Titlebook: Metaclasses and Their Application; Data Model Tailoring Wolfgang Klas,Michael Schrefl Book 1995 Springer-Verlag Berlin Heidelberg 1995 Date

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rechende Grundstruktur dar. Es wendet sich damit an alle, deren Aufgabe die Entwicklung, Anpassung und Umsetzung von Risikomanagementsystemen im Immobilienbereich ist. Es hilft damit ganz wesentlich bei der Umsetzung regulatorischer Vorgaben, wie zum Beispiel der Etablierung von Risikotragf?higkeits

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eardrum

Object class definition by generalization,er to the integration of individual user views to a single conceptual schema, or to the integration of preexisting database schemas into a global view schema. In this chapter, we have been interested in the the second aspect and we have shown how the process of object class definition by generalizat

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Book 1995set of modelling primitives. This constitutes a major problem, as different application domains, e.g. database integration or multimedia, need special support..Using an extended metaclass concept, this book provides for the solution of this problem a simple but extendible open object-oriented data m

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Book 1995odel, a so-called RISC model. By introducing the basic concepts of the open object-oriented database management system VODAK, it demonstrates how the extended metaclass concept can be integrated homogeneously into object-oriented data models.

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0302-9743 ted data model, a so-called RISC model. By introducing the basic concepts of the open object-oriented database management system VODAK, it demonstrates how the extended metaclass concept can be integrated homogeneously into object-oriented data models.978-3-540-60063-3978-3-540-49423-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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0302-9743 h a fixed set of modelling primitives. This constitutes a major problem, as different application domains, e.g. database integration or multimedia, need special support..Using an extended metaclass concept, this book provides for the solution of this problem a simple but extendible open object-orien

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Curse of Dimensionality: Classifying Large Multi-Dimensional Images with Neural Networks,w to be of practical clinical use. It is shown that the process could be speeded up by several orders of magnitude by using FFT-based convolution during the input stage of the classification. Further, hardware-dependent computational gain is possible when the FFT is replaced by a number theoretic transform such as the Fermat number transform.

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