An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Indexing and retrieval in digital libraries : developing taxonomies for a repository of decision technologies

DecisionNet is an online Internet-based repository of decision technologies. It links remote users with these technologies and provides a directory service to enable search and selection of suitable technologies. The ability to retrieve relevant objects through search mechanisms is basic to any repository's success and usability and depends on effective classification of the decision technologies. This thesis develops classification methods to enable indexing of the DecisionNet repository. Existing taxonomies for software and other online repositories are examined. Criteria and principles for a good taxonomy are established and systematically applied to develop DecisionNet taxonomies. A database design is developed to store the taxonomies and to classify the technologies in the repository. User interface issues for navigation of a hierarchical classification system are discussed. A user interface for remote World Wide Web users is developed. This user interface is designed for browsing the taxonomy structure and creating search parameters online. Recommendations for the implementation of a repository search mechanism are given.http://archive.org/details/indexingndretrie1094532199NAU.S. Navy (U.S.N.) authorApproved for public release; distribution is unlimited

EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"

info:eu-repo/semantics/publishedVersio

A framework for knowledge discovery within business intelligence for decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Business Intelligence (BI) techniques provide the potential to not only efficiently manage but further analyse and apply the collected information in an effective manner. Benefiting from research both within industry and academia, BI provides functionality for accessing, cleansing, transforming, analysing and reporting organisational datasets. This provides further opportunities for the data to be explored and assist organisations in the discovery of correlations, trends and patterns that exist hidden within the data. This hidden information can be employed to provide an insight into opportunities to make an organisation more competitive by allowing manager to make more informed decisions and as a result, corporate resources optimally utilised. This potential insight provides organisations with an unrivalled opportunity to remain abreast of market trends. Consequently, BI techniques provide significant opportunity for integration with Decision Support Systems (DSS). The gap which was identified within the current body of knowledge and motivated this research, revealed that currently no suitable framework for BI, which can be applied at a meta-level and is therefore tool, technology and domain independent, currently exists. To address the identified gap this study proposes a meta-level framework: - ‘KDDS-BI’, which can be applied at an abstract level and therefore structure a BI investigation, irrespective of the end user. KDDS-BI not only facilitates the selection of suitable techniques for BI investigations, reducing the reliance upon ad-hoc investigative approaches which rely upon ‘trial and error’, yet further integrates Knowledge Management (KM) principles to ensure the retention and transfer of knowledge due to a structured approach to provide DSS that are based upon the principles of BI. In order to evaluate and validate the framework, KDDS-BI has been investigated through three distinct case studies. First KDDS-BI facilitates the integration of BI within ‘Direct Marketing’ to provide innovative solutions for analysis based upon the most suitable BI technique. Secondly, KDDS-BI is investigated within sales promotion, to facilitate the selection of tools and techniques for more focused in store marketing campaigns and increase revenue through the discovery of hidden data, and finally, operations management is analysed within a highly dynamic and unstructured environment of the London Underground Ltd. network through unique a BI solution to organise and manage resources, thereby increasing the efficiency of business processes. The three case studies provide insight into not only how KDDS-BI provides structure to the integration of BI within business process, but additionally the opportunity to analyse the performance of KDDS-BI within three independent environments for distinct purposes provided structure through KDDS-BI thereby validating and corroborating the proposed framework and adding value to business processes

Report of the seventh meeting of the Caribbean Development Roundtable

The seventh meeting of the Caribbean Development Roundtable was co-hosted by the Economic Commission for Latin America and the Caribbean (ECLAC) subregional headquarters for the Caribbean and the Government of Suriname. The premise for this year’s Roundtable considers that while the prospects for the Caribbean to adequately meet its development needs have been seriously constrained, new opportunities have arisen, both international and regional, that may support the subregion’s efforts to find a robust recovery, and to become more resilient. The meeting focused on five interrelated thematic areas: (i) addressing vulnerability, debt and liquidity in the Caribbean; (ii) the multidimensional vulnerability index (MVI) as an effective measure of vulnerability in small middle-income Caribbean countries; (iii) responding to the data and statistical capacity needs of the Caribbean (iv) global partnership for repositioning, recovery and resilience in the Caribbean and (v) economic restructuring and diversification towards deepening the integration of the Caribbean into Latin America and the global economy. With respect to the Caribbean Resilience Fund (CRF), the meeting reiterated their support for the reconceptualized CRF which now focuses on two windows: resilience building/sustainable resilience and debt restructuring and liquidity enhancement. The establishment of such a Fund can contribute significantly to providing the financial resources to finance the critical investments required to reduce the economic, financial, fiscal, and environmental vulnerabilities experienced by Caribbean countries

A MERGE Model with Endogenous Technological Change and the Cost of Carbon Stabilization

Two stylized backstop systems with endogenous technological learning formulations (ETL) are introduced in MERGE: one for the electric and the other for the non-electric markets. Then the model is applied to analyze the impacts of ETL on carbon-mitigation policy, contrasting the resulting impacts with the situation without learning. As the model considers endogenous technological change in the energy sector only some exogenous key parameters defining the production function are varied together with the assumed learning rates to check the robustness of our results. Based on model estimations and the sensitivity analyses we conclude that increased commitments for the development of new technologies to advance along their learning curves has a potential for substantial reductions in the cost of climate mitigation helping to reach safe concentrations of carbon in the atmosphere.Climate change stabilization policies, Non-linear optimization, Induced technological change, Energy and macroeconomy

A framework for knowledge discovery within business intelligence for decision support

Business Intelligence (BI) techniques provide the potential to not only efficiently manage but further analyse and apply the collected information in an effective manner. Benefiting from research both within industry and academia, BI provides functionality for accessing, cleansing, transforming, analysing and reporting organisational datasets. This provides further opportunities for the data to be explored and assist organisations in the discovery of correlations, trends and patterns that exist hidden within the data. This hidden information can be employed to provide an insight into opportunities to make an organisation more competitive by allowing manager to make more informed decisions and as a result, corporate resources optimally utilised. This potential insight provides organisations with an unrivalled opportunity to remain abreast of market trends. Consequently, BI techniques provide significant opportunity for integration with Decision Support Systems (DSS). The gap which was identified within the current body of knowledge and motivated this research, revealed that currently no suitable framework for BI, which can be applied at a meta-level and is therefore tool, technology and domain independent, currently exists. To address the identified gap this study proposes a meta-level framework: - ‘KDDS-BI’, which can be applied at an abstract level and therefore structure a BI investigation, irrespective of the end user. KDDS-BI not only facilitates the selection of suitable techniques for BI investigations, reducing the reliance upon ad-hoc investigative approaches which rely upon ‘trial and error’, yet further integrates Knowledge Management (KM) principles to ensure the retention and transfer of knowledge due to a structured approach to provide DSS that are based upon the principles of BI. In order to evaluate and validate the framework, KDDS-BI has been investigated through three distinct case studies. First KDDS-BI facilitates the integration of BI within ‘Direct Marketing’ to provide innovative solutions for analysis based upon the most suitable BI technique. Secondly, KDDS-BI is investigated within sales promotion, to facilitate the selection of tools and techniques for more focused in store marketing campaigns and increase revenue through the discovery of hidden data, and finally, operations management is analysed within a highly dynamic and unstructured environment of the London Underground Ltd. network through unique a BI solution to organise and manage resources, thereby increasing the efficiency of business processes. The three case studies provide insight into not only how KDDS-BI provides structure to the integration of BI within business process, but additionally the opportunity to analyse the performance of KDDS-BI within three independent environments for distinct purposes provided structure through KDDS-BI thereby validating and corroborating the proposed framework and adding value to business processes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Coastal management and adaptation: an integrated data-driven approach

Coastal regions are some of the most exposed to environmental hazards, yet the coast is the preferred settlement site for a high percentage of the global population, and most major global cities are located on or near the coast. This research adopts a predominantly anthropocentric approach to the analysis of coastal risk and resilience. This centres on the pervasive hazards of coastal flooding and erosion. Coastal management decision-making practices are shown to be reliant on access to current and accurate information. However, constraints have been imposed on information flows between scientists, policy makers and practitioners, due to a lack of awareness and utilisation of available data sources. This research seeks to tackle this issue in evaluating how innovations in the use of data and analytics can be applied to further the application of science within decision-making processes related to coastal risk adaptation. In achieving this aim a range of research methodologies have been employed and the progression of topics covered mark a shift from themes of risk to resilience. The work focuses on a case study region of East Anglia, UK, benefiting from the input of a partner organisation, responsible for the region’s coasts: Coastal Partnership East. An initial review revealed how data can be utilised effectively within coastal decision-making practices, highlighting scope for application of advanced Big Data techniques to the analysis of coastal datasets. The process of risk evaluation has been examined in detail, and the range of possibilities afforded by open source coastal datasets were revealed. Subsequently, open source coastal terrain and bathymetric, point cloud datasets were identified for 14 sites within the case study area. These were then utilised within a practical application of a geomorphological change detection (GCD) method. This revealed how analysis of high spatial and temporal resolution point cloud data can accurately reveal and quantify physical coastal impacts. Additionally, the research reveals how data innovations can facilitate adaptation through insurance; more specifically how the use of empirical evidence in pricing of coastal flood insurance can result in both communication and distribution of risk. The various strands of knowledge generated throughout this study reveal how an extensive range of data types, sources, and advanced forms of analysis, can together allow coastal resilience assessments to be founded on empirical evidence. This research serves to demonstrate how the application of advanced data-driven analytical processes can reduce levels of uncertainty and subjectivity inherent within current coastal environmental management practices. Adoption of methods presented within this research could further the possibilities for sustainable and resilient management of the incredibly valuable environmental resource which is the coast

Covid-19 and perceived travel risks: the development of a risk evaluation index using Delphi-based and MCDA applications

This work addresses the problem of changing travel risk perceptions of travellers following the aftermath of the COVID-19 pandemic. Following the unprecedented and global health crisis of COVID-19, without a doubt, there has been a tremendous impact on global tourism for two reasons; 1) the imposed travel restrictions discouraging people to travel; and 2) the increased anxieties of travellers in terms of responding to the new travel landscape. The main goal of this study was to identify and weight the important travel risks that are emerging and to create a risk evaluation index in which destinations and strategic interventions’ performance can be measured. The secondary objectives to this study include to contribute to a better understanding of risk perceptions held by travellers in a pandemic situation and apply a multimethodology to the concept of tourist perceived risk that has, to the knowledge of the author, never been carried out before. Empiric investigation analysed a sample of South African travellers’ travel risk perceptions through the use of the Delphi Technique and Multicriteria Decision Analysis (MCDA). The results equip the tourism industry, practitioners and managers with the information needed to evaluate tourist risk perception following a global pandemic, but can also be further applied to other contexts. This allows for the implementation of response strategies to encourage travel and contribute to the recuperation of the tourism sector following the COVID-19 pandemic. The findings from the Delphi-based survey indicate that tourist perceived risks are multidimensional, with first-level dimensions being categories of Financial, Performance, Planning and Regulation risks, which can be further sub-divided into categories that include additional expenses, exchange rates, refunds-related, destination performance, transportation performance, researching-related, psychological, lockdowns, testing-related and comfort-related criteria. MCDA applications, using MACBETH approaches, found that the risk criteria that are considered to be of highest importance to overall travel risk perception include additional expenses, exchange rates and refunds-related factors – with weightings of 20.60, 16.80 and 12.47 respectively. The risk evaluation index that was constructed as a result of this study was applied to five tourist destinations to evaluate their performance with regards to the perceived travel risks identified. Results suggested that the United Kingdom performs better (i.e., is ‘safer’) in terms of this particular South African traveller sample’s risk perceptions. This kind of research contributes to the literature in two ways: methodologically, by applying MCDA and Delphi techniques to the context of tourist risk perceptions, and by the development of a risk evaluation index.Este trabalho aborda o problema das mudanças de perceção de risco de viagem dos turistas durante e após a pandemia de COVID-19. Após a crise de saúde pública global e sem precedentes de COVID-19, houve, sem dúvida, um tremendo impacto no turismo global por dois motivos; 1) as restrições de viagem impostas que desencorajam as pessoas a viajar; e 2) o aumento da ansiedade dos viajantes em responder ao novo cenário de viagens. Desde 2000, o turismo tem enfrentado uma variedade de doenças infeciosas (a título de exemplo, gripe suína, SARS, gripe aviária, Ébola), em que os efeitos negativos foram isolados em países ou regiões específicos. No entanto, desde o surto de COVID-19 enquanto novo coronavírus em Wuhan, China, no início de janeiro de 2020, a disseminação atingiu todos o planeta, fazendo com que a Organização Mundial da Saúde o declarasse uma pandemia a 11 de março de 2020. Por conseguinte, a decisão de viajar envolve riscos, mais do que anteriormente. Mesmo que a doença seja contida, as perceções de risco e a falta de segurança podem persistir e impedir que as pessoas viajem no futuro próximo. De particular interesse para os investigadores de turismo no atual clima de pandemia é a influência da crise de saúde pública do COVID-19 nas perceções de risco dos consumidores de viagens e como essas perceções de risco potencialmente influenciarão o comportamento de viagem no período pós-crise. Considera-se imperativo prever a trajetória de mudança no comportamento do turista, a fim de ajudar os gestores de turismo a responder de forma ideal à situação. O risco percebido como tema de pesquisa tem recebido atenção considerável ao longo das décadas. Normalmente, os estudiosos dividem os tipos de riscos percebidos com a compra de produtos ou serviços gerais como financeiro, físico, desempenho, social, psicológico e tempo/conveniência. Na literatura relacionada com viagens e turismo, o risco tem sido frequentemente examinado usando praticamente o mesmo sistema de classificação. Essa tipologia e classificação na literatura de turismo, baseada em riscos em geral e não riscos relevantes para viajar, pode ser muito ampla e, portanto, impede respostas adequadas de gestão. Caso contrário, resta apenas uma tipologia genérica e ampla de fatores que compreendem cada categoria de riscos que podem afetar significativamente as intenções de viagem, tornando difícil para os gestores de viagens desenvolver estratégias apropriadas para acalmar as preocupações dos viajantes em perspetiva. Isso é especialmente importante desde o surto da pandemia de COVID-19, pois a literatura anterior sugeriu que as crises de saúde têm impactos consideráveis nas perceções de risco dos turistas. Portanto, a pesquisa como a que se apresenta nesta dissertação é particularmente relevante para o clima atual em que o setor de turismo opera, pois a necessidade de reavaliar e explorar as diferentes dimensões de risco que podem estar atuando para inibir o desejo de viajar para os turistas é importante agora mais do que nunca. O principal objetivo deste estudo foi identificar e ponderar os importantes riscos de viagem que estão a surgir e criar um índice de avaliação de risco no qual o desempenho dos destinos e das intervenções estratégicas possa ser medido. Os objetivos deste estudo incluem: 1) contribuir para uma melhor compreensão das perceções de risco dos viajantes em situação de pandemia; 2) desenvolver uma ferramenta pela qual os destinos e futuras intervenções para abordar as perceções de risco possam ser medidos, através da ponderação de diferentes critérios de risco usando MCDA; e 3) a aplicação de uma metodologia combinando procedimentos baseados em Delphi e modelos multicritério (MCDA), utilizando abordagens MACBETH ao tema do risco apercebido em viagens, contribuindo para a investigação de forma inovadora. O MCDA tem sido criticado por ser tecnicamente complicado. Portanto, é necessário o desenvolvimento de uma ferramenta para apoiar os formuladores de políticas locais na seleção de critérios e na classificação do desempenho das intervenções nesses critérios. A ferramenta de classificação é composta por critérios, definições de critérios, pesos de critérios e escalas de classificação para medir o impacto geral das intervenções de risco apercebido e apoiar os objetivos de definição de prioridades. Tal ferramenta poderia ser usada num processo de definição de prioridades mais amplo, baseado em MCDA, para desenvolver estratégias de controlo de risco num ambiente local. O desenvolvimento de tal índice de risco fornece uma ferramenta abrangente ao: 1) permitir a medição e monitorização das perceções gerais de risco dos turistas; 2) dar conta da natureza multidimensional das perceções de risco; 3) prever e discutir o impacto das políticas de turismo multinível que podem abordar as perceções de risco do turista; e 4) fornecer uma base para o diálogo político multinível sobre a indústria do turismo e questões de mercado. A investigação empírica analisou uma amostra de perceções de risco de viagem de turistas sul-africanos através do uso da Técnica Delphi e Análise de Decisão Multicritério (MCDA). Os resultados fornecem à indústria do turismo, profissionais e gestores as informações necessárias para avaliar a perceção de risco do turista após uma pandemia global, mas também podem ser aplicados a outros contextos. Isto permite a implementação de estratégias de resposta para incentivar as viagens e contribuir para a recuperação do setor de turismo após a pandemia de COVID-19. Os resultados da investigação baseada em Delphi indicam que os riscos apercebidos pelos turistas são multidimensional, com as dimensões de primeiro nível sendo categorias de riscos Financeiros, de Desempenho, Planeamento e Regulação, que podem ser subdivididas em categorias que incluem despesas adicionais, taxas de câmbio, relacionadas com reembolsos, desempenho de destino, desempenho de transporte, relacionados com pesquisa, critérios psicológicos, bloqueios, relacionados com testes e relacionados com conforto. As aplicações MCDA, usando abordagens MACBETH, determinaram que os critérios de risco considerados de maior importância para a perceção geral do risco de viagem incluem despesas adicionais, taxas de câmbio e fatores relacionados com reembolsos - com ponderações de 20,60, 16,80 e 12,47, respetivamente. O índice de avaliação de risco que foi construído como resultado deste estudo foi aplicado a cinco destinos turísticos para avaliar seu desempenho em relação aos riscos percebidos de viagem identificados. Os resultados sugeriram que o Reino Unido tem um desempenho melhor (ou seja, é “mais seguro”) em termos das perceções de risco dessa amostra de viajantes sul-africanos em particular. Esse tipo de pesquisa contribui para a literatura de duas maneiras: metodologicamente, aplicando as técnicas MCDA e Delphi ao contexto das perceções de risco do turista, e pelo desenvolvimento de um índice de avaliação de risco

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference