Meta-Modeling Concepts and Tools for Model Management: A Systems Approach

Abstract

We present a new framework for model management based on system concepts and theory. Underlying the framework is a set of meta-modeling concepts that are useful in capturing the semantics of the modeling process in a modeling environment. These concepts include the notions of a general-model type, type specialization, atomic and composite model versions, model instances, and parameterized versions. We describe these concepts both conceptually and formally and then briefly present a Model Description Language (MDL) that embodies them. While other researchers have suggested some of these concepts primarily in different contexts, this paper makes at least four valuable contributions: (1) the identification of fundamental issues and principles related to model management; (2) the development and enhanced treatment of meta-modeling concepts specifically for model management; (3) the synthesis of those concepts into a coherent, unifying framework for model management; and (4) a demonstration of the practicality of those concepts through a prototype system implementation. Our framework proposes a graph-oriented, nonprocedural, and hierarchical approach for model composition. The framework also supports both model-solver independence and model-data independence. Moreover, it offers general solutions to two critical issues in model management: model-model linkage and model-data linkage. We argue that the system framework can serve as a guide for an effective design of a flexible and extensible model management system. An architecture of such a system and its prototype implementation---called SYMMS---are briefly described. Examples are presented to illustrate the features and advantages of our approach.model management systems, decision support systems, modeling environments, hierarchical model design, systems concepts

Decision-enabled dynamic process management for networked enterprises

“…However, over the past decade and a half, additional requirements concerning portability, vendor independence, and compatibility have become critical due to the feasibility of sharing models within and across organizations driven by advances in supporting communication infrastructure. With the exception of Muhanna and Pick (1994) and few others, very little attention was paid to managing large shared model bases. Accordingly, a major limitation of the aforementioned approaches is their limited support to the requirements for model sharing in a distributed environment.…”

“…The ability to share and reuse mathematical or decision models, which support underlying business processes through aligned business and decision objectives, is a core requirement. Further, the distributed nature of networked enterprises drive several other issues and design requirements (Ezechukwu and Maros 2003;Geoffrion 1987;Muhanna and Pick 1994): (1) a single model representation format, (2) representational independence of model structure and the detailed data, (3) representational independence of model structure and the model solution, (4) meta-modeling capability to support reasoning about models, (5) extensible for different modeling paradigms, and (6) accessibility of decision support resources. These requirements also emphasize the need to reason about syntactic as well as semantic knowledge embedded in models.…”

Compositional enterprise modeling and decision support

“…Their system organizes knowledge fragments that are represented as various kinds of formalized, qualitative relationships and constructs models to answer user-specific queries. Earlier cases of model integration examples in the business area include, for example, Dolk and Kotteman (1993) and Muhanna and Pick (1994). Those approaches, however, do not consider model-based reasoning capabilities or the incorporation of qualitative knowledge as a form of representing organizational knowledge in their model bases.…”

A web services framework for distributed model management

“…Knowledge-based approaches for composition include the use of logical inference (Bonczek, Holsapple, & Whinston, 1981), case-based reasoning (Liang, 1988;Liang & Konsynski, 1993) and blackboard systems (Mookerjee & Chaturvedi, 1993). Techniques based on developing meta-linguistic abstractions include the embedded languages approach (Bhargava & Kimbrough, 1993), graph-based structured modeling (Chari & Sen, 1997, Geoffrion, 1992a, 1992b and object-oriented systems methodologies (Ma, 1995;Muhanna & Pick, 1994;Muhanna, 1993). A generic framework for model management from a data modeling perspective has also been outlined in Bernstein, Halevy, and Pottinger (2000).…”