This research is concerned with inheritance as used in object-oriented database. More specifically, partial bi-directional inheritance among classes is examined. In partial inheritance, a class can inherit a proper subset of instance variables from another class. Two subclasses of the same superclass do not need to inherit the same proper subset of instance variables from their superclass. Bi-directional partial inheritance allows a class to inherit instance variables from its subclass. The prototype of an object-oriented database that supports both full and partial bi-directional inheritance among classes was developed on top of an existing relational database management system. The prototype was tested with two database applications. One database application needs full and partial inheritance. The second database application required bi-directional inheritance. The result of this testing suggests both advantages and disadvantages of partial bi-directional inheritance. Future areas of research are also suggested.

Collaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendations from highly sparse user rating data. My contributions to this research topic include proposing the frameworks of imputation-boosted collaborative filtering (IBCF) and imputed neighborhood based collaborative filtering (INCF). We also proposed a model-based CF technique, TAN-ELR CF, and two hybrid CF algorithms, sequential mixture CF and joint mixture CF. Empirical results show that our proposed CF algorithms have very good predictive performances. In the investigation of applying imputation techniques in mining incomplete data, we proposed imputation-helped classifiers, and VCI predictors (voting on classifications from imputed learning sets), both of which resulted in significant improvement in classification performance for incomplete data over conventional machine learned classifiers, including kNN, neural network, one rule, decision table, SVM, logistic regression, decision tree (C4.5), random forest, and decision list (PART), and the well known Bagging predictors. The main imputation techniques involved in these algorithms include EM (expectation maximization) and BMI (Bayesian multiple imputation). / by Xiaoyuan Su. / Vita. / Thesis (Ph.D.)--Florida Atlantic University, 2008. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.

In this thesis, we explore the issues of uncertain data management in several different aspects. First, we propose a novel linear time algorithm to compute the positional probability, the computation of which is a primitive operator for most of the ranking definitions. Our algorithm is based on the conditional probability formulation of positional probability and the system of linear equations. Based on the formulation of conditional probability, we also prove a tight upper bound of the top-k probability of tuples, which is then used to stop the top-k computation earlier. Second, we study top-k probabilistic ranking queries with joins when scores and probabilities are stored in different relations. We focus on reducing the join cost in probabilistic top-k ranking. We investigate two probabilistic score functions, namely, expected rank value and probability of highest ranking. We give upper/lower bounds of such probabilistic score functions in random access and sequential access, and propose new I/O efficient algorithms to find top-k objects. Third, we extend the possible worlds semantics to probabilistic XML ranking query, which is to rank top-k probabilities of the answers of a twig query in probabilistic XML data. The new challenge is how to compute top-k probabilities of answers of a twig query in probabilistic XML in the presence of containment (ancestor/descendant) relationships. We focus on node queries first, and propose a new dynamic programming algorithm which can compute top-k probabilities for the answers of node queries based on the previously computed results in probabilistic XML data. We further propose optimization techniques to share the computational cost. We also show techniques to support path queries and tree queries. Fourth, we study how to rank documents using a set of keywords, given a context that is associated with the documents. We model the problem using a graph with two different kinds of nodes (document nodes and multi-attribute nodes), where the edges between document nodes and multi-attribute nodes exist with some probability. We discuss its score function, cost function, and ranking with uncertainty. We also propose new algorithms to rank documents that are most related to the user-given keywords by integrating the context information. / Uncertain data management has received a lot of attentions recently due to the fact that data obtained can be incomplete or uncertain in many real applications. Ranking of uncertain data becomes an important research issue, the possible worlds semantics-based ranking makes it different from the ranking of deterministic data. In the traditional deterministic data, we can compute a score for each object, and then the objects are ranked based on the computed scores. However, in the scenario of uncertain data, each object has a probability to be the true answer (or the existence probability), besides the computed score. A probabilistic top-k ranking query ranks objects by the interplay of score and probability based on the possible worlds semantics. Many definitions have been proposed in the literature based on the possible worlds semantics. / Chang, Lijun. / Advisers: Hong Cheng; Jeffrey Xu Yu. / Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 131-139). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

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

Yuen, Sze Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves [44]-46). / Abstract also in Chinese. / Thesis Committee --- p.i / Abstract --- p.ii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Related Work --- p.6 / Chapter 2.1 --- Nearest Neighbor Search on Precise Data --- p.6 / Chapter 2.2 --- NN Search on Uncertain Data --- p.8 / Chapter 3 --- Problem Definitions and Basic Characteristics --- p.11 / Chapter 4 --- The Full-Graph Approach --- p.16 / Chapter 5 --- The Pipeline Approach --- p.19 / Chapter 5.1 --- The Algorithm --- p.20 / Chapter 5.2 --- Edge Phase --- p.24 / Chapter 5.3 --- Pruning Phase --- p.27 / Chapter 5.4 --- Validating Phase --- p.28 / Chapter 5.5 --- Discussion --- p.29 / Chapter 6 --- Extension --- p.31 / Chapter 7 --- Experiment --- p.34 / Chapter 7.1 --- Properties of the SNN-core --- p.34 / Chapter 7.2 --- Efficiency of Our Algorithms --- p.38 / Chapter 8 --- Conclusions and Future Work --- p.42 / Chapter A --- List of Publications --- p.43 / Bibliography --- p.44

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"Many approaches to the visualization of multivariate data have been proposed to date. Pixel oriented techniques map each attribute value of the data to a single colored pixel, theoretically yielding the display of the maximum possible information at a time. A large number of pixel layout methods have been proposed, each of which enables users to perform their visual exploration tasks to varying degrees. Pixel oriented techniques typically maintain the global view of large amounts of data while still preserving the perception of small regions of interest, which makes them particularly interesting for visualizing very large multidimensional data sets. Pixel based methods also provide feedback on the given query by presenting not only the data items fulfilling the query but also the data that approximately fulfill the query. The goal of this thesis was to extend XmdvTool, a public domain multivariate data visualization package, to incorporate pixel based techniques and to explore their strengths and weaknesses. The main challenge here was to seamlessly apply the interaction and distortion techniques used in other visualization methods within XmdvTool to pixel based methods and investigate the capabilities made possible by fusing the various multivariate visualization techniques."

"Modern computer applications from business decision support to scientific data analysis use visualization techniques. However, visual exploration tools do not scale well for large data sets, i.e., the level of clutter on the screen is typically unacceptable. To solve the problem of cluttering at the interface level, visualization tools have recently been extended to support hierarchical views of the data, with support for focusing and drilling-down using interactive selection. To solve the scalability problem, we now investigate how best to couple such a near real-time responsive visualization tool with a database management system. Our solution proposes a framework containing three major components: hierarchy encoding, caching and prefetching. Since the direct implementation of the visual user interactions on hierarchical data sets corresponds to recursive query processing, we have developed a hierarchy encoding method, called the MinMax tree, that pushes the on-line recursive processing step into an off-line precomputation step. The MinMax encoding scheme allows us to map the hierarchy to a 2-dimensional space and the recursive navigation operations at the interface level to 2-dimensional spatial range queries. These queries can then be answered efficiently using spatial indexes. To compliment this encoding scheme we employ a caching strategy that exploits user navigation characteristics to cache the nodes having high probability of being referenced again. Based on user characteristics we choose to implement two replacement policies one which exploits temporal locality (LRU) and the other exploits spatial locality (Distance). Also, to enhance the performance of the cache we propose using a prefetching mechanism that predicts and prefetches future user requests into the cache. Together the components form a comprehensive framework that scales the visualization tool to support navigation operations over large data sets. The techniques have been incorporated into XmdvTool, a free software package for multi-variate data visualization and exploration. Our experimental results quantify the effectiveness of each component and show that collectively the components scale the XmdvTool to support navigation operations over large data sets. Mainly, our experimental results show that together the components can achieve 63\% to 96\%reduction in response time latency even with limited system resources."

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Knowing which database management system to use for a project is difficult to know in advance. Luckily, there are tools that can help the developer apply the same database design on multiple different database management systems without having to change the code. In this thesis, we investigate the strengths of SQLAlchemy, which is an SQL toolkit for Python. We compared SQLite, PostgreSQL and MySQL using SQLAlchemy as well as compared a pure MySQL implementation against the results from SQLAlchemy. We conclude that, for our database design, PostgreSQL was the best database management system and that for the average SQL-user, SQLAlchemy is an excellent substitution to writing regular SQL.