MECHANIZATION OF DATA MODEL DESIGN: A PETRI NET BASED APPROACH FOR LEARNING.
Item
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Title
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MECHANIZATION OF DATA MODEL DESIGN: A PETRI NET BASED APPROACH FOR LEARNING.
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Identifier
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AAI8629669
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identifier
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8629669
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Creator
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BASU, DIPAK.
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Contributor
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Michael Anshel
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Date
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1986
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Language
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English
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Publisher
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City University of New York.
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Subject
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Computer Science
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Abstract
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Development and design of data models plays an important role in the mechanization of solution of problems. In this dissertation, we discuss mechanization of the design of data models.;We focus our attention to the micro-world of combinatorial problems, their solutions, and the data models for the solutions. We show how a model can be constructed for the micro-world. We discuss how a machine can learn to construct such a model when it is provided with a rudimentary data model consisting of rules and definitions of a problem.;For this purpose, we interpret the states of the problem and the actions that connect the states, as place-nodes and transition-nodes respectively, of a Petri net: a bipartite directed multi-graph. The petri net is thought to represent the dynamics of the problem. A compatible data model based on the Petri net is constructed which supports and drives the Petri net. This enables the machine to solve the combinatorial problem at hand proving the effectiveness of the data model.;We use a heirarchical learning process to enable the machine to construct the Petri net and the corresponding data model. This evolutionary approach to data model design is viewed as mechanization of design of such models.
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Type
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dissertation
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Source
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PQT Legacy CUNY.xlsx
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degree
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Ph.D.
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Program
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Engineering
