Towards an agile and ontology-aided modeling environment for DSML adaptation
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Author (Corporation)
Publication date
2018
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Type
04B - Conference paper
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Parent work
Advanced Information Systems Engineering Workshops (CAiSE 2018)
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Series
Lecture notes in business information processing
Series number
316
Volume
Issue / Number
Pages / Duration
222-234
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Place of publication / Event location
Tallinn
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Abstract
The advent of digitalization exposes enterprises to an ongoing transformation with the challenge to quickly capture relevant aspects of changes. This brings the demand to create or adapt domain-specific modeling languages (DSMLs) efficiently and in a timely manner, which, on the contrary, is a complex and time-consuming engineering task. This is not just due to the required high expertise in both knowledge engineering and targeted domain. It is also due to the sequential approach that still characterizes the accommodation of new requirements in modeling language engineering. In this paper we present a DSML adaptation approach where agility is fostered by merging engineering phases in a single modeling environment. This is supported by ontology concepts, which are tightly coupled with DSML constructs. Hence, a modeling environment is being developed that enables a modeling language to be adapted on-the-fly. An initial set of operators is presented for the rapid and efficient adaptation of both syntax and semantics of modeling languages. The approach allows modeling languages to be quickly released for usage.
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Event
Advanced Information Systems Engineering Workshops. CAiSE 2018. Lecture Notes in Business Information Processing
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Conference start date
11.06.2018
Conference end date
15.06.2018
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Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
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Peer review of the complete publication
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Closed
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Citation
Laurenzi, E., Hinkelmann, K., Izzo, S., Reimer, U., & van der Merwe, A. (2018). Towards an agile and ontology-aided modeling environment for DSML adaptation. Advanced Information Systems Engineering Workshops (CAiSE 2018), 222–234. https://doi.org/10.1007/978-3-319-92898-2_19