Jan Pettersen Nytun

Automatic generation of modelling tools

Higher-level modelling is considered to be the answer to many of the problems computer science is faced with. In order to do modelling, it is necessary to use proper tools. This article is about modelling tools and how they can be generated automatically out of (modelling) language descriptions. Language descriptions in turn are given in meta-models. In this article, we define a terminology for aspects of meta-models and check how they are supported by existing meta-modelling tools. In particular we look at semantic aspects of the meta-models.

A Conceptual Schema Language for the Management of Multiple Representations of Geographic Entities

Multiple representation of geographic information occurs when a real-world entity is represented more than once in the same or different databases. This occurs frequently in practice, and it invariably results in the occurrence of inconsistencies among the different representations of the same entity. In this paper, we propose an approach to the modeling of multiple represented entities, which is based on the relationships among the entities and their representations. Central to our approach is the Multiple Representation Schema Language that, by intuitive and declarative means, is used to specify rules that match objects representing the same entity, maintain consistency among these representations, and restore consistency if necessary. The rules configure a Multiple Representation Management System, the aim of which is to manage multiple representations over a number of autonomous federated databases. We present a graphical and a lexical binding to the schema language. The graphical binding is built on an extension to the Unified Modeling Language and the Object Constraint Language. We demonstrate that it is possible to implement the constructs of the schema language in the object-relational model of a commercial RDBMS.

Supporting fine-grained generative model-driven evolution

In the standard generative Model-driven Architecture (MDA), adapting the models of an existing system requires re-generation and restarting of that system. This is due to a strong separation between the modeling environment and the runtime environment. Certain current approaches remove this separation, allowing a system to be changed smoothly when the model changes. These approaches are, however, based on interpretation of modeling information rather than on generation, as in MDA. This paper describes an architecture that supports fine-grained evolution combined with generative model-driven development. Fine-grained changes are applied in a generative model-driven way to a system that has itself been developed in this way. To achieve this, model changes must be propagated correctly toward impacted elements. The impact of a model change flows along three dimensions: implementation, data (instances), and modeled dependencies. These three dimensions are explicitly represented in an integrated modeling-runtime environment to enable traceability. This implies a fundamental rethinking of MDA.

Modeling and Testing Legacy Data Consistency Requirements

An increasing number of data sources are available on the Internet, many of which offer semantically overlapping data, but based on different schemas, or models. While it is often of interest to integrate such data sources, the lack of consistency among them makes this integration difficult.

MOF-VM: Instantiation revisited

The Model-Driven Architecture (MDA) is based on an understanding of a hierarchy of levels that are placed on top of each other and that are connected with instantiation. For practical MDA use, it is important to be clear about the kinds of objects that reside on the different levels and the relations between them as well as relations to objects outside of the MDA domain. This article aims at enhancing the understanding of these objects and relations by relating them to a virtual MOF machine.

Reference Design for Smart Collaborative Telehealth and Telecare Services Based on IoT Technologies

The demographic development shows aging societies through-out the globe, leading to higher demands on the health and care systems due to age-related disabilities, chronic diseases, etc. Telehealth and telecare services for more efficient support of health and care providers are needed, which can be significantly improved by the continuous technological developments in areas such as sensor devices, Internet of Things (IoT), cloud services, and data analysis. This paper addresses the potential of selected emerging technologies to make eHealth systems smarter, more collaborative and more efficient. As a result of the analysis of the most promising technological trends, characteristics of future telehealth and telecare services are derived, requirements are identified, and a future eHealth reference design is proposed.