This thesis presents a framework capable of integrating hard (physics-based) and soft (people-generated) data for the purpose of achieving increased situational assessment (SA) and effective course of action (CoA) generation upon risk identification. The proposed methodology is realized through the extension of an existing Risk Management Framework (RMF). In this work, the RMF’s SA capabilities are augmented via the injection of soft data features into its risk modeling; the performance of these capabilities is evaluated via a newly-proposed risk-centric information fusion effectiveness metric. The framework’s CoA generation capabilities are also extended through the inclusion of people-generated data, capturing important subject matter expertise and providing mission-specific requirements. Furthermore, this work introduces a variety of CoA-related performance measures, used to assess the fitness of each individual potential CoA, as well as to quantify the overall chance of mission success improvement brought about by the inclusion of soft data. This conceptualization is validated via experimental analysis performed on a combination of real- world and synthetically-generated maritime scenarios. It is envisioned that the capabilities put forth herein will take part in a greater system, capable of ingesting and seamlessly integrating vast amounts of heterogeneous data, with the intent of providing accurate and timely situational updates, as well as assisting in operational decision making.

Context: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives: The main objectives of this thesis are to provide: • A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner; • A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and • A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context Method: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail). Results: The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.

The thesis is concerned with application of Decision Support Systems and Business Intelligence as a tool for decision-making support into processes of operational risk management in insurance company to improve them and reach a higher corporate performance.The goal of the thesis is to analyze a theory for a support of a process improvement design with the mentioned reach and to develop a design of improved operational risk management processes in a particular insurance company. The mean to reach goals is through-out a literature and articles research regarding a process improvement, decision-making, models and technologies of DSS and BI, and risk management. The theoretical framework is then used as back-bone for implementation of a practical part of innovated processes design. The thesis offers the analysis of an area of risk management processes improvement via DSS and BI in an insurance company that has been researched relatively little, but whose principles can be used for improvement projects in different areas too. A department of operational risk management in a certain insurance company obtains a complete design of innovated processes including DSS / BI application support specification which respects modern techniques of process improvements involving specific methods of decision-making quality. For the same department a part of the design was implemented -- Knowledge management system, that can be fully used. Introductory part of the thesis is concerned with techniques of business process improvement and its alignment with corporate performance. The next chapter describes specifics of operational risk management processes. In the third one models, techniques, information technologies of Decision Support Systems and Business Intelligence disciplines are analyzed. The practical part of the text starts with strategic analysis of the insurance company, proceeds with operational risk management processes analysis and reaches the design of innovated processes involving DSS and BI application support. Benefits of designed processes' changes are verified. In the conclusion a certain process change is accepted for the implementation and its results are described.

Context: Accurate decision-making requires updated and precise information to establish the reality of an overall situation. New data sources (e.g., wearable technologies) have been increasing the amount of available and useful data, which is now called big data. This has a great potential for transforming the entire business process and improving the accuracy of decisions. In this context, disaster management represents an interesting scenario that relies on big data to enhance decision-making. This is because it must cope with data provided not only by traditional sources (e.g., stationary sensors) but also by emerging sources - for instance, information shared by local volunteers, i.e., volunteered geographic information (VGI). When combined, these data sources can be regarded as large in volume, with different velocities, and a variety of formats. Furthermore, an analysis is required to confirm their veracity is required since these data sources are disconnected and prone to various errors. These are the 4Vs that characterize big data. Gap: However, although all these data open up further opportunities, their huge volume, together with an inappropriate data integration and unsuitable visualization, can result in information being overlooked by decision-makers. This problem arises because the integration of the available data is hampered by the intrinsic heterogeneity of their features (e.g., their occurrence in different formats). When integrated, this information also often fails to reach the decision-makers in a suitable way (e.g., in appropriate visualization formats). Moreover, there is not a clear understanding of the decision-makers needs or how the available data can meet these needs. Objective: In light of this, this thesis presents an approach for improving decision-making with heterogeneous geospatial big data based on spatial decision support systems and volunteered geographic information in disaster management. Methods: Systematic mapping studies were conducted to identify gaps in research studies with regard to the use of volunteered information and spatial decision support systems in disaster management. On the basis of these studies, two design science projects were carried out. The first of these aimed at defining the elements that are essential for ensuring the integration of heterogeneous data, whereas the second project aimed at obtaining a better understanding of decision-makers needs. A cross-organizational action research project was also conducted to define the design principles that should be observed for a spatial decision support system to effectively support decision-making with heterogeneous geospatial big data. A series of empirical case studies was undertaken to evaluate the outcomes of these projects. Results: The overall approach thus consists of the three significant outcomes that were derived from these projects. The first outcome was the conceptual architecture that defines the integration of heterogeneous data sources. The second outcome was a model-based framework that describes the connection of decision-making with appropriate data sources. The third outcome is based on the framework and comprises a set of design principles for guiding the development of spatial decision support systems for decision-making with heterogeneous geospatial big data. Conclusion: This thesis has made a useful contribution to both practice and research. In short, it defines ways of integrating heterogeneous data sources, provides a better understanding of decision-makers needs, and supports the development of a spatial decision support system to effectively assist decision-making with heterogeneous geospatial big data. / Contexto: Uma tomada de decisão precisa exige informações mais precisas e atualizadas para estabelecer a realidade da situação geral. Novas fontes de dados (e.g, tecnologias vestíveis) tem aumentado a quantidade de dados úteis disponíveis, que agora é chamado de big data. Isso tem grande potencial para transformar todo o processo de negócio e melhorar a precisão na tomada de decisão. Neste contexto, a gestão de desastres representa um interessante cenário que depende de big data para aprimorar a tomada de decisão. Isso porque, ela tem que lidar com dados fornecidos não apenas por fontes tradicionais (e.g., sensores estáticos), mas também por fontes emergentes por exemplo, informações compartilhadas por voluntários locais, i.e., as informações geográficas de voluntários (VGI). Quando combinadas, estas fontes de dados podem ser consideradas grandes em volume, com diferentes velocidades e uma variedade de formatos. Além disso, uma análise com relação à sua veracidade é necessaria uma vez que estas fontes de dados são desconectadas e propensas à erros. Estes são os 4Vs que caracterizam big data. Problema: No entanto, embora todos estes dados abrem novas oportunidades, seu grande volume em conjunto com uma integração inapropriada e uma visualização inadequada, podem tornar as informações ignoradas por tomadores de decisão. Isso ocorre, pois, a integração dos dados disponíveis torna-se complicada devido a heterogeneidade intrínseca nas suas características (e.g., dados em formatos diferentes). Quando integradas, estas informações frequentemente também não chegam aos tomadores de decisão em uma condição apropriada (por exemplo, no formato de visualização adequado). Além disso, não existe uma clara compreensão sobre as necessidades dos tomadores de decisão ou sobre como os dados disponíveis podem ser usados para atender essas necessidades. Objetivo: Dessa forma, esta tese de doutorado apresenta uma abordagem para melhorar a tomada de decisões com grande volume de dados espaciais heterogêneos baseada em sistemas de suporte à decisão espacial e informações geográficas de voluntários na gestão de desastres. Métodos: Mapeamentos sistemáticos foram conduzidos para identificar lacunas de pesquisa no uso de dados voluntários e sistemas de suporte à decisão na gestão de desastres. Com base nestes estudos, dois projetos de design science foram conduzidos. O primeiro deles buscou definir elementos essências para entender a integração de dados heterogêneos, enquanto o segundo projeto buscou fornecer um melhor entendimento das necessidades dos tomadores de decisão. Também foi conduzido um projeto de pesquisa-ação interinstitucional para definir princípios de projeto que deveriam ser observados para um sistema de suporte à decisão espacial ser efetivo no apoio a tomada de decisão com grande volume de dados espaciais heterogêneos. Uma série de estudos de caso empíricos foram conduzidos para avaliar os resultados destes projetos. Resultados: A abordagem geral então é composta pelos três resultados significantes que foram derivados destes projetos. Em primeiro lugar, uma arquitetura conceitual que especifica a integração de fontes de dados heterogêneas. O segundo elemento é uma estrutura baseada em modelo que descreve a conexão entre a tomada de decisão com as fontes de dados mais adequadas. Com base nesta estrutura, o terceiro elemento consiste em um conjunto de princípios de design que guiam o desenvolvimento de um sistema de suporte à decisão espacial para tomada de decisão com grande volume de dados espaciais heterogêneos. Conclusão: Esta tese de doutorado realizou importantes contribuições para a prática e pesquisa. Em resumo, ela define formas para integrar fontes de dados heterogêneos, fornece uma melhor compreensão sobre as necessidades dos tomadores de decisão e ajuda no desenvolvimento de sistemas de suporte à decisão espacial para tomada de decisão com grande volume de dados espaciais heterogêneos.

Este trabalho trata da especificação, desenvolvimento e utilização do Sistema de Apoio à Gestão de Utilidades e Energia - SAGUE, um sistema concebido para auxiliar na análise de dados coletados de sistemas de utilidades como ar comprimido, vapor, sistemas de bombeamento, sistemas para condicionamento ambiental e outros, integrados com medições de energia e variáveis climáticas. O SAGUE foi desenvolvido segundo conceitos presentes em sistemas de apoio à decisão como Data Warehouse e OLAP - Online Analytical Processing - com o intuito de transformar os dados oriundos de medições em informações que orientem diretamente as ações de conservação e uso racional de energia. As principais características destes sistemas, que influenciaram na especificação e desenvolvimento do SAGUE, são tratadas neste trabalho. Além disso, este texto aborda a gestão energética e os sistemas de gerenciamento de energia visando apresentar o ambiente que motivou o desenvolvimento do SAGUE. Neste contexto, é apresentado o Sistema de Gerenciamento de Energia Elétrica - SISGEN, um sistema de informação para suporte à gestão de energia elétrica e de contratos de fornecimento, cujos dados coletados podem ser analisados através do SAGUE. A aplicação do SAGUE é tratada na forma de um estudo de caso no qual se analisa a correlação existente entre o consumo de energia elétrica da CUASO - Cidade Universitária Armando de Sales Oliveira, obtido através do SISGEN, e as medições de temperatura ambiente, fornecidas pelo IAG - Instituto de Astronomia, Geofísica e Ciências Atmosféricas da USP. / This work deals with specification, development and utilization of the Support System for Utility and Energy Management - SAGUE, a system created to assist in analysis of data collected from utilities systems as compressed air, vapor, water pumping systems, environmental conditioning systems and others, integrated with energy consumption and climatic measurements. The development of SAGUE was based on concepts and methodologies from Decision Support System as Data Warehouse and OLAP - Online Analytical Processing - in order to transform data measurements in information that guide the actions for energy conservation and rational utilization. The main characteristics of Data Warehouse and OLAP tools that influenced in the specifications and development of SAGUE are described in this work. In addition, this text deals with power management and energy management systems in order to present the environment that motivated the SAGUE development. Within this context, it is presented the Electrical Energy Management System - SISGEN, a system for energy management support, whose electrical measurements can be analyzed by SAGUE. The SAGUE utilization is presented in a case study that discusses the relation between electrical energy consumption of CUASO - Cidade Universitária Armando de Sales Oliveira, obtained throughout SISGEN, and the local temperature measurements supplied by IAG - Institute of Astronomic and Atmospheric Science of USP.

With a changing global climate, plant pathologists must understand the impact aberrant weather events may have on the development of plant diseases. Fungal plant infections are largely dependent on temperature and precipitation, climate parameters that are predicted to change more in this century. Venturia inaequalis causes apple scab, one of the most destructive apple diseases of temperate growing regions. Temperature and precipitation drive apple scab infections and forecast models, which guide growers in efficient, effective fungicide applications. In some recent years in the Northeast, these models have failed to accurately predict when ascospores of this fungus are available to cause primary infections, prompting more fungicide intensive management. Identifying cause(s) of model failures will restore confidence in them, enabling growers to reduce fungicide use. As technology becomes an increasingly important component of on farm decision-making, so does educating new farmers and agricultural students in the benefits of Integrated Pest Management and challenges associated with models early on in their college educational experience. This research attempts to identify reasons for ascospore maturity model failures, determine to what degree critical ascospore maturity parameters have changed and create a tool that educators may use to engage undergraduate students in the complexities of Integrated Pest Management research and modern farming. It will more specifically do the following: 1) Dry periods will be analyzed to determine if frequency and duration are increasing, causing the fungus to mature over a longer period of time than models currently estimate. 2) Degree-days during fall and winter will be examined to estimate what effect a warming climate may have on ascospore and tree development, and ultimately apple scab occurrence. The research will use lab and field observations to track the development of V. inaequalis ascospores, the source of primary apple scab infections. These observations will be compared to infection events and spore maturation forecasts from models currently used by apple growers in the Northeast. 3) A case study developed for publication in American Phytopathological Societies’ Plant Health Instructor will provide early career college students with an introduction to forecasting models, Integrated Pest Management and the challenges associated with climate variability.

L’optimisation de la maintenance industrielle revêt différents aspects suivant les objectifs fixés par l’exploitant industriel. L’objectif le plus courant est la réduction des arrêts et des pannes. Le but est d’assurer une disponibilité élevée de l’équipement. Nous allons plus loin en nous posant la question de l’efficience des coûts de maintenance et de la rentabilité. La réponse donnée ici à cette question provient des résultats d’une fonction de coût associée à une plateforme de simulation basée sur les systèmes multiagents. Le choix du paradigme Agent est motivé par l’utilisation des SMA à d’autres fins de simulation et qu’ils garantissent une forme de souplesse quant à l’évolution du contexte métier. La thèse porte sur un modèle de systèmes multiagents destiné à améliorer la gestion des parcs éoliens à travers la définition d'un ensemble de critères financiers propres à l’exploitant éolien. / Optimization & maintenance in the Industrial sector covers different aspects according to the objectives set by the industrial operator. Their common goal is to reduce downtime and failures. For the windfarm operators the goal is to ensure the wind farms high availibility. We went one step further by asking the question of the efficiency of maintenance costs and the profitability. The answer to this question comes from the results of a cost function associated to a simulation model based on multiagents systems. The choice of the multiagent paradigm is motivated by the use of MAS for other simulation purposes and the fact that they guarantee a kind of flexibility regarding the evolution in a moving business context. This Phd thesis focuses on a multi-agent systems model designed to improve the management of wind farms through the definition of a set of financial criteria specific to the wind farm operators.

To date, several disciplines have broached the systems view of service and the engineering of service systems. Operations research applied to services began with a rather simplistic, macro view of resource integration in the form of data envelopment analysis (DEA), introduced by Charnes, Cooper and Rhodes in 1978 (Banker et al., 1984; Charnes et al., 1994). Micro models of service systems have tended to study the systems’ IT components (Hsu, 2009; Qiu 2009). Engineering, which has always been associated with ‘assembling pieces that work in specific ways’ (Ottino, 2004) and ‘a process of precise composition to achieve a predictable purpose and function’ (Fromm, 2010: 2), has contributed to greater scalability and purposeful control in service systems. However, the agents of the system are usually people whose activities may not easily be controlled by predictable processes and yet are critical aspects of the value-creating system (Ng et al., 2011b). There is need for a new combinative paradigm, such as third-generation activity theory, in which two or more activity systems come into contact, to explore dialogue, exchanging perspectives of multiple actors, resulting in networks or groups of activity systems that are constantly interacting (Marken, 2006; Nardi, 1996, Oliveros et al., 2010). While various systems approaches, such as general systems theory (von Bertalanffy, 1962); open systems theory (Boulding, 1956; Katz and Kahn, 1978); and viable systems approach (Barile, 2008; Beer, 1972; Golinelli, 2010), will not be reviewed here (see Ng et al., 2011a for a systems approach to service science), they share common tenets: boundaries, interfaces, hierarchy, feedback and adaptation to which most systems writers would add emergence, input, output and transformation (Kast and Rosenzweig, 1972). These terms may be used as a basis for a research agenda for the consideration of a service system.

Visual Analytics is a powerful tool for decision makers to gather new insights from data. Since Visual Analytics can be hard to get into at first, previous studies have been conducted to bridge the gap between industry experts and these tools. However, few studies have examined the user’s needs regarding how Visual Analytics can generate these valuable insights. In order to examine these needs, the area selected was returns in E-commerce since the returns are devastating both to the companies and to society. The companies collect a lot of data as the goods get returned, which can be visualized. In order to highlight the e-tailer’s needs for visualization tools for their return data, a qualitative empirical study has been conducted. A prototype was developed in order to aid the semi-structured interviews visually. Six e-tailers was interviewed and got to test the prototype, in order to analyze their needs for visualization tools. The results shows that some graphic elements performed better than others, and that return data needs to be presented in comparison to sales data to be relevant. The study’s findings suggests that predefined graphs helped the E-tailers to get into the Visual Analytics mindset and may work as a way to introduce more users into the world of Visual Analytics. / För beslutsfattare är Visual Analytics inom beslutsstödssystem ett kraftfullt verktyg för att få fram nya insikter ur data. Tidigare forskning inom området fokuserar på att brygga gapet mellan branschexperter och Visual Analytics eftersom verktygen ofta är svåra att sätta sig in i. Dock är det få studier som har undersökt vad användarna har för behov av visualiseringsverktygen för att kunna få ut dessa värdefulla insikter. För att undersöka behoven har returer inom E-handel valts ut som tillämpningsområde, eftersom returerna är skadliga för företagen och samhället i stort. I samband med att varor returneras samlar E-handlarna in en hel del data som kan visualiseras. För att identifiera vilka behov E-handlarna har på visualiseringsverktyg kopplat till denna returdata, genomfördes en kvalitativ empirisk studie. I och med att Visual Analytics är visuellt togs en prototyp fram för att enklare kunna genomföra semistrukturerade intervjuer. Sex stycken E-handlare har intervjuats och testat prototypen för att samla in vilka behov dessa har av visualiseringsverktyg. Det framkom att visa grafiska element var att föredra över andra, samt att returdata i sig inte är särskilt intressant för E-handlarna utan att ha den totala försäljningen att jämföra mot. Det visade sig att de flesta E-handlarna var helt nya till Visual Analytics och att de fördefinierade grafiska elementen hjälpte de till att komma in i verktyget samt väckte tankar för hur de skulle vilja arbeta sig vidare i verktyget.

A South African GSM telecommunications market consisting of two incumbents and an entering third player, is modelled utilising a non-linear, system dynamics approach. The model calculates subscriber choice based on a calculated utility. The utility is used to obtain a probability which is fed into a Bass type differential equation relating the different states in the model to their time derivatives. The model encapsulates all the prominent postpaid price plans in the market, as well as five different demographic market segments. Model Predictive Control is used to synthesise a linear feedback controller which uses the observed market state to optimally determine a price time series for one of the operators’ products. The series will maximise Average Revenue Per User (ARPU) for the operator over the simulation time interval. Besides ARPU, the controller is also able to increase total revenue and minimise churn over the simulated interval for the optimising operator, and thus provides valuable decision support to the marketing management of such an operator. / Dissertation (MEng (Electronic))--University of Pretoria, 2004. / Electrical, Electronic and Computer Engineering / unrestricted