Model Capsules for Research and Engineering Networks

Typical system specification consists of a number of models for different facets and aspects of the system. UML-based development uses for instance at least half-dozen different diagrams which coherence is not given in an explicit form. Sometimes, systems description is based on a variety of abstraction levels. These abstraction level refer to each other in some fuzzy informal way. This paper aims in the development of a theory of multi-model development. We introduce model suites as a set of models with explicit associations among the models, with explicit controllers for maintenance of coherence of the models, with application schemata for their explicit maintenance and evolution, and tracers for establishment of their coherence. This theory has been tested against typical applications such as OLTP-OLAP architectures, multi-model suites at the same abstraction layer, and challenging applications for scientific databases. Model suites are based on a general theory, on a specification technology and on an implementation technology for sets of models that share common submodels, that collaborate and that evolve over time, with new or corrected data and with new analysis tasks. These models must be tightly coupled. Model suites are used to specify this model coupling and model collaboration. Renewable and evolving model clusters are going to be developed for the development of a theory and supporting technology. Collaboration and Distribution of Models Specification of distribution has neglected over a long period. Instead of explicit specification of distribution, multi-database systems and federated database systems have been extensively discussed in the literature. From the other side, database research has succeeded in developing approaches that incorporate conceptual specification and allow to reason on systems at a far higher abstraction level. With the advent of web information systems systems became naturally distributed. Therefore, we need a techniques for conceptual description of distribution. Distribution does not stand alone but follows computations and business needs. Thus, we need to consider structuring, functionality and distribution at the same time. Since these aspects are intertwined with each other and systems cooperate, communicate and coordinate their action we base our consideration on collaboration. It integrates communication, coordination and cooperation. In this paper we develop a specification framework for collaborating systems.

Engineering design is a knowledge intensive activity. Design is characterized as comprising a number of phases from requirements to detailed specification. Transitions between the design phases stem from decision-making processes supported both by generally available domain and design knowledge and from unique and original knowledge worth of recording for future reuse. The Clockwork project addresses the problems of representing, sharing and reusing knowledge in a collaborative design engineering environment. The Clockwork knowledge management methodology is supported by a web-based toolkit that allows designers to share, formally and informally annotate and reuse engineering models. Reuse is supported through the semantic search of engineering models and of the design rationale by which the engineering models were developed. The approach has been successfully deployed in two industrial organisations.

Enterprise Interoperability II

The application of model-driven development facilitates faster and more flexible integration by separating system descriptions to different levels of abstraction. In crossorganisational development new challenges arise to enable enterprise models sharing knowledge independent of language and tools. However, interoperability problems in modelling can be hardly overcome by solutions operating essentially at syntactical level. This paper presents an approach using the capabilities of semantic technologies in modeldriven development and discusses its improvements for collaborative modelling.

2009

Abstract. The goal of the workshop was to exchange ideas and experiences related to Model (Co-) evolution and Consistency Management (MCCM) in the context of Model-Driven Engineering (MDE). Contemporary MDE practices typically include the manipulation and transformation of a large and heterogeneous set of models. This heterogeneity exhibits itself in different guises ranging from notational differences to semantic content-wise variations.

IEEE Systems Journal, 2015

Today, one of the major challenges in full-vehicle model creation is to get domain models from different experts while detecting any potential inconsistency problem before the IVVQ (Integration, Verification, Validation, and Qualification) phase. To overcome such challenges, Conceptual Design phase has been adapted to the current Model Development Process (MDP). For that, the system engineer starts to define the most relevant model architecture by respecting quality and time constraints. Next, the architect model designs the delivered model architecture in a more formal way to support the integration of domain models in a consistent fashion. Finally, the architect model negotiates with different model providers with the aim of specifying vehicle and domain level models and their interfaces connections. To improve knowledge sharing between mentioned actors, we propose a Model Identity Card (MIC) for classifying analysis modeling knowledge including input/output parameters and accuracy expectation. The fundamental concepts that form the basis of all engineering analysis models are identified and typed for implementation into a computational environment. An industrial case study of engine-after treatment model is used to show how MICs and integrated model design phase might use in a given scenario. A validation protocol is conducted through a heuristic observation to estimate the rate of model rework and ambiguity reduction. Coatanea, E. (2015). A Model Identity Card to Support Engineering Analysis Model (EAM) Development Process in a Collaborative Multidisciplinary Design Environment. Systems Journal, doi 10.1109/JSYST.2014.2371541 2

Proceedings of the 2006 international workshop on Global integrated model management - GaMMa '06, 2006

2017

The Open Models Laboratory (OMiLAB) provides an open innovation environment for modeling method engineering. Innovation within OMiLAB relates to the engineering of new domain-specific modelling methods. The created modeling methods are shared in a world-wide open and collaborative community. OMiLAB enables prototyping and experimentation in heterogeneous research areas. The backbone of the OMiLAB ecosystem is openness on multiple layers: content, methodology, and technology. Content refers to the sharing of research results, methodology refers to the application domain of modeling methods, and technology refers to the provision and use of open technologies. This paper introduces OMiLAB briefly focusing on the different evolved communities that constitute the OMiLAB ecosystem as it exists today.

IEEE Software, 2015

Model-driven engineering (MDE) is increasingly used across industries to abstract designs and viewpoints. Development productivity improves owing to faster change cycles. However, many current MDE tools are suitable for drawing but won't scale up. Roundtrip for maintenance, tool interoperability, and team collaboration are far from industry needs. But there's a light on the horizon with a new generation of MDE tools. In this issue's column, Alfonso Pierantonio and his team provide an overview of recent MDE technologies. I look forward to hearing from both readers and prospective column authors about this column and the technologies you want to know more about. -Christof Ebert MODEL-DRIVEN ENGINEERING (MDE) promotes the systematic use of models as rst-class abstractions throughout the software development life cycle. This lets developers leverage abstraction by shifting development focus from thirdgeneration programming-language code to design models expressed in domain-speci c modeling languages. The objective is to increase productivity and reduce time to market. MDE does this by enabling the development of complex systems using models de ned with concepts that are much less bound to the underlying implementation technology and much closer to the problem domain. Several model-driven platforms and tools can simplify and automate many steps of MDE. Commercial tools such as Rational Rhapsody Design Manager, Enterprise Architect, Visual Paradigm, and MagicDraw have enabled the adoption of MDE for developing complex software systems in key industrial domains including automotive manufac-Many tools are more suitable for drawing rather than for industry needs.

2009 18th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises, 2009

Increasingly, models are becoming first class core assets, and model-driven engineering requires novel techniques, tools, and practices to face the globalization of software development in the (always more) pervasive IT world. This paper proposes a framework for synchronous and asynchronous concurrent and collaborative modeling. Synchronous collaborative modeling offers services for sharing the modeling space, models, documentation, and configuration, while asynchronous collaborative modeling offers services for supporting merging of models modified and edited separately by different software engineers. Our approach is based on the observation that it is in general more convenient to store differences between subsequent versions of a system than the whole models of each stage.

Lecture Notes in Computer Science, 2008

Computational models of biological systems aim at accurately simulating in vivo phenomena. They have become a very powerful tool enabling scientists to study complex behavior. A side effect of their success unfortunately exists and is observed as an increasing difficulty in managing data, metadata and a myriad of programs and tools used and produced during a research task. In this work we aim at supporting scientists during a research endeavour by using Scientific Models as a main guiding element for describing, searching and running computational models, as well as managing the corresponding results. We assume a data-oriented perspective for scientific model representation materialized into a data model with which users describe scientific models and corresponding computational models, and a query language with which a scientist specifies simulation queries. The model is grounded in XML and tightly related to domain ontologies, which provide formal domain descriptions and uniform terminology. Scientists may search for scientific models and run simulations that automatically invoke the underlying programs on provided inputs. The results of a simulation may generate complex data that can be queried in the context of the scientific model. Higher-level models can be specified through views that export a unified representation of underlying scientific models.