrowseCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    My AccountLoginRegister
    StatisticsMost Popular ItemsStatistics by CountryMost Popular Authors