Controlling view editability in projection-based modeling environments

Atkinson, Colin ; Tunjic, Christian ; Lange, Arne

DOI:	https://doi.org/10.1109/EDOC52215.2021.00026
URL:	https://ieeexplore.ieee.org/document/9626182/autho...
Weitere URL:	https://www.researchgate.net/publication/356039672...
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2021
Buchtitel:	2021 IEEE 25th International Enterprise Distributed Object Computing Conference (EDOC) : 25−29 October 2021, Gold Coast, Australia : proceedings
Seitenbereich:	152-161
Veranstaltungstitel:	EDOC 2021
Veranstaltungsort:	Online
Veranstaltungsdatum:	25.-29.10.2021
Herausgeber:	Bilof, Randall
Ort der Veröffentlichung:	Piscataway, NJ
Verlag:	IEEE
ISBN:	978-1-6654-1191-2 , 978-1-6654-3579-6
ISSN:	2325-6362
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Software Engineering (Atkinson 2003-)
Fachgebiet:	004 Informatik
Abstract:	In projection-based modeling environments, users work with views of the modelled system or enterprise that are generated dynamically, on demand, from a Single Underling Model (SUM). Moreover, they usually cannot change the SUM directly, but can only influence its contents indirectly via views. This means that at least some of the views need to be editable so that they can be used to evolve the SUM to a new state. However, this is not always possible when the transformations used to project SUM content into view content do not provide the information needed to unambiguously map view changes into SUM changes. Therefore, mechanisms are needed to identify and take appropriate measures to ensure that the integrity of the SUM is not comprised, and it is not updated in unintended ways, when insufficient information is available. This paper illuminates the basic nature of the problem, categorizes the different kinds of situations in which editing ambiguities can arise and discusses potential strategies for dealing with them. These techniques contribute to the creation of user-friendly, yet dependable, projection-based modeling environments.