A rule-based recommendation approach for business process modeling

Sola, Diana ; Meilicke, Christian ; Aa, Han van der ; Stuckenschmidt, Heiner

DOI: https://doi.org/10.1007/978-3-030-79382-1_20
URL: https://link.springer.com/book/10.1007%2F978-3-030...
Document Type: Conference or workshop publication
Year of publication: 2021
Book title: Advanced information systems engineering : 33rd International Conference, CAiSE 2021, Melbourne, VIC, Australia, June 28 – July 2, 2021, proceedings
The title of a journal, publication series: Lecture Notes in Computer Science
Volume: 12751
Page range: 328-343
Conference title: CAISE 2021
Location of the conference venue: Online
Date of the conference: 28.06.-02.07.2021
Publisher: La Rosa, Marcello ; Sadiq, Shazia ; Teniente, Ernest
Place of publication: Berlin [u.a.]
Publishing house: Springer
ISBN: 978-3-030-79381-4 , 978-3-030-79382-1
ISSN: 0302-9743 , 1611-3349
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): Process modeling , activity recommendation , rule learning
Abstract: Business process modeling is a crucial, yet time-consuming and knowledge-intensive task. This is particularly the case when modeling a domain-specific process, which often requires the use of highly specialized terminology in a consistent manner. To alleviate these issues, the process modeling task can be supported by techniques that suggest how a model under development can be expanded. In this work, we provide such suggestions through a rule-based activity recommendation approach, which suggests suitable activities to be included at a user-defined position in a process model. A benefit of our rule-based work over other approaches is that it accompanies recommendations with explanations, providing additional transparency and trustworthiness to users. Furthermore, through comprehensive evaluation experiments on a large set of real-world process models, we show that our rule-based approach outperforms other methods, including an embedding-based one.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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