Event stream-based process discovery using abstract representations

    Research output: Contribution to journalArticleAcademicpeer-review

    27 Citations (Scopus)
    143 Downloads (Pure)


    The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper, we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining toolkit ProM (http://promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.

    Original languageEnglish
    Pages (from-to)407-435
    Number of pages29
    JournalKnowledge and Information Systems
    Issue number2
    Publication statusPublished - 1 Feb 2018


    • Abstract representations
    • Event streams
    • Process discovery
    • Process mining

    Fingerprint Dive into the research topics of 'Event stream-based process discovery using abstract representations'. Together they form a unique fingerprint.

    Cite this