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Deductive reconstruction of MLT* for multi-level modeling
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Informationssystem, Information Systems)ORCID iD: 0000-0002-9421-8566
Federal University of Espírito Santo, Vitória, ES, Brazil.
Federal Institute of Espírito Santo, Colatina, ES, Brazil.
Free University of Bozen-Bolzano, Italy.
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2020 (English)In: MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, New York: Association for Computing Machinery (ACM), 2020, article id 83Conference paper, Published paper (Refereed)
Abstract [en]

In the last two decades, about a dozen proposals were made to extend object-oriented modeling by multiple abstraction levels. One group of proposals designates explicit levels to objects and classes. The second group uses the powertype pattern to implicitly establish levels. From this group, we consider two proposals, DeepTelos and MLT*. Both have been defined via axioms and both give a central role to the powertype pattern. In this paper, we reconstruct MLT* with the deductive axiomatization style used for DeepTelos. The resulting specification is executed in a deductive database to check MLT* multi-level models for errors and complete them with derived facts that do not have to be explicitly asserted by modelers. This leverages the rich rules of MLT* with the deductive approach underlying DeepTelos. The effort also allows us to clearly establish the relation between DeepTelos and MLT*, in an attempt to clarify the relations between approaches in this research domain. As a byproduct, we supply MLT-Telos as a fully operational deductive implementation of MLT* to the research community.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2020. article id 83
Keywords [en]
Multi-level modeling, Object-oriented modeling, MLT*, Powertype, ConceptBase, Datalog, DeepTelos
National Category
Computer Sciences Software Engineering
Research subject
Information Systems
Identifiers
URN: urn:nbn:se:his:diva-19215DOI: 10.1145/3417990.3421410Scopus ID: 2-s2.0-85096773950ISBN: 978-1-4503-8135-2 (electronic)OAI: oai:DiVA.org:his-19215DiVA, id: diva2:1485443
Conference
MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems, Virtual Event, Canada, 16-23 October 2020
Note

For copyright information, see attached file.

Available from: 2020-11-02 Created: 2020-11-02 Last updated: 2025-09-29

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Jeusfeld, Manfred A.

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
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