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A rule-based recommendation approach for business process modeling
Sola, Diana
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Meilicke, Christian
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Aa, Han van der
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Stuckenschmidt, Heiner
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DOI:
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https://doi.org/10.1007/978-3-030-79382-1_20
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URL:
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https://link.springer.com/book/10.1007%2F978-3-030...
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Document Type:
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Conference or workshop publication
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Year of publication:
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2021
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Book title:
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Advanced information systems engineering : 33rd International Conference, CAiSE 2021, Melbourne, VIC, Australia, June 28 – July 2, 2021, proceedings
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The title of a journal, publication series:
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Lecture Notes in Computer Science
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Volume:
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12751
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Page range:
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328-343
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Conference title:
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CAISE 2021
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Location of the conference venue:
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Online
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Date of the conference:
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28.06.-02.07.2021
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Publisher:
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La Rosa, Marcello
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Sadiq, Shazia
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Teniente, Ernest
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Place of publication:
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Berlin [u.a.]
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Publishing house:
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Springer
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ISBN:
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978-3-030-79381-4 , 978-3-030-79382-1
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ISSN:
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0302-9743 , 1611-3349
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Subject:
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004 Computer science, internet
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Keywords (English):
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Process modeling , activity recommendation , rule learning
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Abstract:
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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.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Sola, Diana
;
Meilicke, Christian
;
Aa, Han van der
;
Stuckenschmidt, Heiner
Google Scholar:
Sola, Diana
;
Meilicke, Christian
;
Aa, Han van der
;
Stuckenschmidt, Heiner
ORCID:
Sola, Diana  ORCID: https://orcid.org/0000-0001-5688-1730, Meilicke, Christian ORCID: https://orcid.org/0000-0002-0198-5396, Aa, Han van der ORCID: https://orcid.org/0000-0002-4200-4937 and Stuckenschmidt, Heiner ORCID: https://orcid.org/0000-0002-0209-3859
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