Metamodel-based model conformance and multiview consistency checking

Model-driven development, using languages such as UML and BON, often makes use of multiple diagrams (e.g., class and sequence diagrams) when modeling systems. These diagrams, presenting different views of a system of interest, may be inconsistent. A metamodel provides a unifying framework in which to ensure and check consistency, while at the same time providing the means to distinguish between valid and invalid models, that is, conformance. Two formal specifications of the metamodel for an object-oriented modeling language are presented, and it is shown how to use these specifications for model conformance and multiview consistency checking. Comparisons are made in terms of completeness and the level of automation each provide for checking multiview consistency and model conformance. The lessons learned from applying formal techniques to the problems of metamodeling, model conformance, and multiview consistency checking are summarized

Agent-Based Modelling and Sensitivity Analysis by Experimental Design and Metamodelling: An Application to Modelling Regional Structural Change

This paper presents the application of the sensitivity analysis techniques Design of Experiments (DOE) and metamodelling to the agent-based model AgriPoliS, which is a spatial and dynamic simulation model of regional structural change. DOE and metamodelling provide a more systematic analysis of results of complex simulation models. When summarising the results, it becomes clear that interest rates, technical change and managerial ability influence average economic land rent the most.simulation, design of experiments, metamodelling, structural change, Research and Development/Tech Change/Emerging Technologies, C9, C15,

Development and validation of a disaster management metamodel (DMM)

Disaster Management (DM) is a diffused area of knowledge. It has many complex features interconnecting the physical and the social views of the world. Many international and national bodies create knowledge models to allow knowledge sharing and effective DM activities. But these are often narrow in focus and deal with specified disaster types. We analyze thirty such models to uncover that many DM activities are actually common even when the events vary. We then create a unified view of DM in the form of a metamodel. We apply a metamodelling process to ensure that this metamodel is complete and consistent. We validate it and present a representational layer to unify and share knowledge as well as combine and match different DM activities according to different disaster situations

Software modelling languages: A wish list

© 2015 IEEE. Contemporary software engineering modelling tends to rely on general-purpose languages, such as the Unified Modeling Language. However, such languages are practice-based and seldom underpinned with a solid theory-be it mathematical, ontological or concomitant with language use. The future of software modelling deserves research to evaluate whether a language base that is compatible with these various elements as well as being philosophically coherent offers practical advantages to software developers

DM model transformations framework

Metamodelling produces a \u27metamodel\u27 capable of generalizing the domain. A metamodel gathers all domain concepts and their relationships. It enables partitioning a domain problem into sub-problems. Decision makers can then develop a variety of domain solutions models based on mixing and matching solutions for sub-problems indentified using the metamodel. A repository of domain knowledge structured using the metamodel would allow the transformation of models generated from a higher level to a lower level according to scope of the problem on hand. In this paper, we reveal how a process of mixing and matching disaster management actions can be accomplished using our Disaster Management Metamodel (DMM). The paper describes DM model transformations underpinned by DMM. They are illustrated benefiting DM users creating appropriate DM solution models from extant partial solutions

iStarJSON : a lightweight data-format for i* models

JSON is one of the most widely used data-interchange format. There is a large number of tools open for modelling with i*. However, none of them provides supporting for JSON. In this paper we propose iStarJSON language, a JSON-based proposal for interchanging i* models. We also, present an open source software that transforms XML-based format models to JSON models that expose a set of web services for mining iStarJSON models.Peer ReviewedPostprint (author's final draft

How to connect design thinking and cyber-physical systems: the s*IoT conceptual modelling approach

The alignment of enterprise models and information systems is a factor that influences the efficiency of enterprise practices. Considering the changing landscape in the age of the fourth industrial revolution, it is imperative that alignment methodologies are evolved with the progression of enterprise models and the transformation from information systems to cyber-physical systems (CPSs). This issue was dissected in three layers - scenario layer, modelling layer, and run-time environment. In this structure, design thinking and CPSs were extended from the scenario layer and the run-time environment to the modelling layer. Focusing on the modelling layer, progress was made towards composing smart models that innovate enterprise models according to novel influences from design thinking while abstracting from run-time environments that CPS provide. The hypothesis was to consider the automated transformation of knowledge as an axle around which artifacts on the modelling layer revolve. Based on this hypothesis, the modelling layer was structured in a modelling hierarchy, in which a metamodel was defined using a metamodelling platform. The metamodel is the direct model of modelling methods which were used to build smart models that connect design thinking and CPSs

Towards knowledge sharing in disaster management: An agent oriented knowledge analysis framework

Disaster Management (DM) is a complex set of interrelated activities. The activities are often knowledge intensive and time sensitive. Sharing the required knowledge timely is critical for DM. In developed countries, for recurring disasters (e.g. floods), there are dedicated document repositories of Disaster Management Plans (DMP) that can be accessed as needs arise. However, accessing the appropriate plan in a timely manner and sharing activities between plans often requires domain knowledge and intimate knowledge of the plans in the first place. In this paper, we introduce an agent-based knowledge analysis method to convert DMPs into a collection of knowledge units that can be stored into a unified repository. The repository of DM actions then enables the mixing and matching knowledge between different plans. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). We use the flood management plans used by SES (State Emergency Service), an authoritative DM agency in NSW (New State Wales) State of Australia to illustrate and give a preliminary validation of the approach. It is illustrated using DMPs along the flood prone Murrumbidgee River in central NSW

Organisational Metamodel for Large-Scale Multi-Agent Systems: First Steps Towards Modelling Organisation Dynamics

The research presented in this paper is a thesis proposal with the main goal of defining an ontology comprising chosen organisational concepts applicable to large-scale multiagent systems (LSMAS), and building a metamodel for modelling selected organisational features in such systems. The method of applying aspects of human organisations to multiagent systems (MAS) comprising autonomous intelligent agents will be enriched through this research with a new perspective of modelling organisation dynamics in LSMAS. Results of this research, in their final version, will be tested using testbed scenarios based on a specific massively multi-player online role-playing game (MMORPG), since MMORPGs are one of the identified application domains of LSMAS. It is important to note that results described in this paper showcase partial results in their early stage of development. Nevertheless, first traces of a modelling tool that is expected to aid in development of LSMAS for numerous application domains, and ease their organisational design, are recognisable in the proposed combination of ontology engineering, metamodelling and code generating methods