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Research has shown that data warehouses potentially offer great investment opportunities to business. To benefit from this, business needs to invest large sums of money. Such investments are very risky, as no guarantee of the success of these ventures can be given. Object-oriented development has proved successful for developing operational systems in industry. This study researches object-oriented techniques to discover whether these techniques could be used successfully in data warehousing. A literature study focuses on the definition of an information systems development methodology and defines the components of such methodology. A further literature study on four popular object-oriented methodologies determines the commonalities of these methodologies. In conclusion, a literature study on data warehouse methodologies is done to discover the phases and techniques used in developing data warehouses. Based on the literature, a method is proposed to build a data warehouse harnessing object-oriented phases and techniques. The proposed method is applied as an interpretive experiment, followed by an evaluation of the data warehouse implemented. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2009.

Current monitoring tools in the intensive care units focus on displaying physiologically monitored parameters (e.g. vital signs such as heart rate, respiratory rate and blood pressure) at the present moment. Added clinical utility can be found by analyzing how the conditions of a patient evolve with time, and automatically relating that dynamics to population trends. Variability analysis consists of monitoring patterns of variation over intervals in time of physiological signals such as heart rate and respiratory rate. Given that illness has been associated in multiple studies with altered variability, most commonly lack of variation, variability monitoring represents a tool whose contribution at the bedside still needs to be explored. With the long term objective of improving care, this thesis promotes the use of variability analysis through three distinct types of analysis: facing the technical challenges involved with the dimensionality of variability analysis, enhancing the physiological understanding of variability, and showing its utility in real world clinical applications. In particular, the contributions of this thesis include: the review and classification into domains of a large array of measures of variability; the design of system and methods to integrate multiple measures of variability into a unique score, called composite measure, bringing relevant information to specific clinical problems; the comparison of patterns of heart rate variability during exercise and sepsis development, showing the inability of single measures of variability to discriminate between the two kinds of stressors; the analysis of variability produced from a physiologically-based model of the cardiovascular system, showing that each single measure of variability is an unspecific sensor of the body, thereby promoting multivariate analysis to the only means of understanding the physiology underlying variability; the study of heart rate variability in a population at high risk of sepsis development, showing the ability of variability to predict the occurrence of sepsis more than 48 hours in advance respect to the time of diagnosis of the clinical team; the study of heart and respiratory rate variability in intubated intensive care unit patients, showing how variability can provide a better way of assessing extubation readiness respect to commonly used clinical parameters. Overall, it is hoped that these novel contributions will help promoting bedside applications of variability monitoring to improve patient care.

Research has shown that data warehouses potentially offer great investment opportunities to business. To benefit from this, business needs to invest large sums of money. Such investments are very risky, as no guarantee of the success of these ventures can be given. Object-oriented development has proved successful for developing operational systems in industry. This study researches object-oriented techniques to discover whether these techniques could be used successfully in data warehousing. A literature study focuses on the definition of an information systems development methodology and defines the components of such methodology. A further literature study on four popular object-oriented methodologies determines the commonalities of these methodologies. In conclusion, a literature study on data warehouse methodologies is done to discover the phases and techniques used in developing data warehouses. Based on the literature, a method is proposed to build a data warehouse harnessing object-oriented phases and techniques. The proposed method is applied as an interpretive experiment, followed by an evaluation of the data warehouse implemented. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2009.

This dissertation focuses on developing imaging informatics algorithms for clinical decision support systems (CDSSs) based on histopathological whole-slide images (WSIs). Currently, histopathological analysis is a common clinical procedure for diagnosing cancer presence, type, and progression. While diagnosing patients using biopsy slides, pathologists manually assess nuclear morphology. However, making decisions manually from a slide with millions of nuclei can be time-consuming and subjective. Researchers have proposed CDSSs that help in decision making but they have limited reproducibility. The development of robust CDSSs for WSIs faces several informatics challenges: (1) Lack of robust segmentation methods for histopathological images, (2) Semantic gap between quantitative information and pathologist’s knowledge, (3) Lack of batch-invariant imaging informatics methods, (4) Lack of knowledge models for capturing informative patterns in large WSIs, and (5) Lack of guidelines for optimizing and validating diagnostic models. I conducted advanced imaging informatics research to overcome these challenges and developed novel methods to extract information from WSIs, to model knowledge embedded in large histopathological datasets, such as The Cancer Genome Atlas (TCGA), and to assist decision making with biological and clinical validation. I validated my methods for two applications: (1) diagnosis of histopathology-based endpoints such as subtype and grade and (2) prediction of clinical endpoints such as metastasis, stage, lymphnode spread, and survival. The statistically emergent feature subsets in the diagnostic models for histopathology-based endpoints were concordant with pathologists’ knowledge.

The present study aims to develop an analysis package for combining forecasts that are obtained from different forecast methods. The package is composed of three modules, namely, the data input module, the data analysis module and the combination module. In data input module, the data is entered and saved as an Excel file with the given name. In data analysis module, the program computes the forecasts of the selected methods and displays the forecast results, the mean absolute errors, the mean square errors and the mean absolute percentage errors of these methods. In combination module, the forecast results, computed in the data analysis module, are combined according to the selected combination methods. All the detailed calculations of the forecasts and the values assigned by the program to minimize the mean absolute deviations, the mean square errors and the mean absolute percentage errors are displayed under the columns of the related method on the Excel spreadsheet of the file.

It is agreed upon by many researchers that, although risk management (RM) is accepted as one of the critical success factors for construction projects, project participants generally do not have sufficient knowledge pertinent to risk management concept and the number of risk management support tools which facilitate the process is rather low. In order to facilitate risk management activities, decision support tools that will enable risk identification, analysis and response strategy formulation should be developed. Decision support tools are necessary for systematic identification of risks, scenario generation, proactive management of risk and integration of risk management activities with other project management functions such as planning, cost estimating and monitoring project success. The aim of this study is to introduce a conceptual risk management model and a prototype risk management decision support system (DSS) which is applicable to construction projects. The proposed decision support system, namely Integrated Risk Management System (IRMS), is designed to support the user at all phases of the risk management process and to integrate risk management activities with other project management functions in the bid preparation stage of international construction projects. A risk management process model has been developed as well as a risk information model so that IRMS can be used for systematic management of risk by all parties involved in a construction project. Major functions of IRMS include, risk identification by using a built-in Hierarchical Risk Breakdown Structure (HRBS), risk analysis by Monte Carlo (MC) simulation, risk assessment by risk rating, risk re-assessment, response generation, risk monitoring and corporate memory. The applicability of the system has been tested by a real case study and its functionality has been demonstrated using the data associated with the case study.

In today&rsquo / s conditions, systems that surround individuals have evolved in structure such that, nature of variable interactions are much more complex and changing continuously. Logistics systems, which constitute an example for such systems, have also necessitated fast management and decision-making in a fast paced environment, under limited sources with the additional effect of increasing customer requirements and competition. These conditions require a dynamic analysis. In this study, system dynamics approach was shown to be a competent alternative to analyze complex and dynamic systems like logistics systems and its cost structure studied. A theoretical logistics model with a user interface is presented. Then the developed model implemented in an organization operating in aluminum industry by making the required modifications. A scenario analysis regarding a new investment decision studied. All analysis and modeling studies were carried out in Stella 6.0.1.

In this study, a specific decision support system is designed and developed for Production Planning and Marketing Department of an apparel company. The developed system involves two modules with user friendly interface or data input and query. The system is designed to provide support in the following fields of decision making / pre cost estimation, capacity planning, master production planning and production scheduling. A detailed analysis of the existing system is conducted and Microsoft Access is used for the development of software. The cost and benefits of the implementing the system are also discussed in addition to basics, sample reports and the user interface of the developed program.