Structural classification of network models
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ArticlePublisher: oct. 2000Subject(s): Relações de Trabalho
| Gestão de Pessoas | Administração de EmpresasInternational Journal of Project Management 18, 5, p. 361-368Abstract: A newly developed structural classification of network models is suggested. It is based on singling out three main groups of characteristics which define both the structure and the parameters of network models. Those groups are (1) network elements (nodes, arrows, terms`restriction, logical links, etc.). (2) elements` parameters (number, functions, set of variants, random variable, etc.) and (3) degree of alternativity (alternative logical operations at the node`s input and output). The structural classificatin arranges an order in the variety of all the types of network models by using a three-dimension matrix. Each model can be characterised by a set of cells in the three-dimensional `house`. The classification enables not onl a description of the network models, but also a forecast of new models. The latter may be discovered either by filling in "empty places"in the matrix or by implementing new attributes in the groups` characteristics
| Item type | Current location | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
| Periódico | Biblioteca Graciliano Ramos | Periódico | Not for loan |
A newly developed structural classification of network models is suggested. It is based on singling out three main groups of characteristics which define both the structure and the parameters of network models. Those groups are (1) network elements (nodes, arrows, terms`restriction, logical links, etc.). (2) elements` parameters (number, functions, set of variants, random variable, etc.) and (3) degree of alternativity (alternative logical operations at the node`s input and output). The structural classificatin arranges an order in the variety of all the types of network models by using a three-dimension matrix. Each model can be characterised by a set of cells in the three-dimensional `house`. The classification enables not onl a description of the network models, but also a forecast of new models. The latter may be discovered either by filling in "empty places"in the matrix or by implementing new attributes in the groups` characteristics
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