Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2006. / Title from PDF title page (viewed on May 25, 2006). Includes bibliographical references.

Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2006. / Title from PDF title page (viewed on May 25, 2006). Includes bibliographical references.

Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2006. / Title from PDF title page (viewed on May 25, 2006). Includes bibliographical references.

Education is commonly seen as an escape from poverty and a critical path to securing a better standard of living. This is especially relevant in the South African context, where the need is so great that in one instance people were trampled to death at the gates of a higher educational institution, whilst attempting to register for this opportunity. The root cause of this great need is a limited capacity and a demand, which outstrips the supply. This is not a problem specific to South Africa. It is however exaggerated in the South African context due to the country's lack of infrastructure and the opening of facilities to all people. Tertiary educational institutions are faced with ever-increasing applications for a limited number of available positions. This study focuses on a dataset from the Nelson Mandela Metropolitan University's Faculty of Engineering, the Built Environment and Information Technology - with the aim of establishing guidelines for the use of data modelling techniques to improve student admissions criteria. The importance of data preprocessing was highlighted and generalized linear regression, decision trees and neural networks were proposed and motivated for modelling. Experimentation was carried out, resulting in a number of recommended guidelines focusing on the tremendous value of feature engineering coupled with the use of generalized linear regression as a base line. Adding multiple models was highly recommended; since it allows for greater opportunities for added insight.

"No matter how good a portfolio manager is, she or he can not makes right investment decisions without the right information. It is all about data: how can many megabytes of data must be loaded into a continuously growing system, stored efficiently, and made easily accessible to all queries and to all applications? In this project, we build a decision database for managing a portfolio of fixed-income investments. We review the key features of the database architecture and describe key steps in processing the available date. In addition, we review some common analyses that are done by the portfolio manager by studying the report needed for a study of the investment duration at the sector level. "

No description available.

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

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 128-134).

Thesis (Ph.D.)--OGI School of Science & Engineering at OHSU, 2003. / Includes bibliographical references (leaves 174-181).

A database schema should consist of structures and semantic integrity constraints. Se mantic integrity constraints (SICs) are invariant restrictions on the static states of the stored data and the state transitions caused by the primitive operations: insertion, dele tion, or update. Traditionally, database design has been carried out on an ad hoc basis and focuses on structure and efficiency. Although the E-R model is the popular concep tual modelling tool, it contains few inherent SICs. Also, although the relational database model is the popular logical data model, a relational database in fourth or fifth normal form may still represent little of the data semantics. Most integrity checking is distributed to the application programs or transactions. This approach to enforcing integrity via the application software causes a number of problems. Recently, a number of systems have been developed for assisting the database design process. However, only a few of those systems try to help a database designer incorporate SICs in a database schema. Furthermore, current SIC representation languages in the literature cannot be used to represent precisely the necessary features for specifying declarative and operational semantics of a SIC, and no modelling tool is available to incorporate SICs. This research solves the above problems by presenting two models and one subsystem. The E-R-SIC model is a comprehensive modelling tool for helping a database designer in corporate SICs in a database schema. It is application domain-independent and suitable for implementation as part of an automated database design system. The SIC Repre sentation model is used to represent precisely these SICs. The SIC elicitation subsystem would verify these general SICs to a certain extent, decompose them into sub-SICs if necessary, and transform them into corresponding ones in the relational model. A database designer using these two modelling tools can describe more data semantics than with the widely used relational model. The proposed SIC elicitation subsystem can provide more modelling assistance for him (her) than current automated database design systems.