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.

An essential motivation behind concurrent object-oriented systems is to exploit the software modularity and reuse potential of object-oriented features in the development of concurrent systems. However, the marriage between the two disciplines has not been a happy one. In this work we propose a model that reduces the problems associated with introducing concurrency into object-oriented languages. We explore techniques for reasoning about synchronisation requirements in composite object systems operating in a concurrent environment. Furthermore we investigate safe locking strategies, where the locks may be distributed over the components of the system and identify conditions for such locks to be non-redundant. Through the recognition of a Galois connection, we achieve an elegant mathematical characterisation of the lock-distribution problem, which allows us to identify efficient strategies and tools for assisting developers with lock selection.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical Engineering, 2006. / Includes bibliographical references.

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.

This paper seeks to describe the process by which a database-centric web application was redesigned and rewritten to take advantage of Apache’s Lucene - an open-source information retrieval software library written in the Java programming language. After the implementation of a Lucene-based text index of “semi-structured data”, a college radio station's card catalog application was able to deliver higher-quality search results in significantly less time than it was able to do using just a relational database alone. Additionally, the dramatic improvements in speed and performance even allowed the search results interface to be redesigned and enhanced with an improved pagination system and new features such as faceted search/filtering.

This paper describes the design and initial implementation of a web-based financial information system, intended to replace a Microsoft Access financial database in support of a federally funded, international health project. This system is being reimplemented for the web in order to provide stakeholders across the country (and potentially around the world) access to the project's financial information.

Geographically located materials are used by the archaeology to analyze and explain the socio-cultural aspects of ancient life. Thus, Geographical Information Systems (GIS) have started to be used in archaeology for many applications. Although, cooperation of GIS and archaeology is considered as beneficial, it has become insufficient to meet the requirements of archaeologists about excavation study. Therefore, the aim of this thesis is to develop Spatial Decision Support System (SDSS) which includes both the GIS tools and analyses and / analytical modeling capabilities in order to satisfy the needs of archaeologists. Moreover, to develop a specialized system for specific archaeological excavation site is aimed. In this study, Kaunos is selected as a case study area and in order to furnish this aim, firstly, needs of archaeologists working in the excavation study of Kaunos are analyzed. Secondly, GIS tools and analyses are determined which meet the requirements of archaeologists. Finally, SDSS for Kaunos Archaeological Excavation Site is developed. It is composed of four components namely / Database Management, Model Management, Dialog Management and Stakeholder Components. Analyses are conducted under the Model Management Component and results are visualized in Dialog Management Component. Result maps help and assist archaeologists in terms of interpreting and examining the socio-cultural, economical and demographical characteristics of Kaunos.

In the transition to a fully integrated database at Marion College in Marion, Indiana, data elements needed more rigorous definitions both in terms of the relationships existing between data elements and attributes assigned to each between files. This data dictionary was thenused to access the attributes of each data element and the links existing and verify data elements within the database to locate illegal and inconsistent data items. From this data verification, weaknesses in procedures and software for loading information into the database were discovered and corrected.Ball State UniversityMuncie, IN 47306

In this study, I rewrite a relational database, originally written in Pascal, using the Ada language. I tried to exploit certain features of Ada that make it a powerful language, such as packages, in an attempt to have as general a program as possible. So the purpose here was to see how, by using the Ada language, a relational database program could be inproved by eliminating certain restrictions of the Pascal language.Ball State UniversityMuncie, IN 57406