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Towards a rule-based recommendation approach for business process modeling
Sola, Diana
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DOI:
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https://doi.org/10.1007/978-3-030-76352-7_4
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URL:
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https://link.springer.com/chapter/10.1007/978-3-03...
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2021
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Buchtitel:
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Service-oriented computing – ICSOC 2020 Workshops : AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Dubai, United Arab Emirates, December 14-17, 2020 : proceedings
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Titel einer Zeitschrift oder einer Reihe:
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Lecture Notes in Computer Science
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Band/Volume:
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12632
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Seitenbereich:
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25-31
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Veranstaltungstitel:
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ICSOC 2020
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Veranstaltungsort:
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online
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Veranstaltungsdatum:
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14.12. - 17.12. 2020
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Herausgeber:
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Hacid, Hakim
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Ort der Veröffentlichung:
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Berlin [u.a.]
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Verlag:
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Springer
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ISBN:
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978-3-030-76351-0 , 3-030-76351-X, 978-3-030-76352-7, 3-030-76352-8
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ISSN:
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0302-9743 , 1611-3349
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Fachgebiet:
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004 Informatik
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Abstract:
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Business process modeling can be time-consuming and error-prone, especially for inexperienced users. For this reason, graphical editors for business process modeling should support users by providing suggestions on how to complete a currently developed business process model. We address this problem with a rule-based activity recommendation approach, which suggests suitable activities to extend the business process model that is currently edited at a user-defined position. Contrary to alternative approaches, rules provide an additional explanation for the recommendation, which can be useful in cases where a user might be torn between two alternatives. We plan to investigate how rule learning can be efficiently designed for the given problem setting and how a rule-based approach performs compared to alternative methods. In this paper we describe the basic idea, a first implementation and first results.
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