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

Integration of Triple Graph Grammars and Constraints

Metamodels are often augmented with additional constraints that must be satisfied by valid instances of these metamodels. Such constraints express complex conditions that cannot be expressed in the metamodel itself. Model transformations have to take such constraints of the source and target metamodels into account. Given a valid source model, which satisfies the source constraints, a model transformation is expected to return a valid target model (forward validity). However, in current model transformation definition and tool support, such an integration with source and target constraints including validation mechanisms is often ignored or not satisfactory yet.In this paper, we describe how the integration with source and target constraints can be achieved for the special case of model transformations defined by Triple Graph Grammars (TGGs). First, we extend the relational model transformation definition for TGGs and integrate it with source and target constraints. Moreover, we describe how forward/backward validity of TGGs with constraints can be automatically checked, either by static analysis using an invariant checker, or by generating and validating metamodel instances. Finally, we describe how to integrate constraints into our TGG-based model transformation implementation and automatic conformance testing framework

A case study on the transformation of context-aware domain data onto XML schemas

In order to accelerate the development of context-aware applications, it would be convenient to have a smooth path between the context models and the automated services that support these models. This paper discusses how MDA technology (metamodelling and the QVT standard) can support the transformation of high-level models of context-aware services onto the implementation of these services using web services. The total transformation process from context-aware services onto web services involves the following aspects: 1. service signatures, which should be translated onto WSDL definitions; 2. context-aware domain data used as input and output data in service operations, which should be translated onto XML schemas; and 3. service behaviours, which should be used to generate the service implementation. This paper concentrates on the modelling and transformation of the context-aware domain data. The results of this paper are generally applicable to the transformation of elements of any domain-specific language expressed in terms of a metamodel onto XML Schema data

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

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

Automatically Discovering Hidden Transformation Chaining Constraints

Model transformations operate on models conforming to precisely defined metamodels. Consequently, it often seems relatively easy to chain them: the output of a transformation may be given as input to a second one if metamodels match. However, this simple rule has some obvious limitations. For instance, a transformation may only use a subset of a metamodel. Therefore, chaining transformations appropriately requires more information. We present here an approach that automatically discovers more detailed information about actual chaining constraints by statically analyzing transformations. The objective is to provide developers who decide to chain transformations with more data on which to base their choices. This approach has been successfully applied to the case of a library of endogenous transformations. They all have the same source and target metamodel but have some hidden chaining constraints. In such a case, the simple metamodel matching rule given above does not provide any useful information