6,955 research outputs found
Validation of the learning ecosystem metamodel using transformation rules
The learning ecosystem metamodel is a platform-independent model to define learning ecosystems. It is
based on the architectural pattern for learning ecosystems. To ensure the quality of the learning ecosystem
metamodel is necessary to validate it through a Model-to-Model transformation. Specifically, it is required to
verify that the learning ecosystem metamodel allows defining real learning ecosystems based on the
architectural pattern. Although this transformation can be done manually, the use of tools to automate the
process ensures its validity and minimize the risk of bias. This work describes the validations process
composed of eight phases and the results obtained, in particular: the transformation of the MOF metamodel
to Ecore to use stable tools for the validation, the definition of a platform-specific metamodel for defining
learning ecosystems and the transformation from instances of the learning ecosystem metamodel to
instances of the platform-specific metamodel using ATL. A quality framework has been applied to the three
metamodels involved in the process to guarantee the quality of the results. Furthermore, some phases have
been used to review and improve the learning ecosystem metamodel in Ecore. Finally, the result of the
process demonstrates that the learning ecosystem metamodel is valid. Namely, it allows defining models
that represent learning ecosystems based on the architectural pattern that can be deployed in real contexts
to solve learning and knowledge management problem
GMF: A Model Migration Case for the Transformation Tool Contest
Using a real-life evolution taken from the Graphical Modeling Framework, we
invite submissions to explore ways in which model transformation and migration
tools can be used to migrate models in response to metamodel adaptation.Comment: In Proceedings TTC 2011, arXiv:1111.440
P ORTOLAN: a Model-Driven Cartography Framework
Processing large amounts of data to extract useful information is an
essential task within companies. To help in this task, visualization techniques
have been commonly used due to their capacity to present data in synthesized
views, easier to understand and manage. However, achieving the right
visualization display for a data set is a complex cartography process that
involves several transformation steps to adapt the (domain) data to the
(visualization) data format expected by visualization tools. To maximize the
benefits of visualization we propose Portolan, a generic model-driven
cartography framework that facilitates the discovery of the data to visualize,
the specification of view definitions for that data and the transformations to
bridge the gap with the visualization tools. Our approach has been implemented
on top of the Eclipse EMF modeling framework and validated on three different
use cases
Towards a pivotal-based approach for business process alignment.
This article focuses on business process engineering, especially on alignment between business analysis and implementation. Through a business process management approach, different transformations interfere with process models in order to make them executable. To keep the consistency of process model from business model to IT model, we propose a pivotal metamodel-centric methodology. It aims at keeping or giving all requisite structural and semantic data needed to perform such transformations without loss of information. Through this we can ensure the alignment between business and IT. This article describes the concept of pivotal metamodel and proposes a methodology using such an approach. In addition, we present an example and the resulting benefits
Metamodel Instance Generation: A systematic literature review
Modelling and thus metamodelling have become increasingly important in
Software Engineering through the use of Model Driven Engineering. In this paper
we present a systematic literature review of instance generation techniques for
metamodels, i.e. the process of automatically generating models from a given
metamodel. We start by presenting a set of research questions that our review
is intended to answer. We then identify the main topics that are related to
metamodel instance generation techniques, and use these to initiate our
literature search. This search resulted in the identification of 34 key papers
in the area, and each of these is reviewed here and discussed in detail. The
outcome is that we are able to identify a knowledge gap in this field, and we
offer suggestions as to some potential directions for future research.Comment: 25 page
WSCDL to WSBPEL: A Case Study of ATL-based Transformation
The ATLAS Transformation Language (ATL) is a hybrid transformation language that combines declarative and imperative programming elements and provides means to define model transformations. Most transformations using ATL reported in the literature show a simplified use of ATL, and often involve a single transformation. However, in more realistic situations, multiple transformations may be necessary, especially in case the original input/output models are not represented in the metametamodeling representation expected by the transformation engine. In this paper, we discuss a model transformation from service choreography (WSCDL) to service orchestration (WSBPEL), which cannot be performed in a single ATL transformation due to the mismatch between the concrete XML syntax of these languages and the metametamodeling representation expected by the ATL transformation engine. This requires auxiliary transformations in which this mismatch is resolved. In principle, the required auxiliary transformations can be implemented using XSLT or a general-purpose programming language like Java. However, in our case study, we evaluate the use of ATL to perform these transformations. We exploit ATL by leveraging the ATL's XML\ud
injection and the XML extraction mechanisms to perform the overall transformation in terms of a transformation chain
Evaluation of Kermeta for Solving Graph-based Problems
Kermeta is a meta-language for specifying the structure and behavior of graphs of interconnected objects called models. In this paper,\ud
we show that Kermeta is relatively suitable for solving three graph-based\ud
problems. First, Kermeta allows the specification of generic model\ud
transformations such as refactorings that we apply to different metamodels\ud
including Ecore, Java, and Uml. Second, we demonstrate the extensibility\ud
of Kermeta to the formal language Alloy using an inter-language model\ud
transformation. Kermeta uses Alloy to generate recommendations for\ud
completing partially specified models. Third, we show that the Kermeta\ud
compiler achieves better execution time and memory performance compared\ud
to similar graph-based approaches using a common case study. The\ud
three solutions proposed for those graph-based problems and their\ud
evaluation with Kermeta according to the criteria of genericity,\ud
extensibility, and performance are the main contribution of the paper.\ud
Another contribution is the comparison of these solutions with those\ud
proposed by other graph-based tools
Transformation As Search
In model-driven engineering, model transformations are con- sidered a key element to generate and maintain consistency between re- lated models. Rule-based approaches have become a mature technology and are widely used in different application domains. However, in var- ious scenarios, these solutions still suffer from a number of limitations that stem from their injective and deterministic nature. This article pro- poses an original approach, based on non-deterministic constraint-based search engines, to define and execute bidirectional model transforma- tions and synchronizations from single specifications. Since these solely rely on basic existing modeling concepts, it does not require the intro- duction of a dedicated language. We first describe and formally define this model operation, called transformation as search, then describe a proof-of-concept implementation and discuss experiments on a reference use case in software engineering
A Modeling Approach based on UML/MARTE for GPU Architecture
Nowadays, the High Performance Computing is part of the context of embedded
systems. Graphics Processing Units (GPUs) are more and more used in
acceleration of the most part of algorithms and applications. Over the past
years, not many efforts have been done to describe abstractions of applications
in relation to their target architectures. Thus, when developers need to
associate applications and GPUs, for example, they find difficulty and prefer
using API for these architectures. This paper presents a metamodel extension
for MARTE profile and a model for GPU architectures. The main goal is to
specify the task and data allocation in the memory hierarchy of these
architectures. The results show that this approach will help to generate code
for GPUs based on model transformations using Model Driven Engineering (MDE).Comment: Symposium en Architectures nouvelles de machines (SympA'14) (2011
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