Ball State University LibrariesLibrary services and resources for knowledge buildingMasters ThesesThere is no abstract available for this thesis.

Made available in DSpace on 2014-12-17T14:53:17Z (GMT). No. of bitstreams: 1 MarceloCS_DISSERT.pdf: 2801303 bytes, checksum: 8118c830c7f441ce1b3190bca6570c77 (MD5) Previous issue date: 2012-03-28 / The relevance of rising healthcare costs is a main topic in complementary health companies in Brazil. In 2011, these expenses consumed more than 80% of the monthly health insurance in Brazil. Considering the administrative costs, it is observed that the companies operating in this market work, on average, at the threshold between profit and loss. This paper presents results after an investigation of the welfare costs of a health plan company in Brazil. It was based on the KDD process and explorative Data Mining. A diversity of results is presented, such as data summarization, providing compact descriptions of the data, revealing common features and intrinsic observations. Among the key findings was observed that a small portion of the population is responsible for the most demanding of resources devoted to health care / A relev?ncia do aumento dos custos assistenciais ? um fen?meno que consiste em uma das principais discuss?es na ?rea da sa?de suplementar. Em 2011, estas despesas representaram mais de 80% das mensalidades dos planos de sa?de no Brasil. Considerando as despesas administrativas, observa-se que as empresas atuantes neste mercado trabalham, em m?dia, no limiar entre lucro e preju?zo. Esta disserta??o, com base no processo de descoberta de conhecimento (KDD) e minera??o de dados, realizou uma investiga??o dos custos assistenciais de uma operadora de plano de sa?de. Os resultados propiciam descri??es compactas dos dados, revelando caracter?sticas comuns e intr?nsecas das observa??es. Dentre as principais conclus?es observa-se que uma reduzida parcela da popula??o ? respons?vel por demandar a maior parte dos recursos destinados aos cuidados com sa?de

This master’s thesis concerns about the use of data mining techniques in banking, insurance and shopping centres industries. The thesis theoretically describes algorithms and methodology CRISP-DM dedicated to data mining processes. With usage of theoretical knowledge and methods, the thesis suggests possible solution for various industries within business intelligence processes.

Le partage des données de façon confidentielle préoccupe un bon nombre d’acteurs, peu importe le domaine. La recherche évolue rapidement, mais le manque de solutions adaptées à la réalité d’une entreprise freine l’adoption de bonnes pratiques d’affaires quant à la protection des renseignements sensibles. Nous proposons dans ce mémoire une solution modulaire, évolutive et complète nommée PEPS, paramétrée pour une utilisation dans le domaine de l’assurance. Nous évaluons le cycle entier d’un partage confidentiel, de la gestion des données à la divulgation, en passant par la gestion des forces externes et l’anonymisation. PEPS se démarque du fait qu’il utilise la contextualisation du problème rencontré et l’information propre au domaine afin de s’ajuster et de maximiser l’utilisation de l’ensemble anonymisé. À cette fin, nous présentons un algorithme d’anonymat fortement contextualisé ainsi que des mesures de performances ajustées aux analyses d’expérience. / Privacy-preserving data sharing is a challenge for almost any enterprise nowadays, no matter their field of expertise. Research is evolving at a rapid pace, but there is still a lack of adapted and adaptable solutions for best business practices regarding the management and sharing of privacy-aware datasets. To this problem, we offer PEPS, a modular, upgradeable and end-to-end system tailored for the need of insurance companies and researchers. We take into account the entire cycle of sharing data: from data management to publication, while negotiating with external forces and policies. Our system distinguishes itself by taking advantage of the domain-specific and problem-specific knowledge to tailor itself to the situation and increase the utility of the resulting dataset. To this end, we also present a strongly contextualised privacy algorithm and adapted utility measures to evaluate the performance of a successful disclosure of experience analysis.

Cette thèse de doctorat porte sur l’analyse des dommages et sur l’évaluation des coûts induits sur les habitations par les submersions marines. L’étude se base sur les données d’assurance de deux évènements récents ayant touché la France et causé des submersions sur les côtes bretonnes et atlantiques : les tempêtes Johanna (mars 2008) et Xynthia (février 2010).Dans un premier temps, l’analyse des données d’expertise et d’indemnisation d’assurance, en lien avec celle des paramètres de l’aléa et des enjeux exposés, a eu pour but la meilleure compréhension des différents types de dommages, et l’explication des coûts observés. En parallèle, un travail de modélisation de l’aléa a été réalisé à une échelle régionale, afin de déterminer des indicateurs des forçages météo-marins, et à une échelle locale, afin de préciser les processus d’endommagement sur les sites étudiés pour les deux tempêtes. La caractérisation de la vulnérabilité et de la valeur des enjeux (coûts de construction) a été menée à l’aide de différents paramètres issus de bases de données nationales (INSEE et IGN) et de campagnes de terrain.Dans un second temps, les informations recueillies ont permis la construction de modèles empiriques de prédiction du coût des dommages aux habitations spécifiques à l’aléa submersion marine, outils aujourd’hui inexistants en France. Les différents types de modèles testés sont basés sur des approches statistiques univariées (fonctions d’endommagement) et multivariées. L’apport des données d’assurance à la réalisation de tels modèles est discuté, et des recommandations ainsi que des perspectives de recherche sont évoquées, afin de rendre ces modèles opérationnels et d’augmenter leur capacité de prédiction des coûts d’évènements catastrophiques futurs. / This PhD work aims at analysing damage and evaluating costs related to coastal flooding on residential buildings. The study is based on insurance data from two recent storm events, which caused coastal flooding in the Brittany and Atlantic regions in France: the storms Johanna (March 2008) and Xynthia (February 2010).At first, the analysis of insurance indemnities and loss adjustment data, in connection with hazard parameters, and exposed assets characteristics, allowed a better understanding of the different types of damage and costs observed. At the same time, hazard models were carried out at a regional level, in order to identify meteorological forcing indicators, and at a local level, in order to link damages to the associated physical flooding processes on the studied sites. The characterization of the asset’s vulnerability and values (construction costs) was conducted using different parameters from national databases (INSEE and IGN) and field survey.In a second step, empirical cost-assessment models were built on the basis of the data analysed, using univariate (damage functions) and multivariate statistical approaches. This study is the first attempt in France to elaborate models for the prediction of damage costs linked to coastal flooding on housing. The contribution of insurance data to the implementation of such models is discussed, and recommendations and research perspectives are expressed, in order to make the models operational and to increase their capacity to predict future catastrophic events costs.

Senior Health Care System (SHCS) is created for users to enter participants' conditions and store information in a central database. When users are ready for quarterly assessments the system generates a simple summary that can be reviewed, modified, and saved as part of the summary assessments, which are required by Federal and California law.