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An Investigation of the Security Designs of a Structured Query Language (SQL) Database and its Middleware Application and their Secure Implementation within Thinclient EnvironmentsWinner-Leoni, Melissa D. January 2008 (has links) (PDF)
Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2008. / Title from PDF title page (viewed on Feb. 09, 2009). Includes bibliographical references.
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Advanced structured query language instruction for engineers of the Office of Information Technology at Brigham Young University /Rackliffe, Vincent B. January 2005 (has links) (PDF)
Project (M.S.)--Brigham Young University. Dept. of Instructional Psychology and Technology, 2005. / Includes bibliographical references (p. 46-47).
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Physical Plan Instrumentation in Databases: Mechanisms and ApplicationsPsallidas, Fotis January 2019 (has links)
Database management systems (DBMSs) are designed with the goal set to compile SQL queries to physical plans that, when executed, provide results to the SQL queries. Building on this functionality, an ever-increasing number of application domains (e.g., provenance management, online query optimization, physical database design, interactive data profiling, monitoring, and interactive data visualization) seek to operate on how queries are executed by the DBMS for a wide variety of purposes ranging from debugging and data explanation to optimization and monitoring. Unfortunately, DBMSs provide little, if any, support to facilitate the development of this class of important application domains. The effect is such that database application developers and database system architects either rewrite the database internals in ad-hoc ways; work around the SQL interface, if possible, with inevitable performance penalties; or even build new databases from scratch only to express and optimize their domain-specific application logic over how queries are executed.
To address this problem in a principled manner in this dissertation, we introduce a prototype DBMS, namely, Smoke, that exposes instrumentation mechanisms in the form of a framework to allow external applications to manipulate physical plans. Intuitively, a physical plan is the underlying representation that DBMSs use to encode how a SQL query will be executed, and providing instrumentation mechanisms at this representation level allows applications to express and optimize their logic on how queries are executed.
Having such an instrumentation-enabled DBMS in-place, we then consider how to express and optimize applications that rely their logic on how queries are executed. To best demonstrate the expressive and optimization power of instrumentation-enabled DBMSs, we express and optimize applications across several important domains including provenance management, interactive data visualization, interactive data profiling, physical database design, online query optimization, and query discovery. Expressivity-wise, we show that Smoke can express known techniques, introduce novel semantics on known techniques, and introduce new techniques across domains. Performance-wise, we show case-by-case that Smoke is on par with or up-to several orders of magnitudes faster than state-of-the-art imperative and declarative implementations of important applications across domains.
As such, we believe our contributions provide evidence and form the basis towards a class of instrumentation-enabled DBMSs with the goal set to express and optimize applications across important domains with core logic over how queries are executed by DBMSs.
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A fuzzy database query system with a built-in knowledge base.January 1995 (has links)
by Chang Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 111-115). / Acknowledgement --- p.i / Abstract --- p.ii / List of Tables --- p.vii / List of Figures --- p.viii / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Outline of the Work of This Thesis --- p.4 / Chapter 1.3 --- Organization of the Thesis --- p.5 / Chapter 2 --- REVIEW OF RELATED WORKS --- p.6 / Chapter 2.1 --- Deduce2 --- p.6 / Chapter 2.2 --- ARES --- p.8 / Chapter 2.3 --- VAGUE --- p.10 / Chapter 2.4 --- Fuzzy Sets-Based Approaches --- p.12 / Chapter 2.5 --- Some General Remarks --- p.14 / Chapter 3 --- A FUZZY DATABASE QUERY LANGUAGE --- p.18 / Chapter 3.1 --- Basic Concepts of Fuzzy Sets --- p.18 / Chapter 3.2 --- The Syntax of the Fuzzy Query Language --- p.21 / Chapter 3.3 --- Fuzzy Operators --- p.25 / Chapter 3.3.1 --- AND --- p.27 / Chapter 3.3.2 --- OR --- p.27 / Chapter 3.3.3 --- COMB --- p.28 / Chapter 3.3.4 --- POLL --- p.28 / Chapter 3.3.5 --- HURWICZ --- p.30 / Chapter 3.3.6 --- REGRET --- p.31 / Chapter 4 --- SYSTEM DESIGN --- p.35 / Chapter 4.1 --- General Requirements and Definitions --- p.35 / Chapter 4.1.1 --- Requirements of the system --- p.36 / Chapter 4.1.2 --- Representation of membership functions --- p.38 / Chapter 4.2 --- Overall Architecture --- p.41 / Chapter 4.3 --- Interface --- p.44 / Chapter 4.4 --- Knowledge Base --- p.46 / Chapter 4.5 --- Parser --- p.51 / Chapter 4.6 --- ORACLE --- p.52 / Chapter 4.7 --- Data Manager --- p.53 / Chapter 4.8 --- Fuzzy Processor --- p.57 / Chapter 5 --- IMPLEMENTION --- p.59 / Chapter 5.1 --- Some General Considerations --- p.59 / Chapter 5.2 --- Knowledge Base --- p.60 / Chapter 5.2.1 --- Converting a concept into conditions --- p.60 / Chapter 5.2.2 --- Concept trees --- p.62 / Chapter 5.3 --- Data Manager --- p.64 / Chapter 5.3.1 --- Some issues on the implementation --- p.64 / Chapter 5.3.2 --- Dynamic library --- p.67 / Chapter 5.3.3 --- Precompiling process --- p.68 / Chapter 5.3.4 --- Calling standard --- p.71 / Chapter 6 --- CASE STUDIES --- p.76 / Chapter 6.1 --- A Database for Job Application/Recruitment --- p.77 / Chapter 6.2 --- Introduction to the Knowledge Base --- p.79 / Chapter 6.3 --- Cases --- p.79 / Chapter 6.3.1 --- Crispy queries --- p.79 / Chapter 6.3.2 --- Fuzzy queries --- p.82 / Chapter 6.3.3 --- Concept queries --- p.85 / Chapter 6.3.4 --- Fuzzy Match --- p.87 / Chapter 6.3.5 --- Fuzzy operator --- p.88 / Chapter 7 --- CONCLUSION --- p.93 / Appendix A Sample Data in DATABASE --- p.96 / Bibliography --- p.111
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An extended query language for a temporal relational database.January 1990 (has links)
by Chat Siu Wing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 104-107. / ABSTRACT --- p.i / ACKNOWLEDGEMENTS --- p.ii / TABLE OF CONTENTS --- p.iii / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter CHAPTER 2 --- TEMPORAL DATABASES --- p.5 / Chapter 2.1 --- The Importance of Temporal Data --- p.5 / Chapter 2.2 --- Incorporating Time in Databases --- p.6 / Chapter 2.2.1 --- Time Dimensions --- p.6 / Chapter 2.2.2 --- Classification --- p.6 / Chapter 2.2.2.1 --- Snapshot Databases --- p.6 / Chapter 2.2.2.2 --- Rollback Databases --- p.7 / Chapter 2.2.2.3 --- Historical Databases --- p.8 / Chapter 2.2.2.4 --- Temporal Databases --- p.9 / Chapter 2.2.3 --- Current Research Areas --- p.9 / Chapter 2.2.3.1 --- Time Semantics at the Conceptual Level --- p.10 / Chapter 2.2.3.2 --- Temporal Data Model --- p.11 / Chapter 2.2.3.3 --- Temporal Query Language --- p.11 / Chapter CHAPTER 3 --- CONCEPTUAL TEMPORAL DATA MODELING …… --- p.13 / Chapter 3.1 --- The Time Generic --- p.13 / Chapter 3.1.1 --- Time Unit --- p.14 / Chapter 3.1.2 --- Interval --- p.15 / Chapter 3.1.3 --- Periodic Time --- p.16 / Chapter 3.1.4 --- Time Point --- p.17 / Chapter 3.2 --- Extended Entity Relationship (EER) Model --- p.17 / Chapter 3.2.1 --- Attribute --- p.18 / Chapter 3.2.2 --- Entity --- p.22 / Chapter 3.2.3 --- Relationship --- p.23 / Chapter 3.2.4 --- Event --- p.23 / Chapter 3.3 --- EER Modeling of Temporal Data --- p.25 / Chapter 3.3.1 --- Classify entities and attributes --- p.25 / Chapter 3.3.2 --- Define events --- p.26 / Chapter 3.3.3 --- Define relationships --- p.27 / Chapter 3.3.4 --- Classify Attributes --- p.27 / Chapter CHAPTER 4 --- LOGICAL TEMPORAL DATABASE DESIGN --- p.28 / Chapter 4.1 --- Embedding a Temporal Relation into a Snapshot Relation --- p.28 / Chapter 4.2 --- The Proposed Temporal Relational Model --- p.29 / Chapter 4.2.1 --- Extension to Relational Model --- p.30 / Chapter 4.2.2 --- Extended Properties --- p.31 / Chapter 4.2.3 --- Extended Information Contents --- p.32 / Chapter 4.2.3.1 --- Different Temporal Information --- p.32 / Chapter 4.2.3.2 --- Retroactive and Postactive Recording --- p.33 / Chapter 4.2.3.3 --- Multiple Values at an Instant --- p.33 / Chapter 4.2.3.4 --- Discrete Valid Intervals --- p.34 / Chapter 4.2.4 --- Data Manipulation --- p.34 / Chapter 4.2.4.1 --- Retrieval --- p.35 / Chapter 4.2.4.2 --- Updating --- p.36 / Chapter 4.2.5 --- Probable Undesirable Properties --- p.37 / Chapter 4.2.5.1 --- Redundancy --- p.37 / Chapter 4.2.5.2 --- Update Anomalies --- p.39 / Chapter 4.2.5.3 --- Retrieval Anomalies --- p.39 / Chapter 4.3 --- Mapping Conceptual to Temporal Relational Model --- p.40 / Chapter 4.3.1 --- Relations For the Time Generic --- p.40 / Chapter 4.3.1.1 --- Interval --- p.40 / Chapter 4.3.1.2 --- Time Point --- p.41 / Chapter 4.3.1.3 --- Periodic Time --- p.42 / Chapter 4.3.2 --- Mapping History into Time Attributes --- p.42 / Chapter 4.3.2.1 --- Attribute History --- p.42 / Chapter 4.3.2.2 --- Existence History --- p.43 / Chapter 4.3.3 --- Mapping Entity Type --- p.43 / Chapter 4.3.3.1 --- Strong Entity --- p.43 / Chapter 4.3.3.2 --- Weak Entity --- p.46 / Chapter 4.3.3.3 --- Temporally Weak Entity --- p.46 / Chapter 4.3.4 --- Mapping Event Type --- p.46 / Chapter 4.3.5 --- Mapping Relationship Type --- p.48 / Chapter 4.4 --- Joining Synchronous Relations --- p.49 / Chapter 4.5 --- Integrity Constraints --- p.50 / Chapter 4.5.1 --- Creation --- p.51 / Chapter 4.5.2 --- Deletion --- p.51 / Chapter 4.5.3 --- Modification of Past States --- p.52 / Chapter CHAPTER 5 --- A TEMPORAL QUERY LANGUAGE - TempSQL --- p.53 / Chapter 5.1 --- New Statements --- p.53 / Chapter 5.2 --- New Constructs in Statements --- p.54 / Chapter 5.2.1 --- Temporal Operators --- p.54 / Chapter 5.2.2 --- Temporal Comparison Operators --- p.55 / Chapter 5.2.3 --- WHEN Clause --- p.56 / Chapter 5.2.4 --- AS OF clause --- p.58 / Chapter 5.2.5 --- VALID clause --- p.58 / Chapter 5.2.6 --- A General Example --- p.60 / Chapter 5.3 --- Semantics of TempSQL Statements --- p.61 / Chapter 5.3.1 --- SELECT --- p.62 / Chapter 5.3.2 --- INSERT --- p.64 / Chapter 5.3.3 --- DISCARD --- p.66 / Chapter 5.3.4 --- UPDATE --- p.68 / Chapter CHAPTER 6 --- IMPLEMENTATION OF TempSQL --- p.78 / Chapter 6.1 --- The Underlying Environment --- p.78 / Chapter 6.2 --- The Preprocessor --- p.79 / Chapter 6.3 --- The Interactive Interpreter --- p.81 / Chapter 6.4 --- Limitations --- p.82 / Chapter CHAPTER 7 --- AN EXAMPLE TEMPORAL DATABASE --- p.84 / Chapter 7.1 --- The Scenario --- p.84 / Chapter 7.2 --- EER modeling of data --- p.84 / Chapter 7.3 --- Transformation into Temporal Relations --- p.85 / Chapter 7.4 --- Joining Synchronous Relations --- p.87 / Chapter 7.5 --- Sample Queries --- p.87 / Chapter 7.6 --- Remarks --- p.91 / Chapter CHAPTER 8 --- CONCLUSION AND FUTURE RESEARCH / DIRECTIONS --- p.94 / Chapter APPENDIX A --- BNF of TempSQL --- p.100 / REFERENCES --- p.104
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Designing a multimedia query interface for casual users.January 1994 (has links)
by Fong Siu-kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 73-77). / Abstract --- p.1 / Chapter 1. --- Introduction --- p.2 / Chapter 2. --- Background and Related Work --- p.5 / Chapter 2.1 --- Requirements of a Good Query Language /Interface --- p.5 / Chapter 2.2 --- Casual versus Frequent Users --- p.6 / Chapter 2.3 --- Graphical User Interface --- p.8 / Chapter 2.4 --- Windowing --- p.10 / Chapter 2.5 --- Use of Voice in User Interface --- p.11 / Chapter 2.6 --- Related Work --- p.12 / Chapter 2.6.1 --- Examples of Query Interface Designs in the Literature --- p.13 / Chapter 2.6.2 --- Examples of Query Interfaces in Commercial Packages --- p.15 / Chapter 3. --- Interface Design Concepts --- p.17 / Chapter 3.1 --- Data Model --- p.18 / Chapter 3.2 --- General Guidelines on Interface Design --- p.19 / Chapter 3.3 --- Divide and Conquer Strategy --- p.21 / Chapter 3.4 --- Unit of Operation --- p.24 / Chapter 3.5 --- The Second Clicking Principle --- p.26 / Chapter 3.6 --- Use of Voice in the Interface --- p.28 / Chapter 3.7 --- Customization of User Level --- p.29 / Chapter 4. --- Interface Specification and implementation --- p.30 / Chapter 4.1 --- System Menu --- p.31 / Chapter 4.2 --- ER Diagram and Tables Window --- p.33 / Chapter 4.3 --- Overview on R Window and Result Icon Window --- p.36 / Chapter 4.4 --- Choose Fields Operation --- p.38 / Chapter 4.5 --- Choose Rows Operation --- p.41 / Chapter 4.6 --- Combine Tables Operation --- p.45 / Chapter 4.7 --- For Each Group Operation --- p.49 / Chapter 4.8 --- Set Operations --- p.50 / Chapter 4.9 --- Decomposition and Recomposition of Queries --- p.51 / Chapter 5. --- Example of Application for a Complex Query --- p.54 / Chapter 6. --- Help Facilities and Error Handling --- p.63 / Chapter 6.1 --- Help Function --- p.64 / Chapter 6.2 --- Error Diagnosis --- p.66 / Chapter 7. --- Summary and Conclusion --- p.69 / Bibliography --- p.73 / Appendix --- p.78
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Order-sensitive XML query processing over relational sourcesMurphy, Brian R. January 2003 (has links)
Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: computation pushdown; XML; order-based Xquery processing; relational database; ordered SQL queries; data model mapping; XQuery; XML data mapping; SQL; XML algebra rewrite rules; XML document order. Includes bibliographical references (p. 64-67).
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Active learning and compilation of higher order schema integration queriesBarbanson, François Gérard 28 August 2008 (has links)
Not available / text
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Optimization of SQL queries for parallel machines /Hasan, Waqar. January 1996 (has links)
CA, Univ., Diss--Stanford.
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SQLSDM : a SQL front-end semantic data model /Lodico, Marc Richard. January 1989 (has links)
Thesis (M.S.)--Rochester Institute of Technology, 1989. / Includes bibliographical references (leaves 67-68).
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