Photocopy of typescript. / Digitized by Kansas Correctional Industries

Thesis. 1975. M.S.--Massachusetts Institute of Technology. Alfred P. Sloan School of Management. / Bibliography: leaves 152-155. / by Grant N Smith. / M.S.

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.)--York University, 2004. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 230-238). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ99202

Bioinformatics resources should provide simple and flexible support for genomics research. A huge amount of gene mapping data, micro-array expression data, expressed sequence tags (EST), BAC sequence data and genome sequence data are already, or will soon be available for a number of livestock species. These species will have different requirements compared to typical biomedical model organisms and will need an informatics framework to deal with the data. In term of exploring complex-intertwined genomic data, the way to organize them will be addressed in this study. Therefore, we investigated two issues in this study: one is an independent informatics framework including both back end and front end; another is how an informatics framework simplifies the user interface to explore data. We have developed a fundamental informatics framework that makes it easy to organize and manipulate the complex relations between genomic data, and allow for query results to be presented via a user friendly web interface. A genome object-oriented framework (GOOF) was proposed with object-oriented Java technology and is independent of any database system. This framework seamlessly links the database system and web presentation components. The data models of GOOF collect the data relationships in order to provide users with access to relations across different types of data, meaning that users avoid constructing queries within the interface layer. Moreover, the module-based interface provided by GOOF could allow different users to access data in different interfaces and ways. In another words, GOOF not only gives a whole solution to informatics infrastructure, but also simplifies the organization of data modeling and presentation. In order to be a fast development solution, GOOF provides an automatic code engine by using meta-programming facilities in Java, which could allow users to generate a large amount of routine program codes. Moreover, the pre-built data layer in GOOF connecting with Chado simplifies the process to manage genomic data in the Chado schema. In summary, we studied the way to model genomic data into an informatics framework, a one-stop approach, to organize the data and addressed how GOOF constructs a bioinformatics infrastructure for users to access genomic data.

Aim: The aim of this research has been to answer the question of how and based on what combination of grand strategies, Oracle as a high-tech company, developed and improved its business and affected its competitors and other industries. Method: Analytical study of published literature and Oracle internal annual reports combined with personal experience of working with Oracle products and observing its strategies for more than a decade have been the essential part of this study. The main method of data collection was gathering information from those websites which include information and news around Oracle strategic movements. This research has applied that information to deliver an analysis of not only the Oracle current strategy, but its strategy for the future and tendency to expand its market and direct the information industry, moving forward. Result & Conclusion: Being around for three decades, Oracle has had a great history of successful strategies with introducing innovation, leading software industry, and directing the future of information technology. It has been survived from many uncertainties as the nature of its business. Knowing its competitors, spending billions of dollars on research and development, filling the gaps of having the best products through acquisition, and creating valuable products and services for many industries, has brought Oracle on the top in its area. Its vision of sharing information and giving the world wide access to a comprehensive knowledge of using its products and services has increased loyalty in its customers and business partners. Oracle is a crucial company in this information age, and its products and solutions have been critically useful for a wide range of businesses. These are the facts which have been concluded in this study. Contribution of the study: Understanding of Oracle strategy is important for millions of managers, developers, and users of database systems and enterprise applications throughout the world. Being successful in their career and professional positions, they have to know where the information technology is moving and they have no choice of using those software products which a major part of it, is created or at least directed by Oracle. It can guarantee the future of information management in a company.

Virtual machine technologies offer simple and practical mechanisms to address many manageability problems in database systems. For example, these technologies allow for server consolidation, easier deployment, and more flexible provisioning. Therefore, database systems are increasingly being run on virtual machines. This offers many unique opportunities for database research. However, it is also important to understand the cost of virtualization. Virtual machine technologies add a layer of indirection between applications and the hardware that they use (e.g. CPU, memory, disk). This added complexity results in a performance overhead for software systems running in a virtual machine. In this thesis, we present an experimental study of the overhead of running a database workload in a virtual machine. Using a TPC-H workload running on PostgreSQL in a Xen virtual machine environment, we show that Xen does indeed introduce overhead for system calls, page fault handling, and disk I/O. However, these overheads do not translate to a high overhead in query execution time. We show that in all cases the average overhead is less than 10% and, therefore, conclude that the advantages of running a database system in a virtual machine do not come at a high cost in performance.

Virtual machine technologies offer simple and practical mechanisms to address many manageability problems in database systems. For example, these technologies allow for server consolidation, easier deployment, and more flexible provisioning. Therefore, database systems are increasingly being run on virtual machines. This offers many unique opportunities for database research. However, it is also important to understand the cost of virtualization. Virtual machine technologies add a layer of indirection between applications and the hardware that they use (e.g. CPU, memory, disk). This added complexity results in a performance overhead for software systems running in a virtual machine. In this thesis, we present an experimental study of the overhead of running a database workload in a virtual machine. Using a TPC-H workload running on PostgreSQL in a Xen virtual machine environment, we show that Xen does indeed introduce overhead for system calls, page fault handling, and disk I/O. However, these overheads do not translate to a high overhead in query execution time. We show that in all cases the average overhead is less than 10% and, therefore, conclude that the advantages of running a database system in a virtual machine do not come at a high cost in performance.

Bioinformatics resources should provide simple and flexible support for genomics research. A huge amount of gene mapping data, micro-array expression data, expressed sequence tags (EST), BAC sequence data and genome sequence data are already, or will soon be available for a number of livestock species. These species will have different requirements compared to typical biomedical model organisms and will need an informatics framework to deal with the data. In term of exploring complex-intertwined genomic data, the way to organize them will be addressed in this study. Therefore, we investigated two issues in this study: one is an independent informatics framework including both back end and front end; another is how an informatics framework simplifies the user interface to explore data. We have developed a fundamental informatics framework that makes it easy to organize and manipulate the complex relations between genomic data, and allow for query results to be presented via a user friendly web interface. A genome object-oriented framework (GOOF) was proposed with object-oriented Java technology and is independent of any database system. This framework seamlessly links the database system and web presentation components. The data models of GOOF collect the data relationships in order to provide users with access to relations across different types of data, meaning that users avoid constructing queries within the interface layer. Moreover, the module-based interface provided by GOOF could allow different users to access data in different interfaces and ways. In another words, GOOF not only gives a whole solution to informatics infrastructure, but also simplifies the organization of data modeling and presentation. In order to be a fast development solution, GOOF provides an automatic code engine by using meta-programming facilities in Java, which could allow users to generate a large amount of routine program codes. Moreover, the pre-built data layer in GOOF connecting with Chado simplifies the process to manage genomic data in the Chado schema. In summary, we studied the way to model genomic data into an informatics framework, a one-stop approach, to organize the data and addressed how GOOF constructs a bioinformatics infrastructure for users to access genomic data.