Object-oriented biomedical system modeling--the rationale

A short tutorial and a rationale for Object-Oriented Biomedical (Continuous) System Modelling (OOBSM) are given. The paper investigates and defines what is needed in order to make the work with complex bio-medical and pathophysiological models easier, less error prone and conceptually clearer than is possible by using the existing modelling techniques. It also contains a specification of what is required in order to make such models and corresponding knowledge communicable among different research groups and in order to use such models as components in even more complex models. The work shows that hitherto available continuous system modelling languages and tools are less suitable for the construction of complex, interdisciplinary, multilevel, hierarchical models and model components and that those modelling languages do not allow for easy exchange and communication of the model knowledge between different research groups and sites. It concludes that object-oriented and distributed objects methodologies are both feasible and suitable for such modelling.     

Reusability of model components for environmental simulation – Case studies for integrated coastal zone management

Model constructs in environmental models are seldom reused beyond the project lifetime or in other modelling studies. A library of reusable model components could facilitate the maintenance of existing models and make the design of new models more efficient. Although component-based design is the common standard in software engineering and manufacturing few examples are yet found in environmental science. The multi-disciplinary project SPICOSA used a common, component-based simulation framework for environmental modelling, based on 18 case studies through Europe. The development of high-quality model components with potential for reuse turned out to be a challenge despite of the guidelines and tutorial examples provided. Well-designed components are of appropriate granularity, encapsulated, with a limited use of connectors and proper data handling. Ultimately, the success of a model library depends on a sufficient set of quality components with complementary functionalities, a framework for quality control, and support of the environmental modelling community.     

Combining unit and specification-based testing for meta-model validation and verification

Meta-models play a cornerstone role in Model-Driven Engineering as they are used to define the abstract syntax of modelling languages, and so models and all sorts of model transformations depend on them. However, there are scarce tools and methods supporting their Validation and Verification (V&V), which are essential activities for the proper engineering of meta-models.In order to fill this gap, we propose two complementary meta-model V&V languages. The first one has similar philosophy to the xUnit framework, as it enables the definition of meta-model unit test suites comprising model fragments and assertions on their (in-)correctness. The second one is directed to express and verify expected properties of a meta-model, including domain and design properties, quality criteria and platform-specific requirements.As a proof of concept, we have developed tooling for both languages in the Eclipse platform, and illustrate its use within an example-driven approach for meta-model construction. The expressiveness of our languages is demonstrated by their application to build a library of meta-model quality issues, which has been evaluated over the ATL zoo of meta-models and some OMG specifications. The results show that integrated support for meta-model V&V (as the one we propose here) is urgently needed in meta-modelling environments.     

A framework for coupling explanation and prediction in hydroecological modelling

Causal explanation and empirical prediction are usually addressed separately when modelling ecological systems. This potentially leads to erroneous conflation of model explanatory and predictive power, to predictive models that lack ecological interpretability, or to limited feedback between predictive modelling and theory development. These are fundamental challenges to appropriate statistical and scientific use of ecological models. To help address such challenges, we propose a novel, integrated modelling framework which couples explanatory modelling for causal understanding and input variable selection with a machine learning approach for empirical prediction. Exemplar datasets from the field of freshwater ecology are used to develop and evaluate the framework, based on 267 stream and river monitoring stations across England, UK. These data describe spatial patterns in benthic macroinvertebrate community indices that are hypothesised to be driven by meso-scale physical and chemical habitat conditions. Whilst explanatory models developed using structural equation modelling performed strongly (r² for two macroinvertebrate indices = 0.64–0.70), predictive models based on extremely randomised trees demonstrated moderate performance (r² for the same indices = 0.50–0.61). However, through coupling explanatory and predictive components, our proposed framework yields ecologically-interpretable predictive models which also maintain the parsimony and accuracy of models based on solely predictive approaches. This significantly enhances the opportunity for feedback among causal theory, empirical data and prediction within environmental modelling.     

Seeing the forest through the trees: A review of integrated environmental modelling tools

Today’s interconnected socio-economic and environmental challenges require the combination and reuse of existing integrated modelling solutions. This paper contributes to this overall research area, by reviewing a wide range of currently available frameworks, systems and emerging technologies for integrated modelling in the environmental sciences. Based on a systematic review of the literature, we group related studies and papers into viewpoints and elaborate on shared and diverging characteristics. Our analysis shows that component-based modelling frameworks and scientific workflow systems have been traditionally used for solving technical integration challenges, but ultimately, the appropriate framework or system strongly depends on the particular environmental phenomenon under investigation. The study also shows that – in general – individual integrated modelling solutions do not benefit from components and models that are provided by others. It is this island (or silo) situation, which results in low levels of model reuse for multi-disciplinary settings. This seems mainly due to the fact that the field as such is highly complex and diverse. A unique integrated modelling solution, which is capable of dealing with any environmental scenario, seems to be unaffordable because of the great variety of data formats, models, environmental phenomena, stakeholder networks, user perspectives and social aspects. Nevertheless, we conclude that the combination of modelling tools, which address complementary viewpoints – such as service-based combined with scientific workflow systems, or resource-modelling on top of virtual research environments – could lead to sustainable information systems, which would advance model sharing, reuse and integration. Next steps for improving this form of multi-disciplinary interoperability are sketched.     

A geoprocessing workflow system for environmental monitoring and integrated modelling

Environmental information infrastructure benefits from mainstream information technologies including workflow and service technologies. These technologies allow distributed geoprocessing algorithms, models, data, and sensors to be accessed through Web Services, which later can be chained together to support environmental monitoring and integrated modelling. Existing approaches on integrated environmental modelling, such as OpenMI, have advantages in enabling interoperability between modelling components. It is possible to integrate both of them to take the best from both approaches. The paper introduces the design and implementation of a geoprocessing workflow tool, named GeoJModelBuilder, which is able to integrate interoperable Sensor Web, geoprocessing services, and OpenMI-compliant model components into workflows. In this way, sensors, data, geoprocessing functions, and models could be integrated in a flexible, reusable, interoperable, and user-friendly way. The system has been published as an open source software and illustrated in cases on environmental monitoring and integrated modelling.     

An object-oriented application framework for building water resource information and planning tools applied to the design of a flood analysis system

An object-oriented framework for building computer based modelling tools for water resource planning is presented. The focus is on building a program for flood calculations in river systems with several reservoirs and water transfer structures. The foundation for the flood model is a general application framework for building hydrological modelling tools. The general framework provides the user with tools for describing the structural components of the hydrological system, their relation in the system topology and controlling the behaviour of the system during simulation. Hydrological models are often data intensive, and the framework is equipped with tools to handle both time series and spatially-distributed data efficiently. During the development, effort has been put into supporting future changes and extensions to the model system, as well as creating sound reusable components that will benefit future development and maintenance. A flood modelling application in the Norwegian river Gudbrandsdalslågen is described to illustrate the use of the toolkit.     

The application of componentised modelling techniques to catastrophe model generation

In this paper we show that integrated environmental modelling (IEM) techniques can be used to generate a catastrophe model for groundwater flooding. Catastrophe models are probabilistic models based upon sets of events representing the hazard and weights their likelihood with the impact of such an event happening which is then used to estimate future financial losses. These probabilistic loss estimates often underpin re-insurance transactions. Modelled loss estimates can vary significantly, because of the assumptions used within the models. A rudimentary insurance-style catastrophe model for groundwater flooding has been created by linking seven individual components together. Each component is linked to the next using an open modelling framework (i.e. an implementation of OpenMI). Finally, we discuss how a flexible model integration methodology, such as described in this paper, facilitates a better understanding of the assumptions used within the catastrophe model by enabling the interchange of model components created using different, yet appropriate, assumptions.     

A model for teaching and research into computational modelling

Abstract  We propose an instructional model for computational modelling, based on a methodology commonly described in the mathematical modelling literature. Computational modelling should be seen both at the level of creation of a given model, and at that of learning through the construction of series of models. We present an analysis of the learning needed for the construction of a model, and a further level of analysis for what is needed to generalize from particular models. We describe a way of representing qualitative knowledge of relationships, through the construction of causal diagrams. Finally we present a framework for evaluation of teaching, related to the analysis.     

Defining assessment projects and scenarios for policy support: Use of ontology in Integrated Assessment and Modelling

Integrated Assessment and Modelling (IAM) provides an interdisciplinary approach to support ex-ante decision-making by combining quantitative models representing different systems and scales into a framework for integrated assessment. Scenarios in IAM are developed in the interaction between scientists and stakeholders to explore possible pathways of future development. As IAM typically combines models from different disciplines, there is a clear need for a consistent definition and implementation of scenarios across models, policy problems and scales. This paper presents such a unified conceptualization for scenario and assessment projects. We demonstrate the use of common ontologies in building this unified conceptualization, e.g. a common ontology on assessment projects and scenarios. The common ontology and the process of ontology engineering are used in a case study, which refers to the development of SEAMLESS-IF, an integrated modelling framework to assess agricultural and environmental policy options as to their contribution to sustainable development. The presented common ontology on assessment projects and scenarios can be reused by IAM consortia and if required, adapted by using the process of ontology engineering as proposed in this paper.     

UML的形式化描述语义

本文提出了一种新的定义UML形式化语义的方法。我们将建模语言的语义区分为描述语义和功能语义两个方面。描述语义定义哪些系统满足模型,功能语义定义模型中的基本概念。本文用一阶逻辑定义了UML的类图、交互图和状态图的描述语义,并介绍我们实现的将UML模型转换成逻辑系统的软件工具LAMBDES,该工具集成了定理证明器SPASS,可以对模型进行自动推理。我们成功地将此方法和工具应用于模型的一致性检查。     

A monotone framework for CCS

The calculus of communicating systems, CCS, was introduced by Robin Milner as a calculus for modelling concurrent systems. Subsequently several techniques have been developed for analysing such models in order to get further insight into their dynamic behaviour.In this paper we present a static analysis for approximating the control structure embedded within the models. We formulate the analysis as an instance of a monotone framework and thus draw on techniques that often are associated with the efficient implementation of classical imperative programming languages.We show how to construct a finite automaton that faithfully captures the control structure of a CCS model. Each state in the automaton records a multiset of the enabled actions and appropriate transfer functions are developed for transforming one state into another. A classical worklist algorithm governs the overall construction of the automaton and its termination is ensured using techniques from abstract interpretation.     

A case study of environmental modelling and simulation using transplantable components

Recent changes in environmental modelling practice have included adoption of modern software engineering concepts of model reuse, extensibility and flexibility, and development of better visual user interfaces. Additionally, the concepts of component-based software development and use of object-oriented programming have produced new environmental modelling methods. These changes have been supported by development of a range of modelling environments that allow rapid creation of environmental models from components. To date there has been little exploration of the opportunities that modelling environments offer in terms of flexible model construction by transplanting components. In the work reported here, simple catchment-based nonpoint source pollution models are constructed using the ICMS modelling environment, and a number of different data types and components are used within a single model structure to estimate pollution loads. Models include a bulk runoff-concentration load calculation, an export coefficient model, and land-use specific runoff and loads. It was found that, when applied using a powerful and flexible modelling environment, the component-based modelling concept provides not only an elegant method for model development, but also the opportunity to explore model expansion and refinement through use of transplantable components.     

Selecting among five common modelling approaches for integrated environmental assessment and management

The design and implementation of effective environmental policies need to be informed by a holistic understanding of the system processes (biophysical, social and economic), their complex interactions, and how they respond to various changes. Models, integrating different system processes into a unified framework, are seen as useful tools to help analyse alternatives with stakeholders, assess their outcomes, and communicate results in a transparent way. This paper reviews five common approaches or model types that have the capacity to integrate knowledge by developing models that can accommodate multiple issues, values, scales and uncertainty considerations, as well as facilitate stakeholder engagement. The approaches considered are: systems dynamics, Bayesian networks, coupled component models, agent-based models and knowledge-based models (also referred to as expert systems). We start by discussing several considerations in model development, such as the purpose of model building, the availability of qualitative versus quantitative data for model specification, the level of spatio-temporal detail required, and treatment of uncertainty. These considerations and a review of applications are then used to develop a framework that aims to assist modellers and model users in the choice of an appropriate modelling approach for their integrated assessment applications and that enables more effective learning in interdisciplinary settings.     

Modelling Off-the-Shelf Information Systems Requirements: An Ontological Approach

Requirements for choosing off-the-shelf information systems (OISR) differ from requirements for development of new information systems in that they do not necessarily provide complete specifications, thus allowing flexibility in matching an existing IS to the stated needs. We present a framework for OISR conceptual models that consists of four essential elements: business processes, business rules, information objects and required system services. We formalise the definitions of these concepts based on an ontological model. The ontology-based OISR model provides a framework to evaluate modelling languages on how appropriate they are for OISR requirements specifications. The evaluation framework is applied to the Object-Process Methodology, and its results are compared with a similar evaluation of ARIS. This comparison demonstrates the effectiveness of the ontological framework for evaluating modelling tools on how well they can guide selection, implementation and integration of purchased software packages.     

Toward a methodological framework for agent-based modelling and simulation of supply chains in a mass customization context

In a dynamic customer-centric supply chain context, classic forecasting models turn out to have a limited applicability. In order to estimate the key performance indices of these Supply Chains and to facilitate their management, it is necessary to use more elaborate tools such as a simulation. However building simulation of customer-centric supply chains is no trivial matter. It requires the elaboration of a representative model and the execution of this model according to a set of hypotheses associated to scenarios. Due to their properties, Multi-Agent Systems seem particularly well suited for the modelling and the simulation of Supply Chains and more especially in a mass customization context. In this paper we propose an agent modelling framework for the modelling and simulation of such Supply Chains to facilitate their management. We show how this framework can be applied to a case of customer-centric Supply Chain from the golf club industry and we present an experiment plan associated.