Metamodeling for method engineering

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Published 2005 by MIT Press in Cambridge, Ma .
Written in

Subjects:

• Programming (Mathematics),
• Engineering models

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Edition Notes

Includes bibliographical references and index.

Statement	edited by Manfred A. Jeusfeld, Matthias Jarke, John Mylopoulos.
Series	Cooperative information systems
Contributions	Jeusfeld, Manfred., Jarke, Matthias., Mylopoulos, John.
Classifications
LC Classifications	T57.7 .M48 2005
The Physical Object
Pagination	p. cm.
ID Numbers
Open Library	OL3310844M
ISBN 10	0262101084
LC Control Number	2004061363

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Endorsement of the book 'Metamodeling for Method Engineering' Method engineering has emerged in response to the need to adapt methods to better fit the requirements of the development task at hand. Its aim is to provide techniques for modelling reusable method components, adapting and assembling these together to form the new method. Author: Colette Rolland.   This research focuses on the study of the relationships between sample data characteristics and metamodel performance considering different types of metamodeling methods. In this work, four types of metamodeling methods, including multivariate polynomial method, radial basis function method, kriging method and Bayesian neural network method, three sample quality merits, Cited by:

10/01/ "I'd like to suggest you read Patrice Micouin's "Model-Based Systems Engineering - Fundamentals and Methods". Perhaps after you've read Patrice's work on his Property-Model Methodology (PMM) and its concept of Property-Based Requirements (PBR) you might begin think differently and then again maybe not.".Cited by: (English) In: Metamodeling for Method Engineering / [ed] Manfred Jeusfeld, Matthias Jarke, John Mylopoulos, Cambridge, Massachusetts, USA: MIT Press, Chapter in book (Other academic) Abstract [en] This chapter provides a practical guide on how to use the meta datarepository ConceptBase to design information modeling methods by using meta-modeling. Relaxing the assumption on the differentiability of the simulation output makes the MLMC method more widely applicable to stochastic simulation metamodeling problems in industrial engineering. The proposed scheme uses a sequential experiment design which allocates effort unevenly among design points in order to increase its by: 2.

D [Software Engineering]: Design|Methodologies, Representation General Terms Metamodel, Design Process, Domain Speci c Language Keywords Metamodel, Metamodeling Rules, Domain Speci c Model-ing, Design Process, Situational Method Engineering, Stan-dard 1. INTRODUCTION AND MOTIVATION Traditionally, in software engineering, the idea, or the con-. Model integrated computing (MIC) is an effective and efficient method for developing, maintaining, and evolving large-scale, domain-specific software applications for computer-based systems (CBSs). On a higher level, it is possible to use MIC to develop, maintain, and evolve the meta-level tools (metamodeling environments) themselves, by Cited by:   Metamodeling techniques have been developed from many different disciplines including statistics, mathematics, computer science, and various engineering disciplines. These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation by: