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The performance of an interactive graphics/computer aided design system, such as the IC layout and rule checking package implemented as part of the schematic drawing application on the Gerber Systems Technology IDS-80, is very closely related to the performance of the system's data base. This is due to the fact that most editing functions on an interactive graphics/CAD system are data base intensive functions and the data base and its management routines form one of the major building blocks of a CAD system. It can therefore be said that the performance of a CAD system is directly dependent on the data base access time and the efficiency of the managing routines. The primary objective of this project was to enhance the performance of the IC layout and rule checking package. This was done by improving the performance of the data base of the system. This was achieved by following two mutually supportive paths. The first was the transportation of the software to a new host machine which had a 32-bit processor and virtual memory capabilities. The second was to try and improve the performance of the transported data base by utilising sophisticated data base structures and memory management·techniques facilitated by the larger available memory of the new host to optimise the data base operations. The effectiveness of the two paths in achieving their respective goals was evaluated using evaluation programs which simulated characteristic data base activities.This thesis documents the above process, as well as expounds on some of the background related theory which was instrumental in the progress of the project and the drawing of the final conclusions. / Thesis (M.Sc.)-University of Natal, Durban, 1987.

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The relational model has dominated the database field because of its reduced application development time and non-procedural data manipulation features. It has significant problems, however, including weak integrity constraints. This paper discusses the need for object oriented techniques to improve on these flaws. Three existing DBMS will be discussed: IRIS, ORION, and OZ. / Department of Computer Science

The purpose of this study is to explore that mathematics provides a convenient formalism for studying classical database management system problems. There are two main parts in this study, devoted respectively to using mathematical theory of relations and using logical theory to study database management systems. In the first part we focus on relational model and relational algebra. The second part deals with the application of mathematical logic to database management systems, where logic may be used both as a inference system and as a representation language. The features and logical mechanisms of Prolog programming language have been studied. A sample logical database model is developed and tested, using the logic programming language Prolog. / Department of Computer Science

Skyline has been proposed as an important operator for many applications, such as multi-criteria decision making, data mining and visualization, and user-preference queries. Due to its importance, skyline and its computation have received considerable attention from database research community recently. All the existing techniques, however, focus on the conventional databases. They are not applicable to online computation environment, such as data stream. In addition, the existing studies consider efficiency of skyline computation only, while the fundamental problem on the semantics of skylines still remains open. In this thesis, we study three problems of skyline computation: (1) online computing skyline over data stream; (2) skyline cube computation and its analysis; and (3) top-k most representative skyline. To tackle the problem of online skyline computation, we develop a novel framework which converts more expensive multiple dimensional skyline computation to stabbing queries in 1-dimensional space. Based on this framework, a rigorous theoretical analysis of the time complexity of online skyline computation is provided. Then, efficient algorithms are proposed to support ad hoc and continuous skyline queries over data stream. Inspired by the idea of data cube, we propose a novel concept of skyline cube which consists of skylines of all possible non-empty subsets of a given full space. We identify the unique sharing strategies for skyline cube computation and develop two efficient algorithms which compute skyline cube in a bottom-up and top-down manner, respectively. Finally, a theoretical framework to answer the question about semantics of skyline and analysis of multidimensional subspace skyline are presented. Motived by the fact that the full skyline may be less informative because it generally consists of a large number of skyline points, we proposed a novel skyline operator -- top-k most representative skyline. The top-k most representative skyline operator selects the k skyline points so that the number of data points, which are dominated by at least one of these k skyline points, is maximized. To compute top-k most representative skyline, two efficient algorithms and their theoretical analysis are presented.

Skyline analysis is important for multi-criteria decision making applications. The data in some of these applications are inherently uncertain due to various factors. Although a considerable amount of research has been dedicated separately to efficient skyline computation, as well as modeling uncertain data and answering some types of queries on uncertain data, how to conduct skyline analysis on uncertain data remains an open problem at large. In this thesis, we tackle the problem of skyline analysis on uncertain data. We propose a novel probabilistic skyline model where an uncertain object may take a probability to be in the skyline, and a p-skyline contains all the objects whose skyline probabilities are at least p. Computing probabilistic skylines on large uncertain data sets is challenging. An uncertain object is conceptually described by a probability density function (PDF) in the continuous case, or in the discrete case a set of instances (points) such that each instance has a probability to appear. We develop two efficient algorithms, the bottom-up and top-down algorithms, of computing p-skyline of a set of uncertain objects in the discrete case. We also discuss that our techniques can be applied to the continuous case as well. The bottom-up algorithm computes the skyline probabilities of some selected instances of uncertain objects, and uses those instances to prune other instances and uncertain objects effectively. The top-down algorithm recursively partitions the instances of uncertain objects into subsets, and prunes subsets and objects aggressively. Our experimental results on both the real NBA player data set and the benchmark synthetic data sets show that probabilistic skylines are interesting and useful, and our two algorithms are efficient on large data sets, and complementary to each other in performance.

Bibliography: leaves 170-175. / xvi, 220 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis analyses the development of a Generic, Non-standard Specific Information Flow System which enables computer users to select data which is to be shared in a company and store it in a common database from which it can be accessed by anyone in the company. A Generic Database Management System is developed to control the data storage and retrieval processes. / Thesis (Ph.D.)--University of Adelaide, Dept. of Mechanical Engineering, 1996

Skyline has been proposed as an important operator for many applications, such as multi-criteria decision making, data mining and visualization, and user-preference queries. Due to its importance, skyline and its computation have received considerable attention from database research community recently. All the existing techniques, however, focus on the conventional databases. They are not applicable to online computation environment, such as data stream. In addition, the existing studies consider efficiency of skyline computation only, while the fundamental problem on the semantics of skylines still remains open. In this thesis, we study three problems of skyline computation: (1) online computing skyline over data stream; (2) skyline cube computation and its analysis; and (3) top-k most representative skyline. To tackle the problem of online skyline computation, we develop a novel framework which converts more expensive multiple dimensional skyline computation to stabbing queries in 1-dimensional space. Based on this framework, a rigorous theoretical analysis of the time complexity of online skyline computation is provided. Then, efficient algorithms are proposed to support ad hoc and continuous skyline queries over data stream. Inspired by the idea of data cube, we propose a novel concept of skyline cube which consists of skylines of all possible non-empty subsets of a given full space. We identify the unique sharing strategies for skyline cube computation and develop two efficient algorithms which compute skyline cube in a bottom-up and top-down manner, respectively. Finally, a theoretical framework to answer the question about semantics of skyline and analysis of multidimensional subspace skyline are presented. Motived by the fact that the full skyline may be less informative because it generally consists of a large number of skyline points, we proposed a novel skyline operator -- top-k most representative skyline. The top-k most representative skyline operator selects the k skyline points so that the number of data points, which are dominated by at least one of these k skyline points, is maximized. To compute top-k most representative skyline, two efficient algorithms and their theoretical analysis are presented.

[Truncated abstract] Many databases in scientific and business applications have grown exponentially in size in recent years. Accessing and using databases is no longer a specialized activity as more and more ordinary users without any specialized knowledge are trying to gain information from databases. Both expert and ordinary users face significant challenges in understanding the information stored in databases. The databases are so large in most cases that it is impossible to gain useful information by inspecting data tables, which are the most common form of storing data in relational databases. Visualization has emerged as one of the most important techniques for exploring data stored in large databases. Appropriate visualization techniques can reveal trends, correlations and associations in data that are very difficult to understand from a textual representation of the data. This thesis presents several new frameworks for data visualization and visual data mining. The first technique, VisEx, is useful for visual exploration of large multi-attribute datasets and especially for exploring the correlations among the attributes in such datasets. Most previous visualization techniques can display correlations among two or three attributes at a time without excessive screen clutter. ... Although many algorithms for mining association rules have been researched extensively, they do not incorporate users in the process and most of them generate a large number of association rules. It is quite often difficult for the user to analyze a large number of rules to identify a small subset of rules that is of importance to the user. In this thesis I present a framework for the user to interactively mine association rules visually. Another challenging task in data mining is to understand the correlations among the mined association rules. It is often difficult to identify a relevant subset of association rules from a large number of mined rules. A further contribution of this thesis is a simple framework in the VisAR system that allows the user to explore a large number of association rules visually. A variety of businesses have adopted new technologies for storing large amounts of data. Analysis of historical data quite often offers new insights into business processes that may increase productivity and profit. On-line analytical processing (OLAP) has become a powerful tool for business analysts to explore historical data. Effective visualization techniques are very important for supporting OLAP technology. A new technique for the visual exploration of OLAP data cubes is also presented in this thesis.