Effectiveness of minimization and measurement of schema complexity based on weighted graph

Golam Md. Muradul Bashir, Arpita Howlader, I. Shemu, E. Ety
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Abstract

The complexity of data requirement increasing day by day; therefore minimization of schema becomes an important role for equi distribution or equalization of two or more problems. Schema complexity is measured by transmitting schema to a graph with weight or weighted graph. Selected special fundamentals from schema for weight the schema graph are described as major part. Minimization of schema ensures easy to measure the schema complexity. Some consideration for this approaches are normalization and de-normalization. We also find the effect of normalization and de-normalization on minimization of schema of real life data model as well as synthetic data model. The purpose of this research is to examine the effectiveness of schema complexity based on schema minimization with weighted graph.
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基于加权图的模式复杂度最小化与度量的有效性
数据需求的复杂性日益增加;因此,模式的最小化成为两个或多个问题的均衡分配或均衡的重要作用。模式复杂度是通过将模式传输到带权重的图或加权图来度量的。从模式中选取特殊的基本要素进行加权,将模式图描述为主要部分。模式的最小化保证了模式复杂度的度量。对这种方法的一些考虑是规范化和反规范化。我们还发现了归一化和反归一化对现实数据模型和综合数据模型的模式最小化的影响。本研究的目的是检验基于加权图的模式最小化的模式复杂度的有效性。
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