Abstract
Model transformations play a key role in the vision of Model Driven Engineering (MDE) whereby the overcoming of structural heterogeneities,
being a result of applying different meta-modeling constructs
for the same semantic concept, is a challenging, recurring problem, urgently
demanding for reuse of transformations. In this respect, an approach
is required which (i) abstracts from the concrete execution language
allowing to focus on the resolution of structural heterogeneities, (ii)
keeps the impedance mismatch between specification and execution low
enabling seamless debuggability, and (iii) provides formal underpinnings
enabling model checking. Therefore, we propose to specify model transformations
by applying a set of abstract mapping operators (MOPs),
each resolving a certain kind of structural heterogeneity. For specifying
the operational semantics of the MOPs, we propose to use Transformation
Nets (TNs), a DSL on top of Colored Petri Nets (CPNs), since it
allows (i) to keep the impedance mismatch between specification and
execution low and (ii) to analyze model transformations by evaluating
behavioral properties of CPNs.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Workshop on Petri Nets and Software Engineering, Technical Report of University Hamburg, Department of Informatics, Braga, Portugal, June 22, 2010, pp. 141-157, 2010 |
| Pages | 141-157 |
| Number of pages | 16 |
| Publication status | Published - 2010 |
Fields of science
- 102 Computer Sciences
- 102002 Augmented reality
- 102006 Computer supported cooperative work (CSCW)
- 102013 Human-computer interaction
- 102015 Information systems
- 102021 Pervasive computing
- 102025 Distributed systems
- 102027 Web engineering
- 202038 Telecommunications
- 101004 Biomathematics
- 101027 Dynamical systems
- 101028 Mathematical modelling
- 101029 Mathematical statistics
- 101014 Numerical mathematics
- 101015 Operations research
- 101016 Optimisation
- 101017 Game theory
- 101018 Statistics
- 101019 Stochastics
- 101024 Probability theory
- 101026 Time series analysis
- 102001 Artificial intelligence
- 102003 Image processing
- 102004 Bioinformatics
- 102018 Artificial neural networks
- 102019 Machine learning
- 103029 Statistical physics
- 106005 Bioinformatics
- 106007 Biostatistics
- 202017 Embedded systems
- 202035 Robotics
- 202036 Sensor systems
- 202037 Signal processing
- 305901 Computer-aided diagnosis and therapy
- 305905 Medical informatics
- 305907 Medical statistics
- 102032 Computational intelligence
- 102033 Data mining
- 101031 Approximation theory