Libraries of Reusable Models: Theory and Application

Breunese, A.P.J.; Broenink, Johannes F.; Top, J.L.; Akkermans, Jos

doi:10.1177/003754979807100101

Cited by 28 publications

(15 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Formalisms are categorised according to three levels of abstraction, as inspired by (Breunese et al, 1998) and illustrated in Figure 1: technical, conceptual and mathematical.…”

Section: Representationsmentioning

confidence: 99%

On compositional modelling

Keppens¹,

Shen²

2001

The Knowledge Engineering Review

View full text Add to dashboard Cite

Many solutions to AI problems require the task to be represented in one of a multitude of rigorous mathematical formalisms. The construction of such mathematical models forms a difficult problem which is often left to the user of the problem-solver. This void between problem-solvers and their problems is studied by the eclectic field of automated modelling. Within this field, compositional modelling, a knowledge-based methodology for system-modelling, has established itself as a leading approach. In general, a compositional modeller organises knowledge in a structure of composable fragments that relate to particular system components or processes. Its embedded inference mechanism chooses the appropriate fragments with respect to a given problem, instantiates and assembles them into a consistent system model. Many different types of compositional modeller exist, however, with significant differences in their knowledge representation and approach to inference. This paper examines compositional modelling. It presents a general framework for building and analysing compositional modellers. Based on this framework, a number of influential compositional modellers are examined and compared. The paper also identifies the strengths and weaknesses of compositional modelling and discusses some typical applications.

show abstract

“…Formalisms are categorised according to three levels of abstraction, as inspired by (Breunese et al, 1998) and illustrated in Figure 1: technical, conceptual and mathematical.…”

Section: Representationsmentioning

confidence: 99%

On compositional modelling

Keppens¹,

Shen²

2001

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…Furthermore increasing size and geographical separation of design data and teams has created a need for network-based design environments [1,2,3]. Usually modeling and simulation tools are associated with libraries of reusable modeling components that will make the description of the models and also their validation much easier [4,5,6]. Storing models in a common generic library has several benefits.…”

Section: Introductionmentioning

confidence: 99%

DEVSimPy: A Collaborative Python Software for Modeling and Simulation of DEVS Systems

Capocchi

Santucci

Poggi

et al. 2011

2011 IEEE 20th International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises

View full text Add to dashboard Cite

Abstract-The Modeling and Simulation (M&S) of complex systems leans on the collaboration between different actors coming from specific domains. These actors have to communicate through an efficient software in order to improve the M&S process. We therefore propose in this article a collaborative M&S software framework called DEVSimPY. We point out the use of DEVSimPy through a concrete case study: hydraulic network management.

show abstract

“…These functionalities of the system will be obtained by establishing standard libraries of interoperable science and auxiliary components or modules that provide the building blocks for a number of similar applications. Module libraries have been successfully used in several domains, such as the manufacturing, transport, and other systems (Breunese et al, 1998;Praehofer, 1996). One of the earliest modular model developments was done for SHE, the European Hydrologic System Model (Abbot et al, 1986).…”

Section: Introductionmentioning

confidence: 99%

Developing natural resource models using the object modeling system: feasibility and challenges

Ahuja¹,

Ascough²,

David

2005

Adv. Geosci.

View full text Add to dashboard Cite

Abstract. Current challenges in natural resource management have created demand for integrated, flexible, and easily parameterized hydrologic models. Most of these monolithic models are not modular, thus modifications (e.g., changes in process representation) require considerable time, effort, and expense. In this paper, the feasibility and challenges of using the Object Modeling System (OMS) for natural resource model development will be explored. The OMS is a Java-based modeling framework that facilitates simulation model development, evaluation, and deployment. In general, the OMS consists of a library of science, control, and database modules and a means to assemble the selected modules into an application-specific modeling package. The framework is supported by data dictionary, data retrieval, GIS, graphical visualization, and statistical analysis utility modules. Specific features of the OMS that will be discussed include: 1) how to reduce duplication of effort in natural resource modeling; 2) how to make natural resource models easier to build, apply, and evaluate; 3) how to facilitate long-term maintainability of existing and new natural resource models; and 4) how to improve the quality of natural resource model code and ensure credibility of model implementations. Examples of integrating a simple water balance model and a large monolithic model into the OMS will be presented.

show abstract

Libraries of Reusable Models: Theory and Application

Cited by 28 publications

References 5 publications

On compositional modelling

On compositional modelling

DEVSimPy: A Collaborative Python Software for Modeling and Simulation of DEVS Systems

Developing natural resource models using the object modeling system: feasibility and challenges

Contact Info

Product

Resources

About