King-Fung Iu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 72-74).

Thesis (M.C.S.)--Carleton University, 1992. / Also available in electronic format on the Internet.

While the relational model for data storage is sufficient for the modelling and manipulation of a large number of application domains, a growing class of application domains are either difficult or impossible for the relational model to deal with efficiently. The realisation of this fact has led to a proliferation of data models that attempt to increase the complexity and semantic capture of the domains that they can model - the development of object-oriented databases and the various semantic data models are a result of this. The idea of using logic to define, manipulate and constrain data has given rise to large numbers of systems that interface - not always successfully - a database system and a logic processing system. Most such systems are based on Prolog or its derivations. This thesis describes the development and use of an object-oriented and semantically rich form of logic - conceptual graph theory - as a system for the definition, manipulation, and constraint of data. It describes a theoretical correspondence between conceptual graph theory and the relational model, and proceeds to develop an augmented, hybrid theory that is formally more expressive and as rigorous as those languages based on the relational algebra or calculus. This thesis also describes the design and implementation of a hybrid relational database - conceptual graph system, that has a cleaner and more principled system of semantic capture than other (for example, Prolog-based) systems, and that is also adaptive in nature - it automatically modifies its underlying storage structures in accordance with modifications made to the structures of the application domain over time. This completely shields the user from any responsibility for database design and maintenance, and so the user need only be concerned with application domain knowledge. Although the implementation described is incomplete, it can be extended to produce a deductive, object-oriented database system based on conceptual graphs.

Chan, Chi Wai Alan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 121-126). / Abstracts in English and Chinese. / Abstract --- p.v / Acknowledgements --- p.viii / List of Figures --- p.ix / List of Tables --- p.xi / Chapter 1. --- Introduction --- p.12 / Chapter 1.1. --- Object-oriented databases --- p.12 / Chapter 1.2. --- Object-oriented Data Model --- p.14 / Chapter 1.3. --- Class and Object Instances --- p.15 / Chapter 1.4. --- Inheritance --- p.16 / Chapter 1.5. --- Constraint --- p.18 / Chapter 1.6. --- Physical Design for OODB Storage --- p.19 / Chapter 1.7. --- Problem Description --- p.20 / Chapter 1.8. --- Genetic Algorithm --- p.22 / Chapter 1.8.1. --- Constraint Handling Methods in GA --- p.25 / Chapter 1.9. --- Contributions of this work --- p.27 / Chapter 1.10. --- Outline of this work --- p.30 / Chapter 2. --- Literature Review --- p.32 / Chapter 2.1. --- Object-oriented database --- p.32 / Chapter 2.2. --- Object-Oriented Data model --- p.33 / Chapter 2.3. --- Physical Storage Model for OODBs --- p.35 / Chapter 2.3.1. --- Home Class (HC) Model --- p.36 / Chapter 2.3.2. --- Repeated Class (RC) Model --- p.38 / Chapter 2.3.3. --- Split Instance (SI) Model --- p.39 / Chapter 2.4. --- Solving physical storage design for OODBs --- p.40 / Chapter 2.5. --- Transaction-Based Approach --- p.41 / Chapter 2.6. --- Minimize database operational cost --- p.42 / Chapter 2.7. --- Combinational Optimization Method --- p.43 / Chapter 2.8. --- Research in Genetic Algorithm --- p.46 / Chapter 2.9. --- Implementation in GA --- p.47 / Chapter 2.10. --- Fitness function --- p.49 / Chapter 2.11. --- Crossover operation --- p.50 / Chapter 2.12. --- Encoding and Representation --- p.51 / Chapter 2.13. --- Parent Selection in Crossover Operation --- p.52 / Chapter 2.14. --- Reproductive selection --- p.53 / Chapter 2.14.1. --- Selection of Crossover Operator --- p.54 / Chapter 2.14.2. --- Replacement --- p.54 / Chapter 2.15. --- The Use of Constraint Handling Method --- p.55 / Chapter 2.15.1. --- Penalty function --- p.56 / Chapter 2.15.2. --- Decoder gives instruction to build feasible solution --- p.57 / Chapter 2.15.3. --- Adjustment method --- p.58 / Chapter 3. --- Solving Physical Storage Problem for OODB using GA --- p.60 / Chapter 3.1. --- Physical storage models for OODB --- p.61 / Chapter 3.2. --- Database operation for transactions --- p.62 / Chapter 3.3. --- Properly designed physical storage structure --- p.68 / Chapter 3.4. --- Fitness Evaluation --- p.69 / Chapter 3.5. --- Initial population --- p.72 / Chapter 3.6. --- Cross-breeding --- p.72 / Chapter 3.7. --- GA Operators --- p.74 / Chapter 3.8. --- Physical Design Problem Formulation for GA --- p.75 / Chapter 3.9. --- Representation and Encoding --- p.75 / Chapter 3.10. --- Solving Physical Storage Problem for OODB in GA --- p.76 / Chapter 3.10.1. --- Representation of design solution --- p.76 / Chapter 3.10.2. --- Encoding --- p.78 / Chapter 3.10.3. --- Initial population --- p.80 / Chapter 3.10.4. --- Parent Selection for breeding --- p.80 / Chapter 3.11. --- Traditional Constraint handling method --- p.83 / Chapter 3.11.1. --- Improve the Performance of Inheritance Constraint Handling methods --- p.85 / Chapter 3.12. --- Weakness in Gorla's GA approach --- p.87 / Chapter 4. --- Proposed Methodology --- p.88 / Chapter 4.1 --- Enhanced Crossover Operator --- p.90 / Chapter 4.2. --- Infeasible Solutions and Enhanced Adjustment Method --- p.93 / Chapter 4.3. --- Propagation Adjustment Method --- p.97 / Chapter 5. --- Computational Experiments --- p.99 / Chapter 5.1. --- Introduction --- p.99 / Chapter 5.2. --- Experiment Objective --- p.101 / Chapter 5.3. --- Tools and Setup --- p.102 / Chapter 5.4. --- Crossover Operator --- p.105 / Chapter 5.5. --- Mutation Operator --- p.105 / Chapter 5.6. --- Termination condition --- p.106 / Chapter 5.7. --- Computational Experiments --- p.107 / Chapter 5.7.1. --- An Illustrative Example ´ؤ UNIVERSITY database --- p.107 / Chapter 5.7.2. --- Simulation ´ؤ 9 classes and 25 classes --- p.115 / Chapter 5.7.3. --- Result --- p.116 / Chapter 6. --- Conclusions --- p.118 / Chapter 6.1. --- Summary of Achievements --- p.118 / Chapter 7. --- Bibliography --- p.121 / Chapter 8. --- Appendix --- p.127

No description available.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 112-120). Also available via the Internet.

Thesis (M.S. in Computer Science)--Naval Postgraduate School, March 1992. / Thesis Advisor: Nelson, Michael L. "March 1992." Includes bibliographical references (p. 158-161). Also available in print.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 98-104). Also available via the Internet.

Thesis (Ph. D.), Oregon Graduate Institute of Science & Technology, 1995.

Thesis (M.S. in Computer Science) Naval Postgraduate School, March 1996. / Thesis advisor(s): David K. Hsiao, C. Thomas Wu. "March 1996." Includes bibliographical references. Also available online.