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Title of Thesis

Improving Conceptual Modelling in Database Design

Author(s)

Tauqeer Hussain

Institute/University/Department Details
Department of Computer Science / Lahore University of Management Sciences, Lahore
Session
2003
Subject
Computer Science
Number of Pages
243
Keywords (Extracted from title, table of contents and abstract of thesis)
Impact, Completeness, Normalization, Conceptual, Functional, Technique, Methodology, Modelling, Design, Database

Abstract
Conceptual Modeling is one of the most important stages of the database (DB) design methodology. A number of approaches for conceptual modeling have been devised in the literature amongst which the Entity-Relationship (ER) modeling technique is extensively used. Since the quality of a conceptual model impacts the quality of the end product, our research focuses on how the quality of an ER model can be improved. We have identified modeling problems in the existing ER modeling technique and have suggested an approach which solves these problems. The result is an improved ER model which closely represents the real-world problem thereby improving the semantic representation.
Our proposed approach incorporates real-world constraints that can be described in the form of functional dependencies. This approach applies schema transformations iteratively for which a new set of rules has also been defined. New constructs namely single-valued relationship attribute and multi-valued relationship attribute have also been
proposed for improving semantics of the relationship types in an ER model. The impact of the proposed approach on later stages of the database design methodology has also been studied which shows that the resulting relational database satisfies higher normal forms as compared to the existing technique. Quantitative aspect of measuring
improvement in the quality of a conceptual model is also an integral part of the research. For this purpose, we have proposed new metrics called completeness index, normalization index, and overall quality index. Completeness index is further refined by applying fuzzy logic and thus a fuzzy completeness index is proposed. We have also defined quantitative metrics for the structural complexity of an ER model in terms of correctness and modifiability. These metrics help us compare the quality of two ER models quantitatively and objectively. We have shown with several examples the efficacy of our approach and proposed metrics. The ultimate result is a better database design and improved database designers’ productivity.

Download Full Thesis
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S. No. Chapter Title of the Chapters Page Size (KB)
1 0 CONTENTS

 

xiii
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2

1

THEORETICAL ASPECTS

1.1 Context And Motivation
1.2 Traditional Database Design Approach
1.3 Proposed Approach For Database Design
1.4 Our Contribution
1.5 Organization Of The Thesis

1
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3 2 THE DATABASE DESIGN PROCESS AS TODAY

2.1 Introduction
2.2 The Classic Er Model
2.3 Extensions To The Classic Er Model
2.4 Summarizing Evolution Of Er Model
2.5 Mapping Er Diagram To Relational Schema
2.6 Normalization Theory
2.7 Problems Identified
2.8 Summary

26
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4 3 QUALITY ASPECT OF CONCEPTUAL MODELS

3.1 A Quality Framework.
3.2 Quality Through Schema Transformations
3.3 Another Quality Criteria.
3.4 Quality Metrics Of Er Models
3.5 Structural Complexity Of Er Models
3.6 Some Other Approaches
3.7 Problems Identified
3.8 Summary

66
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5 4 IMPROVING CONCEPTUAL DESIGN WITH FUNCTIONAL DEPENDENCIES

4.1 Introduction
4.2 Definitions
4.3 Schema Transformations Based On Composite Attributes
4.4 Schema Transformations Based On Non-key Attributes
4.5 Schema Transformations And Normalization
4.6 Summary

89
340 KB
6 5 IMPACT ON NORMALIZATION

5.1 Introduction
5.2 Er-to-relational Schema Mapping
5.3 Violation Of First Normal Form (1nf)
5.4 Violation Of Second Normal Form (2nf)
5.5 Violation Of Third Normal Form (3nf)
5.6 Violation Of Boyce-codd Normal Form (bcnf)
5.7 Composite Keys, Bcnf And Dependency Preservation
5.8 Summary

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7 6 IMPROVING SEMANTIC REPRESENTATION

6.1 Introduction
6.2 Problem With Relationship Attributes
6.3 Multi-valued Relationship Attributes (mvra)
6.4 Summary

137
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8 7 DEFINING QUALITY METRICS

7.1 Introduction
7.2 Quality Metrics
7.3 Evaluating Quality: A Case Study
7.4 Summary

151
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9 8 REFINING COMPLETENESS INDEX USING FUZZY APPROACH

8.1 Introduction
8.2 Transforming An Erd To A Tas Graph
8.3 Fuzzy Completeness Index (fci)
8.4 Evaluating Quality: An Example
8.5 Effect Of Schema Transformations On Completeness Indices
8.6 Summary

165
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10 9 MEASURING STRUCTURAL COMPLEXITY OF A CONCEPTUAL MODEL

9.1 Introduction
9.2 Understandability And Correctness
9.3 Modifiability
9.4 Measuring Structural Complexity: A Hypothetical Case
9.5 Summary

195
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11 10 CONCLUSIONS AND FURTHER WORK

10.1 Conclusions
10.2 Further Work

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12 11 REFERENCES

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