Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment

Long Cheng (Corresponding author), Boudewijn F. van Dongen, Wil M.P. van der Aalst

Research output: Contribution to journalArticleAcademicpeer-review

21 Citations (Scopus)
28 Downloads (Pure)

Abstract

Process descriptions are used to create products and deliver services. To lead better processes and services, the first step is to learn a process model. Process discovery is such a technique which can automatically extract process models from event logs. Although various discovery techniques have been proposed, they focus on either constructing formal models which are very powerful but complex, or creating informal models which are intuitive but lack semantics. In this work, we introduce a novel method that returns hybrid process models to bridge this gap. Moreover, to cope with today's big event logs, we propose an efficient method, called f-HMD, aims at scalable hybrid model discovery in a cloud computing environment. We present the detailed implementation of our approach over the Spark framework, and our experimental results demonstrate that the proposed method is efficient and scalable.

Original languageEnglish
Article number8669858
Pages (from-to)368-380
Number of pages13
JournalIEEE Transactions on Services Computing
Volume13
Issue number2
DOIs
Publication statusPublished - 1 Mar 2020

Funding

This work was supported by the NWO DeLiBiDa research program. Long Cheng thanks the support of the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 799066. Wil van der Aalst thanks the Alexander von Humboldt (AvH) Stiftung for supporting his research.

FundersFunder number
Horizon 2020 Framework Programme
Marie Skłodowska‐Curie799066
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

    Keywords

    • big data
    • cloud computing
    • event log
    • hybrid process model
    • Process discovery
    • service computing

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