Thesis (M.S.)--Villanova University, 2010. / Computer Science Dept. Includes bibliographical references.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1985. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 130-132).

Thesis (M.Sc.)--Memorial University of Newfoundland, 2003. / Bibliography: leaves 121-123. Also available online.

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains xiii, 316 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 315-316).

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (M.S.)--Carnegie-Mellon University, 1983. / Includes bibliographical references (p. 121-123).

M.Com. (Computer Auditing) / Please refer to full text to view abstract

M.Sc. (Computer Science) / Concurrency control is the problem that exists in a database management system when more than one transaction or application is executed simultaneously. If transactions or applications are executed sequentially there will- be no problem with the allocation of resources. It is however necessary to execute transactions concurrently to utilise computer and resource capacity to its maximum extent. It can lead to inconsistent data if this concurrent execution of transactions are not properly controlled. If this should happen the data would be of no more use to the users of a system. The thesis is divided in the following way. Chapter 1 gives background information on the concurrency control problem. In chapter 2 a couple of mechanisms for solving the concurrency control problem are studied briefly. Chapters 3 and 4 provides a more in depth study of two specific mechanisms namely two-phase locking and timestamps. 80th of these mechanisms have already been implemented in systems to. solve the concurrency control problem.- In chapter 5 a comparison is made of the two methods described in chapters 3 and 4. A third method for handling concurrency control is briefly described in chapter 6. This method hasn't received a lot of attention from researchers yet. And in the last chapter, chapter 7, the concurrency control method used in the SDD-1 system is studied in more detail. SDD-1 is a distributed database management system.

There have been a number of technological advances in the last ten years, which has resulted in the amount of data generated in organisations increasing by more than 200% during this period. This rapid increase in data means that if financial institutions are to derive significant value from this data, they need to identify new ways to analyse this data effectively. Due to the considerable size of the data, financial institutions also need to consider how to effectively visualise the data. Traditional tools such as relational database management systems have problems processing large amounts of data due to memory constraints, latency issues and the presence of both structured and unstructured data The aim of this research was to use data analysis and information visualisation techniques (IV) to support the effective analysis of large financial data sets. In order to visually analyse the data effectively, the underlying data model must produce results that are reliable. A large financial data set was identified, and used to demonstrate that IV techniques can be used to support the effective analysis of large financial data sets. A review of the literature on large financial data sets, visual analytics, existing data management and data visualisation tools identified the shortcomings of existing tools. This resulted in the determination of the requirements for the data management tool, and the IV tool. The data management tool identified was a data warehouse and the IV toolkit identified was Tableau. The IV techniques identified included the Overview, Dashboards and Colour Blending. The IV tool was implemented and published online and can be accessed through a web browser interface. The data warehouse and the IV tool were evaluated to determine their accuracy and effectiveness in supporting the effective analysis of the large financial data set. The experiment used to evaluate the data warehouse yielded positive results, showing that only about 4% of the records had incorrect data. The results of the user study were positive and no major usability issues were identified. The participants found the IV techniques effective for analysing the large financial data set.

The technological progress in terms of increasing computational power and growing virtual space to collect data offers great potential for businesses to benefit from data mining applications. Data mining can create a competitive advantage for corporations by discovering business relevant information, such as patterns, relationships, and rules. The role of the human user within the data mining process is crucial, which is why the research area of domain knowledge becomes increasingly important. This thesis investigates the impact of domain knowledge-driven variable derivation on classifier performance for corporate data mining. Domain knowledge is defined as methodological, data and business know-how. The thesis investigates the topic from a new perspective by shifting the focus from a one-sided approach, namely a purely analytic or purely theoretical approach towards a target group-oriented (researcher and practitioner) approach which puts the methodological aspect by means of a scientific guideline in the centre of the research. In order to ensure feasibility and practical relevance of the guideline, it is adapted and applied to the requirements of a practical business case. Thus, the thesis examines the topic from both perspectives, a theoretical and practical perspective. Therewith, it overcomes the limitation of a one-sided approach which mostly lacks practical relevance or generalisability of the results. The primary objective of this thesis is to provide a scientific guideline which should enable both practitioners and researchers to move forward the domain knowledge-driven research for variable derivation on a corporate basis. In the theoretical part, a broad overview of the main aspects which are necessary to undertake the research are given, such as the concept of domain knowledge, the data mining task of classification, variable derivation as a subtask of data preparation, and evaluation techniques. This part of the thesis refers to the methodological aspect of domain knowledge. In the practical part, a research design is developed for testing six hypotheses related to domain knowledge-driven variable derivation. The major contribution of the empirical study is concerned with testing the impact of domain knowledge on a real business data set compared to the impact of a standard and randomly derived data set. The business application of the research is a binary classification problem in the domain of an insurance business, which deals with the prediction of damages in legal expenses insurances. Domain knowledge is expressed through deriving the corporate variables by means of the business and data-driven constructive induction strategy. Six variable derivation steps are investigated: normalisation, instance relation, discretisation, categorical encoding, ratio, and multivariate mathematical function. The impact of the domain knowledge is examined by pairwise (with and without derived variables) performance comparisons for five classification techniques (decision trees, naive Bayes, logistic regression, artificial neural networks, k-nearest neighbours). The impact is measured by two classifier performance criteria: sensitivity and area under the ROC-curve (AUC). The McNemar significance test is used to verify the results. Based on the results, two hypotheses are clearly verified and accepted, three hypotheses are partly verified, and one hypothesis had to be rejected on the basis of the case study results. The thesis reveals a significant positive impact of domain knowledge-driven variable derivation on classifier performance for options of all six tested steps. Furthermore, the findings indicate that the classification technique influences the impact of the variable derivation steps, and the bundling of steps has a significant higher performance impact if the variables are derived by using domain knowledge (compared to a non-knowledge application). Finally, the research turns out that an empirical examination of the domain knowledge impact is very complex due to a high level of interaction between the selected research parameters (variable derivation step, classification technique, and performance criteria).