Tesis sobre el tema "Data quality indicators"

Les séries temporelles sont des collections de données obtenues par des mesures dans le temps. Cette données vise à fournir des éléments de réflexion pour l'extraction d'événements et à les représenter dans une configuration compréhensible pour une utilisation ultérieure. L'ensemble du processus de découverte et d'extraction de modèles à partir de l'ensemble de données s'effectue avec plusieurs techniques d'extraction, notamment l'apprentissage automatique, les statistiques et les clusters. Ce domaine est ensuite divisé par le nombre de sources adoptées pour surveiller un phénomène. Les séries temporelles univariées lorsque la source de données est unique, et les séries temporelles multivariées lorsque la source de données est multiple. La série chronologique n'est pas une structure simple. Chaque observation de la série a une relation forte avec les autres observations. Cette interrelation est la caractéristique principale des séries temporelles, et toute opération d'extraction de séries temporelles doit y faire face. La solution adoptée pour gérer l'interrelation est liée aux opérations d'extraction. Le principal problème de ces techniques est de ne pas adopter d'opération de prétraitement sur les séries temporelles. Les séries temporelles brutes comportent de nombreux effets indésirables, tels que des points bruyants ou l'énorme espace mémoire requis pour les longues séries. Nous proposons de nouvelles techniques d'exploration de données basées sur l'adoption des caractéristiques plus représentatives des séries temporelles pour obtenir de nouveaux modèles à partir des données. L'adoption des caractéristiques a un impact profond sur la scalabilité des systèmes. En effet, l'extraction d'une caractéristique de la série temporelle permet de réduire une série entière en une seule valeur. Par conséquent, cela permet d'améliorer la gestion des séries temporelles, en réduisant la complexité des solutions en termes de temps et d'espace. FeatTS propose une méthode de clustering pour les séries temporelles univariées qui extrait les caractéristiques les plus représentatives de la série. FeatTS vise à adopter les particularités en les convertissant en réseaux de graphes pour extraire les interrelations entre les signaux. Une matrice de cooccurrence fusionne toutes les communautés détectées. L'intuition est que si deux séries temporelles sont similaires, elles appartiennent souvent à la même communauté, et la matrice de cooccurrence permet de le révéler. Dans Time2Feat, nous créons un nouveau clustering de séries temporelles multivariées. Time2Feat propose deux extractions différentes pour améliorer la qualité des caractéristiques. Le premier type d'extraction est appelé extraction de caractéristiques intra-signal et permet d'obtenir des caractéristiques à partir de chaque signal de la série temporelle multivariée. Inter-Signal Features Extraction permet d'obtenir des caractéristiques en considérant des couples de signaux appartenant à la même série temporelle multivariée. Les deux méthodes fournissent des caractéristiques interprétables, ce qui rend possible une analyse ultérieure. L'ensemble du processus de clustering des séries temporelles est plus léger, ce qui réduit le temps nécessaire pour obtenir le cluster final. Les deux solutions représentent l'état de l'art dans leur domaine. Dans AnomalyFeat, nous proposons un algorithme pour révéler des anomalies à partir de séries temporelles univariées. La caractéristique de cet algorithme est la capacité de travailler parmi des séries temporelles en ligne, c'est-à-dire que chaque valeur de la série est obtenue en streaming. Dans la continuité des solutions précédentes, nous adoptons les fonctionnalités de révélation des anomalies dans les séries. Avec AnomalyFeat, nous unifions les deux algorithmes les plus populaires pour la détection des anomalies : le clustering et le réseau neuronal récurrent. Nous cherchons à découvrir la zone de densité du nouveau point obtenu avec le clustering
Time series are collections of data obtained through measurements over time. The purpose of this data is to provide food for thought for event extraction and to represent them in an understandable pattern for later use. The whole process of discovering and extracting patterns from the dataset is carried out with several extraction techniques, including machine learning, statistics, and clustering. This domain is then divided by the number of sources adopted to monitor a phenomenon. Univariate time series when the data source is single and multivariate time series when the data source is multiple. The time series is not a simple structure. Each observation in the series has a strong relationship with the other observations. This interrelationship is the main characteristic of time series, and any time series extraction operation has to deal with it. The solution adopted to manage the interrelationship is related to the extraction operations. The main problem with these techniques is that they do not adopt any pre-processing operation on the time series. Raw time series have many undesirable effects, such as noisy points or the huge memory space required for long series. We propose new data mining techniques based on the adoption of the most representative features of time series to obtain new models from the data. The adoption of features has a profound impact on the scalability of systems. Indeed, the extraction of a feature from the time series allows for the reduction of an entire series to a single value. Therefore, it allows for improving the management of time series, reducing the complexity of solutions in terms of time and space. FeatTS proposes a clustering method for univariate time series that extracts the most representative features of the series. FeatTS aims to adopt the features by converting them into graph networks to extract interrelationships between signals. A co-occurrence matrix merges all detected communities. The intuition is that if two time series are similar, they often belong to the same community, and the co-occurrence matrix reveals this. In Time2Feat, we create a new multivariate time series clustering. Time2Feat offers two different extractions to improve the quality of the features. The first type of extraction is called Intra-Signal Features Extraction and allows to obtain of features from each signal of the multivariate time series. Inter-Signal Features Extraction is used to obtain features by considering pairs of signals belonging to the same multivariate time series. Both methods provide interpretable features, which makes further analysis possible. The whole time series clustering process is lighter, which reduces the time needed to obtain the final cluster. Both solutions represent the state of the art in their field. In AnomalyFeat, we propose an algorithm to reveal anomalies from univariate time series. The characteristic of this algorithm is the ability to work among online time series, i.e. each value of the series is obtained in streaming. In the continuity of previous solutions, we adopt the functionality of revealing anomalies in the series. With AnomalyFeat, we unify the two most popular algorithms for anomaly detection: clustering and recurrent neural network. We seek to discover the density area of the new point obtained with clustering

Philosophiae Doctor - PhD
This study investigates the relevance of quality measurement indicators at higher education libraries for faculty academics, librarians, and students. The study followed a mixed-method design with a mixture of quantitative and qualitative data collection. Faculty academics, librarians and students ranked the existing quality measurement indicators for South African higher education libraries. The findings revealed that for library quality measures to meet the needs of faculty academics, librarians, and students, the resources must be accessible both physically and virtually, and staff should be accountable and willing to offer services responsive to the users' needs and expectations of a safe, secure, and comfortable library space, be it physical or virtual. The qualitative data highlighted the importance of adequate resources and the adoption of new developments as measures for quality. Quality measurement indicators must include elements such as adequate funding; relevant resources aligned with teaching and learning programmes; programmes that are integrated into teaching plans; effective supplier collaboration with respect to the process of acquiring relevant learning materials; effective student training; communication of the value of library services and alignment with the student learning outcomes; research support in a digital environment with e-tools and website navigability; research data management; and open access, which is a prominent role of the library. Based on the data, there was a quality measure (process) that was commendable even though it did not form part of the existing quality measures nor a service whose relevance was assessed. The separation of undergraduate and postgraduate learning spaces was amongst those services that ranked quite high from the students' responses (qualitative data). Even though there were differences emphasized on each indicator by either faculty academics or students, there were also discrepancies in the interpretation of what each quality indicator means to each study population group. As the study of this nature has recommendations and gaps identified in terms of research findings, it is quite important to record that there was a series of gaps that were identified in terms of library expectations and perceptions. These gaps were suggested as part of further research that must be conducted to fill the void in terms of library users’ voices in the development of higher education library measurement indicators.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
In this paper, we proposed a method that allows us to formulate and evaluate process mining indicators through questions related to the process traceability, and to bring about a clear understanding of the process variables through data visualization techniques. This proposal identifies bottlenecks and violations of policies that arise due to the difficulty of carrying out measurements and analysis for the improvement of process quality assurance and process transformation. The proposal validation was carried out in a health clinic in Lima (Peru) with data obtained from an information system that supports the surgery block process. Finally, the results contribute to the optimization of decision-making by the medical staff involved in the surgery block process.
Revisión por pares

Within the DIECOFIS framework, ec3, the Division of Business Statistics from the Vienna University of Economics and Business Administration and ISTAT worked together to find methods to create a comprehensive database of enterprise data required for taxation microsimulations via integration of existing disparate enterprise data sources. This paper provides an overview of the broad spectrum of investigated methodology (including exact and statistical matching as well as imputation) and related statistical quality indicators, and emphasises the relevance of data integration, especially for official statistics, as a means of using available information more efficiently and improving the quality of a statistical agency's products. Finally, an outlook on an empirical study comparing different exact matching procedures in the maintenance of Statistics Austria's Business Register is presented.

Mestrado em Gestão de Sistemas de Informação
Numa época em que as empresas estão a dar mais e mais importância à implementação de sistemas de informação para suportar o seu negócio, o termo Master Data está a tornar-se mais usual uma vez que concerne a informação mais importante de uma empresa (p.e. dados de clientes e colaboradores). Manter níveis elevados de qualidade para estes dados é um desafio que precisa de ser medido através de indicadores de performance (p.e. Key Performance Indicators). O presente estudo tem o objetivo de investigar a definição, cálculo, divulgação e uso de Key Performance Indicators numa empresa multinacional. Para este propósito, uma formação de 6 meses foi providenciada pela empresa para explicar como calcular estes valores e como obter toda a informação necessária relativamente a estes indicadores dentro do departamento de Recursos Humanos. A análise mostrou que, apesar dos KPIs existentes estarem bem definidos e serem bem calculados, não são suficientes para incluir todas as classes de master data e são também muito abrangentes, tornando quase impossível que seja encontrada a raíz do problema dos RH na empresa.
In a time where companies are giving more and more importance to the implementation of information systems to support their businesses, the term Master Data is becoming more usual since it concerns the core information of a company (e.g. customer and employee data). Maintaining the highest quality for this data is nevertheless a challenge that needs to be measured through performance measures (for example: Key Performance Indicators). The present case study has the purpose of investigating the definition, calculation, divulgation and use of Key Performance Indicators within a multinational company. To this end, a training of 6 months was provided by the company to teach participants how to calculate these values and also how to obtain all the necessary information regarding these indicators within the Human Resources department. The analysis showed that, even though the existing KPIs are well defined and calculated, they are not enough to include all existing master data classes and are also too wide-ranging, making it almost impossible to find the root of the HR problems within the company.
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The purpose of this study is to explore differences in quality between Medicare Accountable Care Organizations (ACO) and Health Maintenance Organizations (HMO). Three outcomes measures reported by these plans use different methodologies but possess enough alignment to permit comparison: percent of diabetic patients with last HbA1c > 9.0%, colon cancer screening rate and ER visits per 1,000. These outcomes are the dependent variables (DV). A secondary purpose is to explore differences in quality based on the size of the beneficiary population served, using the same measures. As the Medicare program faces threats to its solvency in coming decades, with 10,000 baby boomers becoming eligible every day, and the ongoing national conversation about healthcare more generally, approaches to Value-Based Purchasing (VBP) are becoming more common. Organizations seeking to identify the types of VBP arrangements in which they should enter have precious little information on the comparative performance of VBP approaches relative to outcomes measures. Different structures create different incentives through the plan design and risk/reward. The convergence or dissipation of the plan incentives at the level of the provider, particularly in primary care, may be a source of variance. This study is retrospective, non-experimental, and uses publicly available data on the performance of Medicare ACO and HMO plans in calendar year 2015, for the identified measures. Using the Donabedian Structure-Process-Outcome framework, this study explores the impact of structure by type of plan and size of population served, relative to the outcomes. Race, average Hierarchical Condition Category (HCC) risk score and duration of operations are control variables. The analysis uses multiple hierarchical regression to better understand the relationship between the independent variables (IV) and DVs, after the impact of the control variables (CV). After controls, the IVs did offer some explanation of variation in outcomes. The ACO plans fared better on HbA1c control, while HMO plans had fewer ER visits per 1,000. No discernable difference existed between the HMO and ACO plans with regard to colon cancer screening rate. Serving larger populations led to better performance on all three measures. In general performance was worse on each measure in both models when the percent of not-White patients or average HCC risk score increased. A longer duration of operations also associated to better performance on the outcome measures.

Improving the role the nurse plays in health care delivery should be embodied in the performance improvement initiatives to successfully improve the quality of care that is delivered. The purpose of this evidence-based practice project was to collect performance improvement data and present it to staff who, in turn, used the information to improve practice and influence patient safety outcomes. The practice-focused question addressed what would occur if a tool that allowed frequent data trending was used to measure effectiveness of care and thereby influence key outcome measures. Duffy's quality caring model provided a framework for the study to support the need for the development of a dashboard for staff and to ensure that staff were informed as they developed interventions to improve patient outcomes. Publicly available data published by the Centers for Medicare/ Medicaid (CMS) for the Quality Star Report were explored to inform the project. Workgroups, comprised of volunteers from leadership and staff providing care at the bedside, were formed to implement practice changes based on the dashboard reports. By bringing the data to the attention of nurses within the organization, improvements were made in the overall score for safety of care from below national average (25th percentile of the reported 3,647 hospitals across the nation) to the same as national average (47th percentile) as reported by CMS. Through staff involvement, social change occurred as strategies were hardwired to improve categories of the Quality Star Report and ultimately patient care. The project showed that quality improvement tools can assist in empowering staff to understand the data needed to implement process improvement strategies.

A discordance exists between patients and physicians in the assessment of Systemic Lupus Erythematosus (SLE). A more patient-oriented research to address the multifaceted dimensions of the disease burden is warranted. The aim of this study was to investigate patients’ needs and expectations about their medical care, with the objective of integrating patient-reported data to the traditional physician evaluation towards a new shared strategy for the monitoring of SLE. The first step of the project was a systematic literature review of existing clinical and non-clinical indicators for the management of SLE and of currently available SLE-dedicated IT tools (Apps) designed to involve the patient in the gathering of clinical and quality of life data about their health. On the basis of the results of the literature review, ad-hoc on-line surveys have been designed, targeting European experts in the field and SLE patients. The aim was to investigate both the real use of such indicators in clinical practice and to identify needs and expectations experienced by doctors and patients in management of the disease. Finally, the survey results have been discussed in dedicated focus groups of patients, clinicians but also general practitioners, nurses and one caregiver. According to the project results, the key elements for the development of a new paradigm for SLE management should include: • MULTIDISCIPLINARITY and NETWORKING • PATIENT-PHYSICIAN COMMUNICATION • PATIENT’S EDUCATION • PATIENT’S EMPOWERMENT First of all, the ideal setting for the management of the disease seems to be represented by a dedicated “Lupus clinic”, where the patient can be in charge of a team of expert health care professionals. The networking with multi-disciplinary specialists should be encouraged. From both the patients’ and clinicians’ perspective emerged that the communication between the parts is often inadequate in terms of both contents and means. SLE patients declared that they have a real, shared hunger for information about the disease and, on the other hand, clinicians think that only an educated patient can be really empowered. Some instruments may be of help to make such an educational process to happen. Both clinicians and patients agreed on the fact that an ideal IT solution would be of benefit in the management of the disease. Self-management of simple tasks is important as one of the first steps towards patient’s engagement and empowerment. In conclusion, several unmet needs seem to be present in the management of SLE, both from the patient’s and the clinician’s point of view. In this setting, the results of the project pointed out a path to follow in the management of Lupus patients and lay the foundations for a new shared strategy for disease management in clinical practice which may serve as a prototype for other chronic diseases.

Using administrative data to measure the quality and safety of hospital care offers many opportunities. However, progress has been limited to few countries and predominantly to a small subset of broad measures, such as Hospital Standardised Mortality Rates. In this thesis, I investigate the potential advantages and feasibility - in terms of validity and applicability - of specialty-specific indicators. In the first part of my PhD work, I examine the case for specialty-specific indicators. I also present potential applications which overcome some of the existing shortcomings of previous uses of indicators based on administrative data. In the next stage of the project I focus on assessing feasibility by focusing on two specialties - stroke and obstetric care - conducting systematic reviews and consulting with experts to develop two indicator sets. As part of this, I identified the shortcomings in current use of indicators in these specialties. To investigate the limitations of these indicators, I applied the indicator definitions to English hospital administrative data (Hospital Episode Statistics, HES) and evaluated whether they can be used to discriminate between hospitals based on their performance and, importantly, to understand the effect of differences in coding practice. The final aspect of the research was to investigate alternative applications for the indicators which can overcome some of the shortcomings highlighted in both the prior analyses and existing literature. In doing so, I raise serious, robust shortcomings on the quality and safety of weekend care.

La croissance incessante des technologies numériques a renforcé les capacité à détecter, visualiser, analyser et gérer de manière autonome l'environnement qui nous entoure. Intelligent et plus connecté est la tendance dominante au sein du métavers émergent, où des environnements connectés sont conçus pour recueillir d'énormes quantités de données. Ces environnements contribuent à une meilleure compréhension des activités humaines, améliorent la qualité de vie et favorisent la durabilité. Cette thèse introduit deux concepts clés - le continuum de zones et l'obsolescence des données - visant à approfondir la sensibilisation aux environnements connectés en expliquant comment les données se déplacent entre les zones et en détectant quand les données deviennent pertinentes dans leur contexte. En abordant ces aspects souvent négligés, nous visons à améliorer l'interprétation et la gestion des données dans les environnements connectés.Cette thèse aborde quatre défis clés : (1) concevoir un modèle de données hautement expressif qui capture efficacement les composants essentiels des environnements connectés, y compris les zones d'accueil, les dispositifs intelligents et leurs réseaux de capteurs associés ; (2) établir un mécanisme pour identifier et tracer comment les données se propagent entre différentes zones ; (3) proposer une approche à grande échelle pour détecter l'obsolescence des données tout en tenant compte de l'ensemble de l'environnement ; et (4) traiter la nature dynamique des environnements connectés, telle que la mobilité des dispositifs, les pannes et les changements dans les configurations de zone (par exemple, fusionner plusieurs zones ou diviser une zone en plusieurs).Pour relever ces défis, nous proposons d'abord le modèle de données HSSN+ pour représenter de manière résiliente les éléments principaux dans les environnements connectés.Ensuite, en nous appuyant sur ce modèle, nous présentons deux cadres étroitement liés. Le premier est le cadre de calcul de continuité de zones pour les environnements connectés, qui formalise la notion de continuité de zones et permet de comprendre comment les données circulent entre les zones. Le second est le cadre de détection de l'obsolescence des données dans les environnements connectés, qui fournit une méthode structurée d'identification et de calcul de l'obsolescence des données sur la base de mesures de qualité prédéfinies.Les deux cadres permettent un contrôle et une compréhension efficaces de tous les environnements connectés
The relentless expansion of digital technologies has enhanced the capabilities of collecting, visualizing, analyzing, and autonomously managing the environment surrounding us.Intelligent and increasingly connected is the prevailing trend within the rising metaverse, where connected environments are driven to gather a plethora of data.These environments contribute to a better understanding of human activities, improve quality of life, and ease paths for sustainability.This thesis introduces two pivotal concepts - zone continuum and data obsolescence - furthering awareness of connected environments by explaining how data crosses between zones and detecting when data becomes irrelevant with respect to its environment.By addressing these overlooked aspects, we aim to improve data interpretation and management within connected environments.This thesis addresses four key challenges: (1) designing a deep and expressive data model that effectively captures the core components of connected environments, such as hosting zones, smart devices, and their corresponding sensor networks; (2) establishing a mechanism to discover and trace how data propagates between different zones; (3) proposing a large-scale approach to detect data obsolescence while considering the entire environment; and (4) handling the dynamic nature of the CE that could be illustrated by the mobility and failure of the devices, as well as modifications in zone configurations (e.g., combining multiple zones into one or dividing a single zone into several).To address these challenges, we first propose the HSSN+ data model, which underpins a resilient representation of the elements in connected environments.Consequently, in light of this model, we put forward two interconnected frameworks. The first one is the Zone Continuum Computation for Connected Environments (ZCCCE) framework that formalizes the zone continuum concept and enables to understand how data moves between zones. The second one is the Data Obsolescence Detection in Connected Environments (DODCE) framework which offers a structured approach to identify and compute the data obsolescence based on predefined quality indicators.These two frameworks provide opportunities for effective control and understanding in connected environments

L’objectif de cette thèse est de développer et de proposer de nouvellesméthodes de réduction de dimension pour la construction d’indicateurs composites dequalité de vie à l’échelle communale. La méthodologie statistique développée met l’accentsur la prise en compte de la multidimensionnalité du concept de qualité de vie, avecune attention particulière sur le traitement de la mixité des données (variables quantitativeset qualitatives) et l’introduction des conditions environnementales. Nous optonspour une approche par classification de variables et pour une méthode multi-tableaux(analyse factorielle multiple pour données mixtes). Ces deux méthodes permettent deconstruire des indicateurs composites que nous proposons comme mesure des conditionsde vie à l’échelle communale. Afin de faciliter l’interprétation des indicateurscomposites construits, une méthode de sélection de variables de type bootstrap estintroduite en analyse factorielle multiple. Enfin nous proposons la méthode hclustgeode classification d’observations qui intègre des contraintes de proximité géographiqueafin de mieux appréhender la spatialité des phénomènes mis en jeu
The purpose of this thesis is to develop and suggest new dimensionreduction methods to construct composite indicators on a municipal scale. The developedstatistical methodology highlights the consideration of the multi-dimensionalityof the quality of life concept, with a particular attention on the treatment of mixeddata (quantitative and qualitative variables) and the introduction of environmentalconditions. We opt for a variable clustering approach and for a multi-table method(multiple factorial analysis for mixed data). These two methods allow to build compositeindicators that we propose as a measure of living conditions at the municipalscale. In order to facilitate the interpretation of the created composite indicators, weintroduce a method of selections of variables based on a bootstrap approach. Finally,we suggest the clustering of observations method, named hclustgeo, which integratesgeographical proximity constraints in the clustering procedure, in order to apprehendthe spatiality specificities better

As the public and government officials become aware of the impact of public health on communities, it is important that relevant public health statistics be available for decision making. Existing web resources have limited visualization options, cannot visually compare a county to all others in the US, and cannot compare the counties in an arbitrary region to all others in the US. The College of Public Health Indicator Visualization Tool (CPHIVT) is a web application providing visualization and ranking for a county in the US in comparison to all counties for a specific health indicator. An iterative development methodology was used to complete major features and refine the features over time. Features divided into small tasks that could be completed within two-week cycles. After the first version of the web application was completed and presented to the client, client feedback on the application was used to refine specifications and was incorporated into planning for future iterations. Iterative development was adopted with a focus on improving and expanding existing features and making the application publicly available online. A suite of automated user interface tests is being developed to verify the application’s functions. Making a complete version of the application publicly available involves significant research and software configuration to deploy the web application in a secure and performant manner. The web application has two major components corresponding to its two major user groups. The first component allows authenticated users from the Department of Public Health to upload and manage sets of data for various health indicators. Tools are included to automatically process uploaded data points. This allows the information presented on the web site to be expanded and kept up to date over time with minimal effort. The second component is accessible to anyone and allows a user to choose to a state or county with text search or hierarchical navigation. The application then provides graphical charts showing that location’s standing for various health indicators compared to all other counties nationally. This is accomplished by applying percentile rankings to the counties and plotting the percentiles against the values for a selected indicator. A user can save a generated chart to a variety of export formats including PNG image or PDF document. The application is expected to serve as a tool for many community members. Staff and students at the College of Public Health will use this tool for presentations and research. County health departments will be able to use the tool when planning community programs. County government leaders can use this tool to determine areas of need in the community. Decision makers will have the ability to visualize their county or region as compared to the nation, not just to neighboring counties or within a state.

Mestrado em Economia Monetária e Financeira
Esta dissertação estuda a relação entre os Worldwide Governance Indicators e a Dívida Pública em 164 países para o período entre 2002 e 2015. Para tal, estimaram-se os modelos de fixed effects (FE) e generalized method of moments (GMM). Os resultados sugerem que a qualidade da governance está negativamente e estatisticamente relacionada com a dívida. Para os países de rendimento per capita mais baixo, foi encontrada evidência de que um melhor ambiente de governance está associado a níveis mais baixos de dívida pública.
This dissertation examines the relationship between Worldwide Governance Indicators and Government Debt in 164 countries for the period between 2002 and 2015. For this purpose, fixed effects (FE) and generalized method of moments (GMM) models are estimated. The results suggest that governance quality is negatively and statistically related with government debt. For Low Income countries was found evidence that better governance environment is associated with lower public debt levels.
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Tese (Doutoramento)
IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

A previsibilidade de indicadores de qualidade de vida pode contribuir na projeção de variáveis dependentes, auxiliar em tomadas de decisões para sustentar ou não políticas públicas e justificar o cenário vivido pelos países e o mundo. Objetivo: Prever o Índice de Desenvolvimento Humano (IDH) e a expectativa de vida (EV) nos países latino-americanos no período de 2015 a 2020, utilizando técnicas de Mineração de Dados. Metodologia: Foram percorridas as etapas do processo Descoberta de Conhecimento em Base Dados (DCBD). Durante a etapa de DCBD de Mineração de Dados, foi avaliado o desempenho de diferentes algoritmos com paradigma de aprendizado baseados em funções. A partir do algoritmo com melhor desempenho, foram desenvolvidos 748 modelos de previsão univariados e dois multivariados para previsão do IDH de 187 países do mundo e seus resultados, comparados com os últimos relatórios do United Nations Development Programme (UNDP), para definição do modelo mais eficiente. Os resultados desses testes de previsões ainda foram comparados com 44 modelos univariados Autoregressive Integrated Moving Average (ARIMA). A partir da definição do melhor algoritmo de Mineração de Dados e modelo, fez-se a previsão do IDH e da EV para os países da America Latina para o período de 2015 a 2020. Resultados: O algoritmo SMOReg e os modelos multivariados apresentaram melhor desempenho nos testes desenvolvidos durante o experimento. As médias de crescimento do IDH e EV previstas para os países latino-americanos tendem a aumentar no período analisado, respectivamente, 4,99±3,90 % e 2,47±0,09 anos. Conclusão: Experiências multivariadas possibilitam maior aprendizagem dos algoritmos, aumentando sua precisão. As técnicas de Mineração de Dados apresentaram melhor qualidade nas previsões em relação à técnica mais popular, ARIMA. As previsões sugerem média de crescimento do IDH e EV dos países latino-americanos maiores que a média mundial.
The predictability of quality of life indicators can contribute to the projection of dependent variables, help decision-making processes to support public policies and justify the scenario experienced by the countries and the world. Aim: This study aimed to predict the Human Development Index (HDI) and life expectancy (LE) in Latin American countries in the period of 2015–2020 using data mining techniques. Methodology: The study followed the steps of Knowledge Discovery in Database (KDD) processes. During the data mining KDD step, the performance of different algorithms with function-based learning paradigms was analyzed. From the algorithm with the best performance, 748 prediction models of univariate and two multivariate were developed to predict the HDI of 187 countries and their results were compared with the last reports from the United Nations Development Program (UNDP) in order to define the most efficient model. The results of these prediction tests were compared with 44 univariate Autoregressive Integrated Moving Average (ARIMA) models. From the definition of the best algorithm of data mining and model, the prediction of HDI and LE for Latin American countries from 2015 and 2020 was done. Results: The SMOReg algorithm and the multivariate models presented the best performance in the tests during the experiment. The average growth in HDI and LE predicted for Latin American countries tend to increase in the period analyzed, 4.99±3.90 % and 2.47±0.09 years, respectively. Conclusion: Multivariate experiences allow better learning of algorithms, increasing their prediction. Mining data techniques present better quality in the predictions compared to Autoregressive Integrated Moving Average (ARIMA), which is the most popular technique. The predictions suggest an average growth in HDI and LE in Latin American countries compared to the world average.

Over 1.7 million Virginians rely on private water systems to supply household water. The heaviest reliance on these systems occurs in rural areas, which are often underserved in terms of financial resources and access to environmental health education. As the Safe Drinking Water Act (SDWA) does not regulate private water systems, it is the sole responsibility of the homeowner to maintain and monitor these systems.
    Previous limited studies indicate that microbial contamination of drinking water from private wells and springs is far from uncommon, ranging from 10% to 68%, depending on type of organism and geological region. With the exception of one thirty-year old government study on rural water supplies, there have been no documented investigations of links between private system water contamination and household demographic characteristics, making the design of effective public health interventions, very difficult.
    The goal of the present study is to identify potential associations between concentrations of fecal indicator bacteria (e.g. coliforms, E. coli) in 831 samples collected at the point-of-use in homes with private water supply systems and homeowner-provided demographic data (e.g. homeowner age, household income, education, water quality perception). Household income and the education of the perceived head of household were determined to have an association with bacteria concentrations. However, when a model was developed to evaluate strong associations between total coliform presence and potential predictors, no demographic parameters were deemed significant enough to be included in the final model. Of the 831 samples tested, 349 (42%) of samples tested positive for total coliform and 55 (6.6%) tested positive for E. coli contamination. Chemical and microbial source tracking efforts using fluorometry and qPCR suggested possible E. coli contamination from human septage in 21 cases.  The findings of this research can ultimately aid in determining effective strategies for public health intervention and gain a better understanding of interactions between demographic data and private system water quality.

Master of Science

A qualidade dos efluentes líquidos do Pólo Industrial de Belford Roxo e das águas do Rio Sarapuí foram avaliadas por meio de ensaios de toxicidade aguda com Daphnia similis, Ceríodaphnia dúbia e Danio rerio e ensaios de toxicidade crônica com Ceriodaphnia dúbia e Selenastrum capricornutum. Em associação ao monitoramento ecotoxicológico, foram realizados os procedimentos de Avaliação e Identificação da Toxicidade - AIT (Toxicity Identification Evaluation - TIE) os quais possibilitaram a identificação dos agentes tóxicos para as duas matrizes. Para o efluente, foi identificado o íon cloreto como principal agente tóxico, complementado por efeitos aditivos de metais, amônia e sulfeto. As águas do Rio Sarapuí apresentam os nutrientes fósforo e nitrogênio como principais agentes tóxicos, com toxicidade aditiva de metais, amônia e sulfeto. Embora a Estimativa de Impactos Ambientais a partir dos dados de toxicidade do efluente sugira impactos mínimos por parte do lançamento de efluentes do Pólo no compartimento água superficial, a qualidade das águas desse rio já apresenta por si só um impedimento à manutenção da vida aquática. O constante lançamento de esgoto não tratado compromete a qualidade das águas do rio Sarapuí, culminando num quadro de completa degradação devido ao aumento da concentração de nutrientes e matéria orgânica, que acaba por inviabilizar o correto equilíbrio desse ecossistema e suas interações ecológicas naturais.
The quality of Belford Roxo Industrial Plant effluent and water from Sarapuí River were evaluated with Daphnia similis, Ceriodaphnia dubia and Danio rerio acute and chronic toxicity tests. In association with the ecotoxicological monitoring, the Toxicity Identification Evaluation procedure were performed and the identification of the toxic compounds was possible. The Chloride ion was identified as the major toxic compound in the effluent with additional effects of Metals, Ammonium and Sulfide. For the Sarapuí River, the compounds of Phosphorus and Nitrogen were identified as the major toxic compounds with addictive effects of Metals, Ammonium and Sulfide. Although the environmental impact estimation based on the effluent toxicity suggests a minor impact on the water quality of Sarapuí River, this was already sufficiently contaminated to make impracticable the establishment of an aquatic community. The constant discharge of untreated sludge promotes the eutrophication of this water body and makes impossible the equilibrium of this ecosystem.

Quality of Experience (QoE) is the degree of delight or annoyance of the user of an application or service [1]. To ensure a proper level of QoE for end users, networks and service providers have to continuously monitor their systems in terms of technical parameters, which can then be used to estimate QoE. Especially for video streaming services, which consume a large amount of traffic, network problems such as bandwidth fluctuations quickly develop into annoying artefacts visible to the users, which may lead to abandonment of services. Internet Service Providers (ISPs) are therefore continuously monitoring video network streams in order to provide the better QoE. In this regard to conduct the user behavioral studies, the ISPs spend a large amount of money and energy every time. To avoid this, we are using existing user behavioral studies and simulating the user behavior in an automated set-up and try to measure the impact of network conditions. In our current studies based on the user-behavioral model used [5], we can conclude that low upload speeds don’t affect on simulated user behavior unless they are in high download speed networks. Simulated users with the mid-range download and upload bandwidth tend to face more stalling and quality switches compared to both low and high-bandwidth users. Key quality indicators(KQIs) of video QoE also depends on the number of videos we measure in a single session. Reloading of player helps to reduce stalling for mid and high bandwidths. Reloading worsens the situation in low bandwidth scenarios.
Kvalitet av erfarenhet (QoE) definieras som: "Graden av fröjd eller förargelse av användaren av en applikation eller en service. Den resulterar från uppfyllelsen av hans eller hennes förväntningar med hänsyn till hjälpmedlet, och/eller njutning av applikationen eller servicen i ljuset av användarens personlighet och aktuella tillstånd" [1]. Att att se till en riktig nivå av QoE för slut användare, nätverk och tjänstefamiljeförsörjare måste övervakar fortlöpande deras system när det gäller tekniska parametrar, som kan därefter vara den van vid bedömningen QoE. Speciellt för videoen som strömmar service, som konsumerar ett stort belopp av trafik, framkallar nätverksproblem liksom bandbreddväxlingar snabbt in i förargliga artefacts som är synliga till användarena, som kan leda till övergivande av service. Internetleverantörer (ISPs) är därför fortlöpande videopp nätverksströmmar för övervakning för att att ge den bättre QoEen. I detta avseende att föra de beteendestudierna för användaren, spenderar ISPsna en stor mängd pengar och energi varje gång. Att undvika denna, använder simulerar vi beteendestudier för existerande användare och användareuppförandet i en automatiserat aktivering och försök för att mäta inverkan av nätverksvillkor. I våra aktuella studier som baseras på den använda användare-beteendemodellen [5], oss kan avsluta som laddar upp lågt hastigheter inte påverkar på simulerat användareuppförande, om inte de är i höga nedladdninghastighetsnätverk. Simulerade användare med mitt–området nedladdar och laddar upp bandbredd ansar för att vända mot mer avbrottsoch kvalitetsströmbrytare som jämförs till både låga och höga bandbreddanvändare. Nyckelkvalitetsindikatorer (KQIs) av video QoE beror också på numret av video som vi mäter i en enkel period. Tillbakaläggande av spelaren hjälper att förminska avbrott för mittoch höga bandbredder. Tillbakaläggande försämrar läget i scenarion för låg bandbredd.

INTRODUÇÃO: O câncer de pulmão é a terceira neoplasia maligna mais frequentemente diagnosticada em todo o mundo e a primeira em termos de mortalidade. O tratamento cirúrgico é a melhor abordagem nos estágios iniciais, contudo, está associado a morbimortalidade considerável. Para que o impacto do tratamento cirúrgico na diminuição global da mortalidade pelo câncer de pulmão no estado de São Paulo seja maior, precisamos conhecer os indicadores de qualidade das instituições envolvidas no tratamento desta neoplasia, através da criação de uma base de dados abrangente, confiável e transparente. Este estudo envolveu a implementação do Registro Paulista de Tratamento Cirúrgico do Câncer de Pulmão (RPCP). O desfecho principal foi a análise da qualidade dos dados capturados através de um sistema de auditoria direta e indireta, com o intuito de identificar as variáveis com menor padrão de qualidade. MÉTODOS: Estudo prospectivo, multicêntrico, com participação de 10 instituições no estado de São Paulo. A auditoria dos dados foi realizada de forma direta por revisão dos prontuários, para análise da taxa de discordância, Coeficiente Kappa e Intraclass correlation e de forma indireta para análise dos índices de completude, acurácia e consistência. RESULTADOS: Dos 536 casos disponíveis, 511 foram incluídos para a auditoria indireta. O índice total de completude por questionário variou de 0,82 a 1, sendo que as seguintes variáveis obtiveram valor individual abaixo da meta estabelecida de 0,8: ECOG, MRC, hematócrito, potássio, uréia, creatinina, DHL, albumina, cálcio e FA, tempo de cirurgia e data da recidiva. O índice total de acurácia e consistência foi 0,99 e 0,96, respectivamente. Para auditoria direta foram randomizados 100 casos entre os 511 iniciais, sendo 4 excluídos, restando 96 para análise. As variáveis com maiores taxas de discordância ( > 20%), estavam no questionário de avaliação pré-operatória (ECOG, MRC, carga tabágica, DPOC, PFP, peso, altura, IMC e exames laboratoriais). Variáveis relacionadas ao estadiamento (tamanho da neoplasia, invasão de estruturas adjacentes, status linfonodal não invasivo) e dados cirúrgicos (tempo de cirurgia) também apresentaram taxas > 20%. CONCLUSÕES: A auditoria indireta dos dados mostrou índices de completude, acurácia e consistência aceitáveis para o padrão estabelecido e comparáveis a bancos de dados internacionais. Por outro lado, a auditoria direta, revelou algumas variáveis com altos índices de discordância, dados que serão analisados futuramente para aprimoramento do RPCP e que poderão contribuir para o desenvolvimento de outras bases de dados semelhantes
BACKGROUND: Lung cancer is the third malignant neoplasm most frequently diagnosed worldwide and the first in terms of mortality. Surgical treatment is the best approach in the initial stages; however, it\'s associated with considerable morbidity and mortality. In order to improve the surgical treatment global impact on lung cancer mortality in the state of Sao Paulo, we need to know the quality indicators of the institutions involved in the treatment of this neoplasm through the creation of a extensive, reliable and transparent database. The study involved the implementation of the Paulista Lung Cancer Registry (PLCR). The main outcome was the quality analysis of the data captured through a direct and indirect audit system, in order to identify the variables with the lowest quality standard. METHODS: A prospective, multicenter study with the participation of 10 institutions in the state of São Paulo. The data audit was performed directly, through the revision of medical registries, with the intention to analyze the discordance rate; and indirectly, with the intention to analyze the completeness, accuracy and consistency indexes. RESULTS: Of the 536 cases available, 511 were included for the indirect audit. The total completeness index per questionnaire ranged from 0.82 to 1, and the following variables had a in individual value bellow the established target of 0,8: ECOG, MRC, hematocrit, potassium, urea, creatinine, LDH, albumin, calcium, AF, surgical time, date of recurrence. The total accuracy and consistency index was 0.99 and 0.96, respectively. For direct audit, 100 cases were randomized among the initial 511, of which 4 were excluded, remaining 96 for analysis. The variables with the highest discordance rates ( > 20%) were in the preoperative evaluation questionnaire (ECOG, MRC, smoking rate, COPD, PFT, weight, high, BMI and lab tests). Variables related to staging (size of neoplasm, invasion of adjacent structures, noninvasive lymph node status) and surgical data (time of surgery) also presented rates > 20%. CONCLUSIONS: Regarding the established standards, the Indirect audit showed acceptable completeness, accuracy and consistency indices, comparable to international databases. On the other hand, the direct audit revealed some variables with high discordance indices, data that will be analyzed in the future for the improvement of the PLCR and that may contribute to the development of other similar databases

This thesis is focused on Master Data Management (MDM), Customer Data Integration (CDI) area and its main domains. It is also a reference to a various theoretical directions that can be found in this area of expertise. It summarizes main aspects, domains and presents different perspectives to referenced principles. It is an exhaustive background research in area of Master Data Management with emphasis on practical use with references on authors experience and opinions. Secondary focus is directed to the field of business value of Master Data Management initiatives. Thesis presents a thought concept for initiations of MDM project. The reason for such a concept is based on current trend, where companies are struggling to determine actual benefits of MDM initiatives. There is overall accord on the subject of necessity of such initiatives, but the struggle is in area of determining actual measureable impact on company's revenue or profit. Since the MDM initiative is more of an enabling function, rather than direct revenue function, the benefit is less straight forward and therefore harder to determine. This work describes different layers and mapping of business requirements through layers for transparent linkage between enabling functions to revenue generating ones. The emphasis is given to financial benefit calculation, measurability and responsibility of business and IT departments. To underline certain conclusions thesis also presents real world interviews with possible stakeholders of MDM initiative within the company. These representatives were selected as key drivers for such an initiative. Interviews map their recognition of MDM and related terms. It also focus on their reasons and expectations from MDM. The representatives were also selected to equally represent business and IT departments, which presents interesting clash of views and expectations.

Les données acquises lors des surveillances environnementales peuvent être sujettes à différents types d'anomalies (i.e., données incomplètes, inconsistantes, inexactes ou aberrantes). Ces anomalies qui entachent la qualité des données environnementales peuvent avoir de graves conséquences lors de l'interprétation des résultats et l’évaluation des écosystèmes. Le choix des méthodes de prétraitement des données est alors crucial pour la validité des résultats d'analyses statistiques et il est assez mal défini. Pour étudier cette question, la thèse s'est concentrée sur l’acquisition des données et sur les protocoles de prétraitement des données afin de garantir la validité des résultats d'analyse des données, notamment dans le but de recommander la séquence de tâches de prétraitement la plus adaptée. Nous proposons de maîtriser l'intégralité du processus de production des données, de leur collecte sur le terrain et à leur analyse, et dans le cas de l'évaluation de la qualité de l'eau, il s’agit des étapes d'analyse chimique et hydrobiologique des échantillons produisant ainsi les données qui ont été par la suite analysées par un ensemble de méthodes statistiques et de fouille de données. En particulier, les contributions multidisciplinaires de la thèse sont : (1) en chimie de l'eau: une procédure méthodologique permettant de déterminer les quantités de pesticides organochlorés dans des échantillons d'eau collectés sur le terrain en utilisant les techniques SPE–GC-ECD (Solid Phase Extraction - Gas Chromatography - Electron Capture Detector) ; (2) en hydrobiologie : une procédure méthodologique pour évaluer la qualité de l’eau dans quatre rivières Mexicaines en utilisant des indicateurs biologiques basés sur des macroinvertébrés ; (3) en science des données : une méthode pour évaluer et guider le choix des procédures de prétraitement des données produites lors des deux précédentes étapes ainsi que leur analyse ; et enfin, (4) le développement d’un environnement analytique intégré sous la forme d’une application développée en R pour l’analyse statistique des données environnementales en général et l’analyse de la qualité de l’eau en particulier. Enfin, nous avons appliqué nos propositions sur le cas spécifique de l’évaluation de la qualité de l’eau des rivières Mexicaines Tula, Tamazula, Humaya et Culiacan dans le cadre de cette thèse qui a été menée en partie au Mexique et en France
Data obtained from environmental surveys may be prone to have different anomalies (i.e., incomplete, inconsistent, inaccurate or outlying data). These anomalies affect the quality of environmental data and can have considerable consequences when assessing environmental ecosystems. Selection of data preprocessing procedures is crucial to validate the results of statistical analysis however, such selection is badly defined. To address this question, the thesis focused on data acquisition and data preprocessing protocols in order to ensure the validity of the results of data analysis mainly, to recommend the most suitable sequence of preprocessing tasks. We propose to control every step in the data production process, from their collection on the field to their analysis. In the case of water quality assessment, it comes to the steps of chemical and hydrobiological analysis of samples producing data that were subsequently analyzed by a set of statistical and data mining methods. The multidisciplinary contributions of the thesis are: (1) in environmental chemistry: a methodological procedure to determine the content of organochlorine pesticides in water samples using the SPE-GC-ECD (Solid Phase Extraction – Gas Chromatography – Electron Capture Detector) techniques; (2) in hydrobiology: a methodological procedure to assess the quality of water on four Mexican rivers using macroinvertebrates-based biological indices; (3) in data sciences: a method to assess and guide on the selection of preprocessing procedures for data produced from the two previous steps as well as their analysis; and (4) the development of a fully integrated analytics environment in R for statistical analysis of environmental data in general, and for water quality data analytics, in particular. Finally, within the context of this thesis that was developed between Mexico and France, we have applied our methodological approaches on the specific case of water quality assessment of the Mexican rivers Tula, Tamazula, Humaya and Culiacan

Le Big Data, phénomène sociotechnique porteur de mythes, se traduit dans les entreprises par la mise en place de premiers projets, plus particulièrement des projets de Data Science. Cependant, ils ne semblent pas générer la valeur espérée. La recherche-action menée au cours de 3 ans sur le terrain, à travers une étude qualitative approfondie de cas multiples, pointe des facteurs clés qui limitent cette génération de valeur, et notamment des modèles de processus projet trop autocentrés. Le résultat est (1) un modèle ajusté de dispositif projet data (Brizo_DS), ouvert et orienté sur les usages, dont la capitalisation de connaissances, destiné à réduire les incertitudes propres à ces projets exploratoires, et transposable à l’échelle d’une gestion de portefeuille de projets data en entreprise. Il est complété par (2) un outil de documentation de la qualité des données traitées, le Databook, et par (3) un dispositif de Médiation Homme-Données, qui garantissent l’alignement des acteurs vers un résultat optimal
Big Data, a sociotechnical phenomenon carrying myths, is reflected in companies by the implementation of first projects, especially Data Science projects. However, they do not seem to generate the expected value. The action-research carried out over the course of 3 years in the field, through an in-depth qualitative study of multiple cases, points to key factors that limit this generation of value, including overly self-contained project process models. The result is (1) an open data project model (Brizo_DS), orientated on the usage, including knowledge capitalization, intended to reduce the uncertainties inherent in these exploratory projects, and transferable to the scale of portfolio management of corporate data projects. It is completed with (2) a tool for documenting the quality of the processed data, the Databook, and (3) a Human-Data Mediation device, which guarantee the alignment of the actors towards an optimal result

Cette thèse propose une analyse économique de la qualité et de l'efficience des universités et des systèmes universitaires au niveau international, à travers l'utilisation des indicateurs intrants et extrants et la méthode d'enveloppement des données (Data Envelopment Analysis DEA). La thèse est composée de quatre chapitres. Le premier chapitre intitulé "les classements des universités : une mise en perspective critique" présente et évalue la pertinence des indicateurs intrants/extrants les plus utilisés par les classements des universités. Il est l'occasion d'exposer un certain nombre de critiques présentes dans la littérature et de focaliser sur un problème méthodologique répandu dans les classements. Il s'agit de l'utilisation des inputs (intrants) comme des mesures de la qualité des universités. Une telle pratique confond moyen et résultat et fait l'impasse sur les concepts de base des modèles de comptabilité en termes de fonction de production et d'efficience. Le deuxième chapitre intitulé "caractéristiques et classements des universités : autour de quelques facteurs capables d'expliquer les différences de performance entre les universités", compare les résultats de deux classements: QS-Times et Shanghai et propose une liste de facteurs capables d'expliquer pourquoi il existe de telles différences de qualité, selon ces classements, entre les universités. [...] Le troisième chapitre intitulé "la performance et l'efficience des universités et leurs déterminants: une évaluation à partir des classements mondiaux des universités et de la méthode DEA" évalue sur la base d'une méthode DEA l'efficience de 214 universités issues de 13 pays différentes afin de savoir si les universités les mieux classées parmi les classements traditionnels sont aussi les universités qui utilisent le mieux leurs ressources. [...] Enfin, le quatrième chapitre, titré "l'efficience des systèmes universitaires de 35 pays et leurs déterminants: une évaluation par la méthode DEA et le calcul des indices de Malmquist (2006-2012)" évalue l'efficience et les performances des systèmes universitaires de 35 pays. Il propose de nouveaux scores d'efficience globale qui viennent compléter les deux premières études sur ce sujet d’Agasisti (2011) et St. Aubyn et al (2009). Les nouveautés par rapport à l'article d'Agasisti (2011) sont au nombre de cinq: l'échantillon est plus important (35 pays au lieu de 18), la période d'observation est actualisée, l'évolution de l'efficience entre deux périodes est calculée, le nombre des intrants et des extrants incorporé dans chaque modèle est plus élevé et un modèle spécifique d'évaluation de l'efficience de la recherche est proposé. Il conforte la thèse selon laquelle les systèmes universitaires de la Suisse et du Royaume-Uni sont les plus efficients. Il montre aussi sur la base d'un calcul des indices de Malmquist qu'entre 2006 et 2012 l'efficience en matière d'enseignement des 35 systèmes universitaires évalués a eu tendance à diminuer alors que l'efficience de la recherche et de l'attractivité- réputation de ces mêmes systèmes a plutôt eu tendance à augmenter. Ce qui permet de mieux évaluer l'effet des réformes inspirées par l'usage du classement de Shanghai sur les systèmes universitaires. Ces réformes conduisent les enseignants-chercheurs à délaisser l'enseignement pour la recherche
This thesis aims to economically analyze the quality and efficiency of universities and university systems at an international level of comparison, by using input/output indicators and the Data Envelopment Analysis (DEA) method. The thesis is composed of four chapters. The first chapter entitled "university rankings: a critical perspective" presents and evaluates the relevance of inputs/outputs indicators used by most university rankings. It is the opportunity to present a number of criticisms found in the literature and focus on a common methodological problem in the rankings. It is the use of inputs as measures of university quality. This practice confuses means and results and ignores the basic concepts of accounting models in terms of production functions and efficiency. The second chapter entitled "characteristics and rankings of universities : around some factors that can explain the differences in performance between universities", compares the results of two rankings: QS-Times and Shanghai and offers a list of factors that may explain why there are such differences in quality, according to these rankings between universities. [...] The third chapter entitled "performance and efficiency of universities and their determinants: an evaluation using world university rankings and DEA methodology" evaluates on the basis of a DEA methodology the efficiency of 214 universities from 13 different countries, in order to find if the top ranked universities among traditional rankings are also universities that best utilize their financial and human resources. [...] The fourth chapter titled "efficiency of university systems in 35 countries and its determinants: an assessment by DEA methodology and the calculation of Malmquist indices (2006-2012)" assesses the efficiency and performance of university systems of 35 countries. It offers new scores for overall efficiency that complement the first two studies on this topic in the literature by Agasisti (2011) and St.Aubyn et al (2009). Compared to the article of Agasisti (2011), we identify five new developments in our study : the sample is higher (35 countries instead of 18), the observation period is updated, the evolution of efficiency between two periods is calculated, the number of inputs and outputs incorporated into each model is higher and a specific model for evaluating the efficiency of research is proposed. Our study confirms the thesis that the university systems of Switzerland and the United Kingdom are the most efficient. It also shows based on the calculations of Malmquist indices between 2006 and 2012 that teaching efficiency of 35 reviewed university systems has a tendency of declining while the research efficiency and that of attractivity-reputation is rather increasing. This allows a better assessment of the impact of reforms inspired by the Shanghai ranking on university systems. These reforms led the academic staff of universities to abandon their focus on teaching in favor of research activities

Les données issues de la surveillance des rivières sont volumineuses, avec des relations complexes. Des méthodes de fouille de données non supervisées peuvent s’y appliquer et donner des résultats pertinents pour leur gestion, sous réserve d’une collaboration étroite entre hydroécologues et informaticiens. L’extraction de motifs partiellement ordonnés à partir de séquences temporelles de pressions physico-chimiques précédant un état biologique a été réalisée. Ces motifs temporels permettent d’identifier une partie des pressions en cause dans un état écologique dégradé ou non, de préciser l’importance de la durée des séquences avant l’évaluation de l’état biologique, d’identifier les altérations caractéristiques à l’échelle d’une hydro-écorégion. A terme nous envisageons d’élargir ces motifs aux pressions hydromorphologiques
Data from the assessment of river are big data, with complex relationships. Unsupervised data mining methods can be applied on them and give relevant results for their management, if a close collaboration exists between hydroecologists and computer scientists. The extraction of partially ordered patterns from temporal sequences of physicochemical pressures preceding a biological state has been achieved. These temporal patterns allow to identify a part of the pressures involved or not in a degraded ecological status, to specify the importance of the sequences time-length before a biological assessment, to identify the characteristic pressure categories at a regional scale. To go further, we plan to extend these patterns to hydromorphological pressures

Despite significant efforts have been directed toward reducing waste generation and encouraging alternative waste management strategies, landfills still remain the main option for Municipal Solid Waste (MSW) disposal in many countries. Hence, landfills and related impacts on the surroundings are still current issues throughout the world. Actually, the major concerns are related to the potential emissions of leachate and landfill gas into the environment, that pose a threat to public health, surface and groundwater pollution, soil contamination and global warming effects. To ensure environmental protection and enhance landfill sustainability, modern sanitary landfills are equipped with several engineered systems with different functions. For instance, the installation of containment systems, such as bottom liner and multi-layers capping systems, is aimed at reducing leachate seepage and water infiltration into the landfill body as well as gas migration, while eventually mitigating methane emissions through the placement of active oxidation layers (biocovers). Leachate collection and removal systems are designed to minimize water head forming on the bottom section of the landfill and consequent seepages through the liner system. Finally, gas extraction and utilization systems, allow to recover energy from landfill gas while reducing explosion and fire risks associated with methane accumulation, even though much depends on gas collection efficiency achieved in the field (range: 60-90% Spokas et al., 2006; Huitric and Kong, 2006). Hence, impacts on the surrounding environment caused by the polluting substances released from the deposited waste through liquid and gas emissions can be potentially mitigated by a proper design of technical barriers and collection/extraction systems at the landfill site. Nevertheless, the long-term performance of containment systems to limit the landfill emissions is highly uncertain and is strongly dependent on site-specific conditions such as climate, vegetative covers, containment systems, leachate quality and applied stress. Furthermore, the design and operation of leachate collection and treatment systems, of landfill gas extraction and utilization projects, as well as the assessment of appropriate methane reduction strategies (biocovers), require reliable emission forecasts for the assessment of system feasibility and to ensure environmental compliance. To this end, landfill simulation models can represent an useful supporting tool for a better design of leachate/gas collection and treatment systems and can provide valuable information for the evaluation of best options for containment systems depending on their performances under the site-specific conditions. The capability in predicting future emissions levels at a landfill site can also be improved by combining simulation models with field observations at full-scale landfills and/or with experimental studies resembling landfill conditions. Indeed, this kind of data may allow to identify the main parameters and processes governing leachate and gas generation and can provide useful information for model refinement. In view of such need, the present research study was initially addressed to develop a new landfill screening model that, based on simplified mathematical and empirical equations, provides quantitative estimation of leachate and gas production over time, taking into account for site-specific conditions, waste properties and main landfill characteristics and processes. In order to evaluate the applicability of the developed model and the accuracy of emissions forecast, several simulations on four full-scale landfills, currently in operative management stage, were carried out. The results of these case studies showed a good correspondence of leachate estimations with monthly trend observed in the field and revealed that the reliability of model predictions is strongly influenced by the quality of input data. In particular, the initial waste moisture content and the waste compression index, which are usually data not available from a standard characterisation, were identified as the key unknown parameters affecting leachate production. Furthermore, the applicability of the model to closed landfills was evaluated by simulating different alternative capping systems and by comparing the results with those returned by the Hydrological Evaluation of Landfill Performance (HELP), which is the most worldwide used model for comparative analysis of composite liner systems. Despite the simplified approach of the developed model, simulated values of infiltration and leakage rates through the analysed cover systems were in line with those of HELP. However, it should be highlighted that the developed model provides an assessment of leachate and biogas production only from a quantitative point of view. The leachate and biogas composition was indeed not included in the forecast model, as strongly linked to the type of waste that makes the prediction in a screening phase poorly representative of what could be expected in the field. Hence, for a qualitative analysis of leachate and gas emissions over time, a laboratory methodology including different type of lab-scale tests was applied to a particular waste material. Specifically, the research was focused on mechanically biologically treated (MBT) wastes which, after the introduction of the European Landfill Directive 1999/31/EC (European Commission, 1999) that imposes member states to dispose of in landfills only wastes that have been preliminary subjected to treatment, are becoming the main flow waste landfilled in new Italian facilities. However, due to the relatively recent introduction of the MBT plants within the waste management system, very few data on leachate and gas emissions from MBT waste in landfills are available and, hence, the current knowledge mainly results from laboratory studies. Nevertheless, the assessment of the leaching characteristics of MBT materials and the evaluation of how the environmental conditions may affect the heavy metals mobility are still poorly investigated in literature. To gain deeper insight on the fundamental mechanisms governing the constituents release from MBT wastes, several leaching experiments were performed on MBT samples collected from an Italian MBT plant and the experimental results were modelled to obtain information on the long-term leachate emissions. Namely, a combination of experimental leaching tests were performed on fully-characterized MBT waste samples and the effect of different parameters, mainly pH and liquid to solid ratio (L/S,) on the compounds release was investigated by combining pH static-batch test, pH dependent tests and dynamic up-flow column percolation experiments. The obtained results showed that, even though MBT wastes were characterized by relatively high heavy metals content, only a limited amount was actually soluble and thus bioavailable. Furthermore, the information provided by the different tests highlighted the existence of a strong linear correlation between the release pattern of dissolved organic carbon (DOC) and several metals (Co, Cr, Cu, Ni, V, Zn), suggesting that complexation to DOC is the leaching controlling mechanism of these elements. Thus, combining the results of batch and up-flow column percolation tests, partition coefficients between DOC and metals concentration were derived. These data, coupled with a simplified screening model for DOC release, allowed to get a very good prediction of metal release during the experiments and may provide useful indications for the evaluation of long-term emissions from this type of waste in a landfill disposal scenario. In order to complete the study on the MBT waste environmental behaviour, gas emissions from MBT waste were examined by performing different anaerobic tests. The main purpose of this study was to evaluate the potential gas generation capacity of wastes and to assess possible implications on gas generation resulting from the different environmental conditions expected in the field. To this end, anaerobic batch tests were performed at a wide range of water contents (26-43 %w/w up to 75 %w/w on wet weight) and temperatures (from 20-25 °C up to 55 °C) in order to simulate different landfill management options (dry tomb or bioreactor landfills). In nearly all test conditions, a quite long lag-phase was observed (several months) due to the inhibition effects resulting from high concentrations of volatile fatty acids (VFAs) and ammonia that highlighted a poor stability degree of the analysed material. Furthermore, experimental results showed that the initial waste water content is the key factor limiting the anaerobic biological process. Indeed, when the waste moisture was lower than 32 %w/w the methanogenic microbial activity was completely inhibited. Overall, the obtained results indicated that the operative conditions drastically affect the gas generation from MBT waste, in terms of both gas yield and generation rate. This suggests that particular caution should be paid when using the results of lab-scale tests for the evaluation of long-term behaviour expected in the field, where the boundary conditions change continuously and vary significantly depending on the climate, the landfill operative management strategies in place (e.g. leachate recirculation, waste disposal methods), the hydraulic characteristics of buried waste, the presence and type of temporary and final cover systems.

This project assessed whether the Global Reporting Initiative (GRI) focuses on providing true environmental indicator reporting within its framework. The analysis focused on environmental indicator data quality through observation of the data collection process for a sustainability report. Participants showed satisfaction with the requested data in regards to future comparability and transparency, concerns with data reliability and stakeholder concerns and significant concerns with data gathering systems. A review of 2007 reporting identified a low level of complete environmental indicator reporting and a low level of independent assurance. The study recommends significant enhancements at the GRI and governmental level. At the GRI level, the study recommends mandatory assurance of reports to receive GRI designation. At the governmental level, the study recommends mandatory corporate reporting for larger organizations to limit data quality issues and raise the overall sustainability performance of those organizations having the largest potential impact on sustainability.

碩士
國立雲林科技大學
資訊管理系碩士班
91
Abstract In order to improve the medical quality, the medical society of Taiwan successively adopt TQIP and THIS these indicator systems. However, they both meet a problem that data of indicators are hard to apply in management area. Therefore, they use data warehouse technology to integrate and analyze the data of indicators to improve the above problem. In a hospital, they usually waste a lot of strength, resource and time to design, construct and maintain their data warehouse. As a result, some researches propose the concepts and methods of using metadata to construct data warehouse for the purpose of reducing the cost of construct the data warehouse. This research focuses on raising the efficiency and increasing successful opportunity of a hospital when they construct a data warehouse of their indicator project. Therefore, we propose a framework for designing and management data warehouse based on metadata. We establish metadata by XML structure due to its properties and features of strictness and flexibility. The users could comprehend the structure of their data warehouse and manage it simultaneously while they design a data warehouse by the way of recording metadata information of indicators and the structure of the data warehouse. So, the hospital can design and manage the metadata and exchange their experience and thoughts with each other by the way of importing and exporting metadata to the XML documents. Finally, this research implements a prototype system based on the proposed framework to explain the framework’s feasibility, and then proposes our perspectives and questions in the light of a result of examine and evaluate the prototype system and confer the possible future research aspects.

碩士
國立台北護理學院
長期照護研究所
91
The purposes of the study is aimed to explore the quality of nursing home care, and to distinguish the characteristics of nursing homes belongings and resident’s health status effect on the quality of care. We wish the findings will help us to establish countrilized quality indicators of care, draw up Taiwan long-term care policies, and accreditation criteria of nursing home, so far as to threshold of quality indicators. This study used secondary data from the project “Development of Long-term Care Case-Mix and Planning for Financing Long-term Care” (Granted by National Health Academic Yuan, Granted Code: 90SHU025),1999-2001, conducted by Dr. Herng-Chia Chiu, institute of Public Health, Kaohsiung Medical University, from 1999 to 2001, and contained 2,139 residends lived in 52 registered nursing home, located in 8 counties in Taiwan. It contains a few, frequently investigated health status informations of nursing home residents, such as prevalent rate of falling, pressure ulcers rate, prevalent rate of body restrain, urinary tract infection rate, body weight losing rate. The study was to use desciptive analysis, Chi-square test, and logistic analysis were use to examine the relationships among nursing care quality indicators, facilities’ attributes (size and ownship), residents’ attributes (demographic variables, functional status). The finding was listed below: (1) the prevalent rate of falling was 3.8%, threshold value (25-90 percentile) was 0-16.9%. There are six statistically significant predictive factors, such as hospital-based type nursing home, size of facility 50-100 bed, length of admission less one year, over 3 diagnosis diseases, moderate/high dependency of ADL, and taking psychiatric medicine. (2) the prevalent rate of body restrain was 19.4%, threshold value (25-90 percentile) was 8-30.3%. Over 100 beds, male, more dependant of ADL, and cognitive impairement were statistically significant predictive factors. (3) the prevalent rate of weight loss was 10.1%, threshold value (25-90 percentile) was 5.3-22.2%. Solo type nursing home, small size, unmarried or other non-married status, moderate/high dependency of ADL, and using supportive piping system were statistically significant predictive factors. (4) pressure ulcers rate was 11.6%, threshold value (25-90 percentile) was 0.4-32.6%. Male, 75~84 years old, length of admission over one year, over 3 diagnosis diseases, moderate/high dependency of ADL, and remained urine catheter were statistically significant predictive factors. (5) the prevalent rate of urinary tract infection was 7.3%, threshold value (25-90 percentile) was 0-17%. There were 5 statistically significant predictive factors included widow or separation, over 3 diagnosis diseases, using supportive piping system, urine catheter, and taking psychiatric medicine. All threshold of quality indicators were higher than MDS’s suggestion upper limit. In additions, bedsore rate was positive correlated with body weight loss, UTI in all facilities. In view of the above, we suggest : (1) in health care policy decision making side, the government should reference MDS, conclude and make nursing home use standardized resident assessment tool as soon as possible as well as constructs national wide long-term care dataset, binding quality management system. Besides, examines the resident’s body restrain and medication standard, principles, protocols, and guidelines, setups accreditation mechanisam, and integrates long-term care human resources. (2) in practical management side, case managers should efficiently allocate and modify resources according with resident’s health and functional status, and continuing monitor, and follow-up each quality indicator of care. In additions, according with predictive factors of quality care, care providers should draft out effective protection care strategies and promote quality improvement projects following the care provider team’s anticipated value of quality inidicators. Finally, we suggest the longitudinal data should be collected for exploring the trend of care quality, focus on structure and outcome based indicators and relatively impacting on quality of care refactors, and how to distinguish with the high or low risk groups and referencing threshold in future researches.

The high prevalence of Adverse Events (AEs) in Australian hospitals and their effects on patient outcomes is a major concern among healthcare service organizations and authorities. The contributing factors which exacerbate this situation, such as the effect of intensified hospital workload on AEs have not been adequately examined in the previous literature. The few studies that have concentrated on the association of hospital workload with AEs have methodological drawbacks due to insufficient numbers of Hospital Workload Indicators (HWIs) employed and the limitation of dimensions that include different types of HWIs. This thesis aims to examine the association of HWIs with AEs using indicators drawn from a hospital episode dataset.

Health practitioners collect health data on a daily basis at health facility levels in order to monitor and evaluate the performance of priority national health programmes (District Health Plan 2012:6). Routine data quality for health programmes monitoring need a collective intervention to ensure clear understanding for what data to be collected at primary health care setting. The aim of the study is to explore the understanding of routine health data, determine the use of routine data and feedback mechanism at primary health care clinic setting. Quantitative descriptive research design was used to answer the research question on this research study. Structured data collection questionnaire was used for the study to accomplish the research purpose and reach the study objectives. A total of 400 participants was sampled, and 247 responded. One of the findings was that the understanding of routine health data by Health Practitioners was at 82.6% (% = f/n*100, f= 3242 and n= 3926).
Health Studies

博士
東海大學
環境科學與工程學系
107
Due to the rapid development of industry and economy, natural environment was damaged and contaminated and it is not easy for local environmental agencies to find a corresponding environmental strategy. Nowadays, the big data era is already here, how to utilize big data analysis to realize environmental issue as well as establishing strategy or system. To obtain the aim of environmental sustainability planning and management, this study establish sustainable system indicators, air quality monitoring and underground water analysis by big data analysis. Firstly, this study establish sustainable indicators system to explore the conflict among society, environment and economy in coastal area by DPSIR and Analytic Network Process and provide strategies coping with conflict. Secondly, exploring area highly occurring pollution emission in river barren utilize Internet of Things technology and pollution source tracking technology. Thirdly, exploring the relation between environmental parameters and pollutant level take advantage of principal component analysis, cluster analysis and redundancy analysis. The results show that river and habitat protection, pollution control, ecotourism and mitigation actions of community sustainability policies are critical indicators which is effective for sustainable indicators system. Plus, it is providing rapidly that location highly occurring pollution emission in river barren to local environmental agency which conduct management strategies utilize on time portable environmental monitoring device and cloud computing system calculating pollution source contribution. Most of the environment in coastal industrial areas has been salinized, and the ORP, groundwater level and conductivity of hydrogeochemical characteristics have a critical impact on the formation of local groundwater pollutants. All of all, The integrated application of big data analysis methods effectively realize the situation of the local environment and establish a corresponding environmental response strategy.

This thesis contributes to the ongoing global discourse in ICT4D on ICT and its effect on socio-economic development in both theory and practice. The thesis comprises five studies presented logically from chapters 5 to 9. The thesis employs Mixed Methods research methodology within the Critical Realist epistemological perspective in Information Systems Research. Studies 1-4 employ different quantitative research and analytical methods while study 5 employs a qualitative research and analytical method. Study 1 proposes and operationalizes a predictive analytics framework in Learning Analytics by using a case study of the Computer Science Department of the University of Jos, Nigeria. Multiple Linear Regression was used with the aid of the Statistical Package for Social Sciences (SPSS) analysis tool. Statistical Hypothesis testing was then used to validate the model with a 5% level of significance. Results show how predictive learning analytics can be successfully operationalized and used for predicting students’ academic performances. In Study 2 the relative efficiency of ICT infrastructure utilization with respect to the educational component of the Human Development Index (HDI) is investigated. A Novel conceptual model is proposed and the Data Envelopment Analysis (DEA) methodology is used to measure the relative efficiency of the components of ICT infrastructure (Inputs) and the components of education (Outputs). Ordinary Least Squares (OLS) Regression Analysis is used to determine the effect of ICT infrastructure on Educational Attainment/Adult Literacy Rates. Results show a strong positive effect of ICT infrastructure on educational attainment and adult literacy rates, a strong correlation between this infrastructure and literacy rates as well as provide a theoretical support for the argument of increasing ICT infrastructure to provide an increase in human development especially within the educational context. In Study 3 the relative efficiency and productivity of ICT Infrastructure Utilization in Education are examined. The research employs the Data Envelopment Analysis (DEA) and Malmquist Index (MI), well established non-parametric data analysis methodologies, applied to archival data on International countries divided into Arab States, Europe, Sub-Saharan Africa and World regions. Ordinary Least Squares (OLS) Regression analysis is applied to determine the effect of ICT infrastructure on Adult Literacy Rates. Findings show a relatively efficient utilization and steady increase in productivity for the regions but with only Europe and the Arab States currently operating in a state of positive growth in productivity. A strong positive effect of ICT infrastructure on Adult Literacy Rates is also observed. Study 4 investigates the efficiency and productivity of ICT utilization in public value creation with respect to Adult Literacy Rates. The research employs Data Envelopment Analysis (DEA) and Malmquist Index (MI), well established non-parametric data analysis methodologies, applied to archival data on International countries divided into Arab States, Europe, Sub-Saharan Africa and World regions. Findings show a relatively efficient utilization of ICT in public value creation but an average decline in productivity levels. Finally, in Study 5 a Critical Discourse Analysis (CDA) on the UNDP Human Development Research Reports from 2010-2016 is carried out to determine whether or not any public value is created or derived from the policy directions being put forward and their subsequent implementations. The CDA is operationalized by Habermas’ Theory of Communicative Action (TCA). Findings show that Public Value is indeed being created and at the core of the policy directions being called for in these reports.
School of Computing
Ph.D. (Information Systems)

Indiana University-Purdue University Indianapolis (IUPUI)
A move toward care of residents in nursing homes where they are respected and heard is finally emerging. Common strategies used in nursing homes to improve quality of care for residents are integration of person-centered care and assessing care using satisfaction surveys. Although approaches of integrating person-centered care and satisfaction surveys have been valuable in improving nursing home quality, strategies of care that include things that matter from residents’ perspectives while living in nursing homes need investigation. The purpose of this qualitative descriptive study was to describe things that residents age 65 and older state matter to them while living in the long-term care sections of nursing homes. A qualitative mode of inquiry using purposeful sampling led to a natural unfolding of data that revealed things that mattered to residents. Content analysis was used to reduce the data in a manner that kept the data close to the context yet moved the data toward new ideas about including things that mattered to residents in nursing care. The findings revealed residents’ positive and negative experiences and addressed the question: How can nurses manage residents’ positive and negative aspects of care in nursing homes? This study substantiated the importance of developing nursing care strategies derived from residents’ descriptions of care. Finding ways to promote nurses’ investment in attitudes about a person-centered care philosophy is essential for successful person-centered care implementation. Enhancing nurses’ knowledge, skills, and attitudes with an investment in person centeredness will be more likely to put nurses in a position to role-model care that is person-centered from residents’ perspectives.

Une meilleure utilisation des ressources infirmières représente une avenue prometteuse dans l’amélioration de la performance des services de première ligne. La mesure de la performance des services infirmiers constitue une composante centrale à l’amélioration de leur organisation et à la qualité des soins dispensés dans ce secteur d’activités. Le but de cette recherche était de mesurer et de valider des indicateurs de performance des services infirmiers en première ligne à partir d’un cas traceur en soins de plaies ainsi que d’évaluer la fiabilité des données clinico-administratives utilisées pour mesurer les indicateurs à partir des dossiers cliniques. Cette étude a adopté un devis longitudinal corrélationnel. Les données ont été collectées sur une période d’une année dans un service de soins courants d’un Centre local de services communautaires (CLSC) à partir de données clinico-administratives contenues dans le système informatique I-CLSC. L’épisode de soins constituait l’unité d’analyse. Huit indicateurs ont été mesurés, dont cinq indicateurs de processus : 1) le suivi infirmier; 2) la continuité relationnelle; 3) l’enseignement; 4) l’évaluation initiale; et 5) la rencontre avec une infirmière spécialisée en soins de plaies, et trois indicateurs de résultats : 1) la fréquence; 2) la durée; et 3) l’intensité du suivi. Les objectifs de mesure et de validation ont été réalisés à partir d’un échantillon de 482 épisodes de soins de plaies d’une durée supérieure à sept jours. L’étude de fiabilité a été réalisée à partir d’un sous-échantillon de 107 épisodes. Des analyses descriptives et corrélationnelles ont été réalisées. Les résultats de validation ont démontré des associations fortes statistiquement significatives entre les indicateurs de suivi infirmier et de continuité et les trois indicateurs de résultats. Les résultats de fiabilité ont démontré un haut taux de concordance entre les données contenues dans les dossiers cliniques et les données clinico-administratives pour six des huit indicateurs à l’étude. En conclusion, des indicateurs de processus valides et pertinents dans la pratique infirmière de première ligne peuvent être mesurés de façon régulière par les gestionnaires à partir de données clinico-administratives fiables et facilement accessibles.
Better use of nursing resources is a promising avenue for improving the performance of primary care services. Measuring the performance of nursing services is a central component in improving their organization and the quality of care delivered in this sector. The aim of this study was to measure and validate primary care nursing performance indicators from a tracer case in wound care and to assess the reliability of clinical-administrative data used to measure indicators from clinical records. This study adopted a correlational longitudinal design. Data were collected over a one-year period in a Local community services centre (CLSC) using clinical-administrative data contained in the I-CLSC electronic database. The episode of care was the unit of analysis. Eight indicators were measured, including five process indicators: 1) nursing follow-up; 2) relational continuity; 3) teaching; 4) initial assessment; and 5) consultation with a specialized nurse, and three outcome indicators: 1) frequency; 2) duration; and 3) intensity. Measurement and validation objectives were performed using a sample of 482 episodes of wound care lasting more than seven days. The reliability study was based on a sub-sample of 107 episodes. Descriptive and correlational analyzes were performed. Validation results demonstrated very strong associations between nursing follow-up and continuity indicators and the three outcome indicators. Reliability results demonstrated a high concordance between clinical records and clinical-administrative data for six of the eight indicators. In conclusion, valid and relevant process indicators in primary care nursing can be measured on a regular basis by managers using reliable and easily accessible clinical-administrative data.