Elsevier

Information Systems

Volume 20, Issue 2, April 1995, Pages 127-153
Information Systems

Computational modeling systems

https://doi.org/10.1016/0306-4379(95)98558-UGet rights and content

Abstract

A computational modeling system (CMS) provides scientific investigators with a unified computational environment and easy access to a broad range of modeling tools. The goal of a CMS is to provide computational support that increases the efficiency of scientists in the iterative process of modeling. A CMS consists of a computational modeling environment and transparent computational support for the environment. The modeling environment is based on a characterization of scientific modeling activities that is focussed on the manner in which scientific concepts are represented, manipulated, and evaluated, in the scientific modeling process. Based on a formalization of the representation for a concept as representational structures (or “R-structures”), the process of scientific modeling may be viewed as one in which (1) extensible collections R-structures are constructed, evaluated and applied in modeling both the phenomena in specific application domains and the phenomena of the modeling process itself; and (2) instances of the domain elements of R-structures are created and sequences of transformations are applied to the instances. R-structures provide a “complete” and consistent foundation for both the modeling environment of a CMS and its associated, high-level computational modeling language (CML). CML may be employed in creating, accessing, and manipulating R-structures and their components in a simple, uniform manner. A CMS provides a unifying framework for the integration of existing tools, such as DBMS and mathematical software modules, and a distributed modeling environment. Based on the general specification of a CMS, we have designed and implemented a specific CMS, Amazonia, which supports earth science applications in terms of a specific set of R-structures and a “seamlessly” integrated and extendable collection of computational modules, including an object-oriented DBMS.

References (53)

  • W.J. Campbell et al.

    Evolution of an Intelligent Information Fusion System

    Photogrammetric Engineering and Remote Sensing

    (1990)
  • L. Cardelli

    A semantics of multiple inheritance

  • L. Cardelli

    Structural subtyping and the notion of power types

  • L. Cardelli et al.

    On understanding types, data abstraction, and polymorphism

    ACM Computing Surveys

    (1985)
  • S. de Hoop et al.

    Storage and manipulation of topology in Postgres

  • H.D. Ebbinghaus et al.
  • P.K. Garg et al.

    Quantitative representation of land-surface morphology from digital elevation models

  • D. Harel et al.

    Statemate: a working environment for the development of complex reactive systems

    IEEE Transactions on Software Engineering

    (1990)
  • R. Hull et al.

    Semantic data modeling: Survey, applications, and research issues

    ACM Computing Surveys

    (1987)
  • P. Jankowski et al.

    A model management approach to modeling and simulation natural systems

  • C.V. Jones

    An introduction to graph-based modeling systems, Part I: Overview

    ORSA Journal on Computing

    (1990)
  • C.V. Jones

    An introduction to graph-based modeling systems, Part II: Graph-grammars and the implementation

    ORSA Journal on Computing

    (1991)
  • D.B Kidner et al.

    Digital terrain models for radio path profiles

  • L.-C. Liu

    Object database support for CASE

  • D.D.E. Long et al.

    REINAS: Real time environmental information network and analysis system: Concept statement

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