Thesis (M.Sc.)--York University, 2007. Graduate Programme in Computer Science / Typescript. Includes bibliographical references. Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR32031

Report (M.S.)--Virginia Polytechnic Institute and State University. M.S. 1993. / Abstract. Includes bibliographical references (leaf 81). Also available via the Internet.

Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2010. / Title from PDF title page (viewed on Jul. 14, 2010). Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Supervisor: Daniel P. Miranker. Vita. Includes bibliographical references.

This dissertation develops a cost model for a particular implementation of the database query language GraphLog. The order in which the subgoals of a GraphLog query are executed has a major effect on the total processing time. Our model may be used to compare the expected execution costs for different orderings of the same general query, thus, allowing us to select an efficient execution plan. We describe two cost models: one that is tailored to a specific architecture and another that is more general. Both models assume a top-down evaluation strategy. In particular, we address the issue of how to handle recursive predicates. We also provide some experimental results that confirm the validity of our work. / Graduate

We survey and compare the different major mechanisms for embedding the relational database language SQL in object-oriented programming languages such as Java and C#, with regard to how much impedance mismatch these embeddings suffer. Here impedance mismatch refers to clarity and performance difficulties that arise because of the nature of the embedding. Because of the central position in the information technology industry of object-oriented programs that access SQL-based relational database systems, reducing impedance mismatch is generally recognized in that industry as an important practical problem. We argue for the suitability of SQL as a database language, and hence for the desirability of keeping SQL as the view provided by a SQL embedding. We make the case that SQLJ, a SQL embedding for Java in which it appears that Java directly supports SQL commands, is the kind of SQL embedding that suffers the least impedance mismatch, when compared with call-level interfaces and object-relational mappings. We propose extensions to SQLJ that would reduce its impedance mismatch even further. / by Jose Luis Hurtado. / Thesis (M.S.C.S.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

by Lam Chin-keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 129-135 (2nd gp.)). / ACKNOWLEDGMENTS --- p.I / ABSTRACT --- p.II / TABLE OF CONTENTS --- p.III / LIST OF FIGURES --- p.VI / LIST OF TABLES --- p.VIII / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Motivations --- p.1 / Chapter 1.2 --- Objectives --- p.3 / Chapter 1.3 --- More to go --- p.3 / Chapter 1.4 --- Chapter Summary --- p.4 / Chapter CHAPTER 2 --- RELATED WORK --- p.6 / Chapter 2.1 --- Chinese Related Work --- p.6 / Chapter 2.1.1 --- Chinese Natural Language --- p.6 / Chapter 2.1.2 --- Chinesized Query Language From English --- p.7 / Chapter 2.2 --- High Level Database Query Language --- p.8 / Chapter 2.2.1 --- Relational Algebra vs Relational Calculus --- p.9 / Chapter 2.2.2 --- Procedural vs Declarative --- p.10 / Chapter 2.2.3 --- Natural Language (NL) vs Restricted Natural Language (RNL) --- p.11 / Chapter 2.3 --- Database Query Interface --- p.13 / Chapter 2.3.1 --- Linear Textual Interface --- p.13 / Chapter 2.3.2 --- Form-based Interface --- p.14 / Chapter 2.3.3 --- Graphical Interface --- p.14 / Chapter 2.4 --- Remarks --- p.14 / Chapter CHAPTER 3 --- DESIGN PRINCIPLES --- p.16 / Chapter 3.1 --- Underlying Data Model of the new language --- p.16 / Chapter 3.2 --- Problems Under Attack --- p.17 / Chapter 3.2.1 --- Naturalness --- p.17 / Chapter 3.2.2 --- Procedural vs Declarative --- p.19 / Chapter 3.2.3 --- Supports of Chinese Characters --- p.21 / Chapter 3.3 --- Design Principles --- p.22 / Chapter 3.4 --- Chapter Summary --- p.26 / Chapter CHAPTER 4 --- LANGUAGE DEFINITION --- p.28 / Chapter 4.1 --- Language Overvew --- p.28 / Chapter 4.2 --- The Data Manipulation Language --- p.29 / Chapter 4.2.1 --- Relational Operators --- p.30 / Chapter 4.2.2 --- Rail-Track Diagram of Chiql --- p.32 / Chapter 4.2.3 --- The 11-template --- p.33 / Chapter 4.2.4 --- Chiql Examples --- p.37 / Chapter 4.2.5 --- Common Language Constructs --- p.39 / Chapter 4.2.6 --- ONE issue about GROUP BY and RESTRICTION --- p.41 / Chapter 4.3 --- Other Language Features --- p.42 / Chapter 4.3.1 --- Aggregate Functions --- p.43 / Chapter 4.3.2 --- Attribute Alias --- p.44 / Chapter 4.3.3 --- Conditions in Chinese --- p.45 / Chapter 4.3.4 --- Unquantifed Predicates --- p.45 / Chapter 4.3.5 --- sorting --- p.47 / Chapter 4.4 --- Treatment of Quantified Predicates --- p.48 / Chapter 4.5 --- The Data Definition Language --- p.52 / Chapter 4.5.1 --- Create Table --- p.52 / Chapter 4.5.2 --- Drop Table --- p.54 / Chapter 4.5.3 --- Alter Table --- p.54 / Chapter 4.5.4 --- Insert Row --- p.56 / Chapter 4.5.5 --- Delete Row --- p.56 / Chapter 4.5.6 --- Update Row --- p.57 / Chapter 4.5.7 --- Remarks on DDL --- p.58 / Chapter 4.6 --- Chapter Summary --- p.59 / Chapter CHAPTER 5 --- END-USER INTERFACE --- p.61 / Chapter 5.1 --- EUI Overview --- p.61 / Chapter 5.2 --- Design Principles --- p.62 / Chapter 5.2.1 --- Language Independent Aspects --- p.62 / Chapter 5.2.2 --- Language Dependent Aspects --- p.64 / Chapter 5.3 --- Complex Condition Handling --- p.68 / Chapter 5.4 --- Input Sequences of the EUI --- p.71 / Chapter 5.5 --- Query Formulation： An Example --- p.73 / Chapter 5.6 --- Chapter Summary --- p.85 / Chapter CHAPTER 6 --- CHIQL TO SQL TRANSLATIONS --- p.86 / Chapter 6.1 --- Related Work --- p.87 / Chapter 6.2 --- Translation Overview --- p.87 / Chapter 6.2.1 --- "Pass One:Mapping( Input = Chiql, Output = multi-statement SQL)" --- p.89 / Chapter 6.2.2 --- "Pass Two:Nesting(Input = multi-statement SQL, Output = single statement SQL)" --- p.92 / Chapter 6.2.3 --- Technical Difficulties in Chiql/SQL Translation --- p.99 / Chapter 6.3 --- Chapter Summary --- p.106 / Chapter CHAPTER 7 --- EVALUATION --- p.108 / Chapter 7.1 --- Expressiveness Test --- p.108 / Chapter 7.1.1 --- Results --- p.109 / Chapter 7.1.2 --- Implications --- p.111 / Chapter 7.2 --- Usability Evaluation --- p.111 / Chapter 7.2.1 --- Evaluation Methodology --- p.112 / Chapter 7.2.2 --- Result:Completion Time --- p.113 / Chapter 7.2.3 --- Result: Additional Help --- p.116 / Chapter 7.2.4 --- Result： Query Error --- p.116 / Chapter 7.2.5 --- Result： Overall Score --- p.118 / Chapter 7.2.6 --- User Comments --- p.120 / Chapter 7.3 --- Chapter Summary --- p.120 / Chapter CHAPTER 8 --- CONCLUSIONS --- p.122 / Chapter 8.1 --- Thesis Conclusions --- p.122 / Chapter 8.2 --- Future Work --- p.124 / REFERENCES / APPENDIX

Leung Siu Kuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 67-68). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.1.1 --- A Story --- p.1 / Chapter 1.2 --- Overview --- p.2 / Chapter 1.2.1 --- Introduction of SQL Injection --- p.4 / Chapter 1.3 --- The importance of SQL Injection --- p.6 / Chapter 1.4 --- Thesis organization --- p.8 / Chapter 2 --- Background --- p.10 / Chapter 2.1 --- Flow of web applications using DBMS --- p.10 / Chapter 2.2 --- Structure of DBMS --- p.12 / Chapter 2.2.1 --- Tables --- p.12 / Chapter 2.2.2 --- Columns --- p.12 / Chapter 2.2.3 --- Rows --- p.12 / Chapter 2.3 --- SQL Syntax --- p.13 / Chapter 2.3.1 --- SELECT --- p.13 / Chapter 2.3.2 --- AND/OR --- p.14 / Chapter 2.3.3 --- INSERT --- p.15 / Chapter 2.3.4 --- UPDATE --- p.16 / Chapter 2.3.5 --- DELETE --- p.17 / Chapter 2.3.6 --- UNION --- p.18 / Chapter 3 --- Details of SQL Injection --- p.20 / Chapter 3.1 --- Basic SELECT Injection --- p.20 / Chapter 3.2 --- Advanced SELECT Injection --- p.23 / Chapter 3.2.1 --- Single Line Comment (--) --- p.23 / Chapter 3.2.2 --- Guessing the number of columns in a table --- p.23 / Chapter 3.2.3 --- Guessing the column name of a table (Easy one) --- p.26 / Chapter 3.2.4 --- Guessing the column name of a table (Difficult one) . --- p.27 / Chapter 3.3 --- UPDATE Injection --- p.29 / Chapter 3.4 --- Other Attacks --- p.30 / Chapter 4 --- Current Defenses --- p.32 / Chapter 4.1 --- Causes of SQL Injection attacks --- p.32 / Chapter 4.2 --- Defense Methods --- p.33 / Chapter 4.2.1 --- Defensive Programming --- p.34 / Chapter 4.2.2 --- hiding the error messages --- p.35 / Chapter 4.2.3 --- Filtering out the dangerous characters --- p.35 / Chapter 4.2.4 --- Using pre-complied SQL statements --- p.36 / Chapter 4.2.5 --- Checking for tautologies in SQL statements --- p.37 / Chapter 4.2.6 --- Instruction set randomization --- p.38 / Chapter 4.2.7 --- Building the query model --- p.40 / Chapter 5 --- Proposed Solution --- p.43 / Chapter 5.1 --- Introduction --- p.43 / Chapter 5.2 --- Natures of SQL Injection --- p.43 / Chapter 5.3 --- Our proposed system --- p.44 / Chapter 5.3.1 --- Features of the system --- p.44 / Chapter 5.3.2 --- Stage 1 - Checking with current signatures --- p.45 / Chapter 5.3.3 --- Stage 2 - SQL Server Query --- p.45 / Chapter 5.3.4 --- Stage 3 - Error Triggering --- p.46 / Chapter 5.3.5 --- Stage 4 - Alarm --- p.50 / Chapter 5.3.6 --- Stage 5 - Learning --- p.50 / Chapter 5.4 --- Examples --- p.51 / Chapter 5.4.1 --- Defensing BASIC SELECT Injection --- p.52 / Chapter 5.4.2 --- Defensing Advanced SELECT Injection --- p.52 / Chapter 5.4.3 --- Defensing UPDATE Injection --- p.57 / Chapter 5.5 --- Comparison --- p.59 / Chapter 6 --- Conclusion --- p.62 / Chapter A --- Commonly used table and column names --- p.64 / Chapter A.1 --- Commonly used table names for system management --- p.64 / Chapter A.2 --- Commonly used column names for password storage --- p.65 / Chapter A.3 --- Commonly used column names for username storage --- p.66 / Bibliography --- p.67

As we continue to produce large amounts of time-series data, the need for data analysis is growing rapidly to help gain insights from this data. These insights form the foundation of data-driven decisions in various aspects of life. Data annotations are information about the data such as comments, errors and provenance, which provide context to the underlying data and aid in meaningful data analysis in domains such as scientific research, genomics and ECG analysis. Storing such annotations in the database along with the data makes them available to help with analysis of the data. In this thesis, I propose a user-friendly technique for Annotation-Enabled Analysis through which a user can employ annotations to help query and analyze data without having prior knowledge of the details of the database schema or any kind of database programming language. The proposed technique receives the request for analysis as a high-level specification, hiding the details of the schema, joins, etc., and parses it, validates the input and converts it into SQL. This SQL query can then be executed in a relational database and the result of the query returned to the user. I evaluate this technique by providing real-world data from a building-data platform containing data about Portland State University buildings such as room temperature, air volume and CO2 level. This data is annotated with information such as class schedules, power outages and control modes (for example, day or night mode). I test my technique with three increasingly sophisticated levels of use cases drawn from this building science domain. (1) Retrieve data with include or exclude annotation selection (2) Correlate data with include or exclude annotation selection (3) Align data based on include annotation selection to support aggregation over multiple periods. I evaluate the technique by performing two kinds of tests: (1) To validate correctness, I generate synthetic datasets for which I know the expected result of these annotation-enabled analyses and compare the expected results with the results generated from my technique (2) I evaluate the performance of the queries generated by this service with respect to execution time in the database by comparing them with alternative SQL translations that I developed.

Thesis (M.Sc.)--York University, 2008. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 147-152). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR38782