The paradigm of Decision Support Systems (DSS) is to support decision-making, while an Expert System’s (ES) major objective is to provide expert advice in specialised situations. Knowledge-Based DSS (KB-DSS), also called Intelligent Decision Support Systems (IDSS), integrate traditional DSS with the advances of ES. A KB-DSS’ knowledge base usually contains knowledge expressed by an expert and captured by a knowledge engineer. The indirect transfer between the domain expert and the knowledge base through a knowledge engineer may lead to a long and inefficient knowledge acquisition process. This thesis compares 11 DSS packages in search of a (KB-) DSS generator where domain experts can specify and maintain a Specific Decision Support System (SDSS) to assist users in making decisions. The proposed (KB-) DSS-generator is tested with a university and study-program prototype. Since course and study plan programs change intermittently, the (KB-) DSS’ knowledge base enables domain experts to set and maintain their course and study plan rules without the assistance of a knowledge engineer. Criteria are set to govern the (KB-) DSS generator search process. Example knowledge base rules are inspected to determine if domain experts will be able to maintain a set of production rules used in a student registration advice system. By developing a prototype and inspecting knowledge base rules, it was found that domain experts would be able to maintain their knowledge in the decentralised knowledge base, on condition that the objects and attributes used in the rule base were first specified by a builder/programmer. / Dissertation (MSc Computer Science)--University of Pretoria, 2005. / Computer Science / unrestricted

Syftet med denna studie är att utreda begreppen big data och analytics. Utifrån vetenskapliga teorier om begreppen undersöks hur konsultföretag uppfattar och använder sig av big data och analytics. För att skapa en nyanserad bild har även en organisation inom vården undersökts för att få kunskap om hur de kan dra nytta av big data och analytics. Ett antal viktiga svårigheter och framgångsfaktorer kopplade till båda begreppen presenteras. De svårigheterna kopplas sedan ihop med en framgångsfaktor som anses kunna bidra till att lösa det problemet. De mest relevanta framgångsfaktorer som identifierats är att högkvalitativ data finns tillgänglig men även kunskap och kompetens kring hur man hanterar data. Slutligen tydliggörs begreppens innebörd där man kan se att big data oftast beskrivs ur dimensionerna volym, variation och hastighet och att analytics i de flesta fall syftar till att deskriptiv och preventiv analys genomförs. / The purpose of this study is to investigate the concepts of big data and analytics. The concepts are explored based on scientific theories and interviews with consulting firms. A healthcare organization has also been interviewed to get a richer understanding of how big data and analytics can be used to gain insights and how an organisation can benefit from them. A number of important difficulties and sucess facors connected to the concepts are presented. These difficulties are then linked to a sucess factor that is considered to solve the problem. The most relevant success factors identified are the avaliability of high quality data and knowledge and expertise on how to handle the data. Finally the concepts are clarified and one can see that big data is usually described from the dimensions volume, variety and velocity and analytics is usually described as descriptive and preventive analysis.

A strong trend of returning to nuclear power is evident in different places in the world. Forty-five countries are planning to add nuclear power to their grids and more than 66 nuclear power plants are under construction. Nuclear power plants that generate electricity and steam need to improve safety to become more acceptable to governments and the public. One novel practical solution to increase nuclear power plants' safety factor is to build them away from urban areas, such as offshore or underground. To date, Land-Based siting is the dominant option for siting all commercial operational nuclear power plants. However, the literature reveals several options for building nuclear power plants in safer sitings than Land-Based sitings. The alternatives are several and each has advantages and disadvantages, and it is difficult to distinguish among them and choose the best for a specific project. In this research, we recall the old idea of using the alternatives of offshore and underground sitings for new nuclear power plants and propose a tool to help in choosing the best siting technology. This research involved the development of a decision model for evaluating several potential nuclear power plant siting technologies, both those that are currently available and future ones. The decision model was developed based on the Hierarchical Decision Modeling (HDM) methodology. The model considers five major dimensions, social, technical, economic, environmental, and political (STEEP), and their related criteria and sub-criteria. The model was designed and developed by the author, and its elements' validation and evaluation were done by a large number of experts in the field of nuclear energy. The decision model was applied in evaluating five potential siting technologies and ranked the Natural Island as the best in comparison to Land-Based, Floating Plant, Artificial Island, and Semi-Embedded plant.

Master of Regional and Community Planning / Department of Landscape Architecture/Regional and Community Planning / Claude A. Keithley / Planning Support Systems allow planners to create alternative development scenarios to forecast a more accurate and precise future trend of development in their communities. The software What If?™ has been developed and introduced in the planning profession since its first release in the 1990’s. This report evaluates the software What If?™ based on the planning decision-making process. The report provides three aspects of evaluation: technical, empirical, and subjective. In addition, the paper will be also providing an overall understanding of the analytical capability of What If?™, and an overview of its operating procedures.

This research compared the use of an electronic meeting system tool to a manual group process in large and small groups in a controlled laboratory experiment. Outcomes measured include the quality of decision, the time taken in various stages of the decision making process, and group member satisfaction. A research model of the variables influencing group decision making was developed. The six independent variables included in this model are group size, the rule by which the group makes a decision, the incentives driving the group, the distribution of useful information within the group, the task complexity, and the meeting support (electronic or manual). In this research group size and method of support were manipulated, while the other variables were controlled. A decision-making task was developed for this research to specify and manipulate the six independent variables. The task described a product mix problem in which information on each product was given to group members. The group shared information and jointly determined an outcome. The group used an unanimous decision rule to choose a solution. A numerical outcome was used to objectively measure decision quality. Each member of the group received a cash payoff determined by the group's solution as incentive in accomplishing the task. All groups found the optimal solution. The simplicity of the task may have minimized the differences found between groups. There was no significant difference in general member satisfaction or time to decision. Prior knowledge was found to influence general member satisfaction and the time needed for the group to share information. Members of large groups perceived more uneven distribution of participation than members of small groups. Voting differences were very large: large groups took significantly more votes than small groups, and electronic groups took significantly more votes than manual groups. "Conjunctive" and "disjunctive" task descriptions are used to discuss task/tool interaction.

Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data into useful, personalized decision support knowledge in a flexible, cost-effective, and productive way. The goal of my Ph.D. research is to address some key challenges in current clinical deci-sion support, including (1) the lack of a flexible, evidence-based, and personalized data mining tool, (2) the need for interactive interfaces and visualization to deliver the decision support knowledge in an accurate and effective way, (3) the ability to generate temporal rules based on patient-centric chronological events, and (4) the need for quantitative and progressive clinical predictions to investigate the causality of targeted clinical outcomes. The problem statement of this dissertation is that the size, complexity, and biases of the current clinical data make it very difficult for current informatics technologies to extract individual-specific knowledge for clinical decision support. This dissertation addresses these challenges with four overall specific aims: Evidence-Based and Personalized Decision Support: To develop clinical decision support systems that can generate evidence-based rules based on personalized clinical conditions. The systems should also show flexibility by using data from different clinical settings. Interactive Knowledge Delivery: To develop an interactive graphical user interface that expedites the delivery of discovered decision support knowledge and to propose a new visualiza-tion technique to improve the accuracy and efficiency of knowledge search. Temporal Knowledge Discovery: To improve conventional rule mining techniques for the discovery of relationships among temporal clinical events and to use case-based reasoning to evaluate the quality of discovered rules. Clinical Casual Analysis: To expand temporal rules with casual and time-after-cause analyses to provide progressive clinical prognostications without prediction time constraints. The research of this dissertation was conducted with frequent collaboration with Children’s Healthcare of Atlanta, Emory Hospital, and Georgia Institute of Technology. It resulted in the development and adoption of concrete application deliverables in different medical settings, including: the neuroARM system in pediatric neuropsychology, the PHARM system in predictive health, and the icuARM, icuARM-II, and icuARM-KM systems in intensive care. The case studies for the evaluation of these systems and the discovered knowledge demonstrate the scope of this research and its potential for future evidence-based and personalized clinical decision support.

Thesis (PhD)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: The policing and monitoring of South Africa's coastline and economic exclusion zone is made di cult not only by the size of the area of interest, but also by the limited resources available for maritime detection and policing. As a consequence, illegal activities, such as smuggling, poaching and illegal border crossings, are often conducted with impunity. Conventional approaches to monitoring coastal areas, such as the use of patrol boats, port inspections and aircraft surveillance, may be augmented by advances in technology that are steadily contributing vast amounts of data related to maritime activity. For example, various South African agencies collect auto- matic identi cation system and vessel monitoring system transmissions, and gather additional kinematic data of maritime vessels through a number of strategically placed coastal radars. A command and control centre for actively monitoring these data (outside of the intelligence community) was established by the South African Navy in 2014. Such centres provide surveillance operators with a real-time picture of a maritime region of interest from which they can identify relevant facts of interest through a reliance on experience and domain knowledge. The e ectiveness of this process may, however, be undermined by the vast quantities of data typically under consideration, by the di culty of identifying long-term trends in vessel kinematic behaviour and by the possibility of operator fatigue brought on by the relatively low incidence levels of activities of interest. E ective decision support tools may play a valuable role in this context by the automatic processing of these vast collections of data, by the identi cation of concepts of interest and by the prediction of future occurrences of interest. It is, however, essential that such tools should be exible enough to adapt to changes in typical vessel behaviour over time and that they should be capable of integrating new trends and new types of behaviours. Various approaches to maritime surveillance are investigated in this dissertation from the perspectives of threat detection and anomaly identi cation, with particular emphasis on a systems approach to decision support. A decision support system framework that utilises rule-based and data-driven mechanisms is proposed as a means to separate the interesting from the uninteresting and to provide early warnings of potentially threatening maritime vessel behaviour to operators. This system framework is primarily concerned with kinematic data and is restricted to the identi cation of certain types of activities. Successful classi cation and, ultimately, timely prediction of potentially threatening behaviour would allow for e ective policing by providing early warning to relevant entities, thus potentially leading to more e ective use of available policing resources. / AFRIKAANSE OPSOMMING: Die patrollering en monitering van die Suid-Afrikaanse kusgebied en gepaardgaande ekonomiese eksklusiewe zone word bemoeilik deur die grootte van die tersprake area en die beperkte hulpbronne wat vir patrollie-doeleindes aangewend kan word. Gevolglik gaan onwettige aktiwiteite, soos smokkelary, stroping en onwettige immigrasie dikwels ongestraf. Konvensionele benaderings tot die monitering van kusgebiede, soos die aanwending van patrolliebote, die uitvoer van hawe-inspeksies en gere elde lugpatrollies, kan aangevul word deur tegnologiese vooruitgang wat voortdurend tot groot hoeveelhede data oor maritieme aktiwiteit bydra. Verskeie Suid- Afrikaanse agentskappe ontvang byvoorbeeld outomatiese identi kasiestelsel en vaartuigmoni- teringstelsel uitsendings, en samel ook addisionele kinematiese data oor maritieme vaartuie deur middel van strategies-geplaasde kusradars in. 'n Bevel-en-beheersentrum wat hierdie inligting (buite die intelligensiegemeenskap) aktief ontleed, is in 2014 deur die Suid-Afrikaanse Vloot tot stand gebring. Sulke sentra verskaf 'n intydse blik oor die maritieme gebied onder beskouing aan operateurs wat dan, gebaseer op hulle ervaring en omgewingskennis, relevante inligting oor vaartuie kan a ei. Die doeltre ende uitvoering van hierdie proses kan egter ondermyn word deur die tipiese groot hoeveelhede data, die moeilikheidsgraad van die identi kasie van langtermyn tendense in die kinematiese gedrag van vaartuie om die kus en die moontlikheid van operateur-uitputting as gevolg van lang periodes van relatiewe oninteressante vaartuiggedrag. Doeltre ende besluitsteunhulpmiddels kan 'n waardevolle bydrae in hierdie konteks maak deur die ge-outomatiseerde prosessering van hierdie groot hoeveelhede data, die identi kasie van interessante vaartuiggedrag en die voorspelling van toekomstige relevante insidente. Dit is egter noodsaaklik dat sulke hulpmiddels buigsaam genoeg moet wees om te kan aanpas by veranderings in tipiese maritieme aktiwiteit oor tyd en dat nuwe tendense en tipes aktiwiteite geakkommodeer kan word. Verskeie benaderings tot maritieme oorsig word in hierdie proefskrif vanuit die perspektiewe van die bespeuring van bedreigings en die opsporing van vreemde verskynsels ondersoek, met 'n spesi eke fokus op 'n stelselbenadering tot besluitsteun. 'n Besluitsteun stelselraamwerk wat berus op re el-gebaseerde en data-aangedrewe meganismes word as 'n hulpmiddel voorgestel waarmee interessante maritieme gedrag van oninteressante gedrag onderskei kan word om sodoende 'n vroe e waarskuwing aan operateurs met betrekking tot moontlike bedreigende maritieme aktiwiteite te kan rig. Die werking van hierdie stelselraamwerk berus hoofsaaklik op die gebruik van kinematiese vaartuigdata en is beperk tot die naspeuring van sekere soorte bedreigende gedrag. Die suksesvolle klassi kasie en tydige voorspelling van potensi ele bedreigende maritieme gedrag behoort doeltre ende kusmonitering en verbeterde aanwending van die beperkte, gepaardgaande hulpbronne deur relevante kusagentskappe moontlik te maak.

published_or_final_version / Urban Planning and Environmental Management / Doctoral / Doctor of Philosophy

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / Business Administration / Master / Master of Business Administration