Le pronostic industriel vise à étendre le cycle de vie d’un dispositif physique, tout en réduisant les couts d’exploitation et de maintenance. Pour cette raison, le pronostic est considéré comme un processus clé avec des capacités de prédiction. En effet, des estimations précises de la durée de vie avant défaillance d’un équipement, Remaining Useful Life (RUL), permettent de mieux définir un plan d’action visant à accroitre la sécurité, réduire les temps d’arrêt, assurer l’achèvement de la mission et l’efficacité de la production.Des études récentes montrent que les approches guidées par les données sont de plus en plus appliquées pour le pronostic de défaillance. Elles peuvent être considérées comme des modèles de type boite noire pour l’ étude du comportement du système directement `a partir des données de surveillance d’ état, pour définir l’ état actuel du système et prédire la progression future de défauts. Cependant, l’approximation du comportement des machines critiques est une tâche difficile qui peut entraîner des mauvais pronostic. Pour la compréhension de la modélisation du pronostic guidé par les données, on considère les points suivants. 1) Comment traiter les données brutes de surveillance pour obtenir des caractéristiques appropriées reflétant l’ évolution de la dégradation? 2) Comment distinguer les états de dégradation et définir des critères de défaillance (qui peuvent varier d’un cas `a un autre)? 3) Comment être sûr que les modèles définis seront assez robustes pour montrer une performance stable avec des entrées incertaines s’ écartant des expériences acquises, et seront suffisamment fiables pour intégrer des données inconnues (c’est `a dire les conditions de fonctionnement, les variations de l’ingénierie, etc.)? 4) Comment réaliser facilement une intégration sous des contraintes et des exigence industrielles? Ces questions sont des problèmes abordés dans cette thèse. Elles ont conduit à développer une nouvelle approche allant au-delà des limites des méthodes classiques de pronostic guidé par les données. / Prognostics and Health Management (PHM) aims at extending the life cycle of engineerin gassets, while reducing exploitation and maintenance costs. For this reason,prognostics is considered as a key process with future capabilities. Indeed, accurateestimates of the Remaining Useful Life (RUL) of an equipment enable defining furtherplan of actions to increase safety, minimize downtime, ensure mission completion andefficient production.Recent advances show that data-driven approaches (mainly based on machine learningmethods) are increasingly applied for fault prognostics. They can be seen as black-boxmodels that learn system behavior directly from Condition Monitoring (CM) data, usethat knowledge to infer its current state and predict future progression of failure. However,approximating the behavior of critical machinery is a challenging task that canresult in poor prognostics. As for understanding, some issues of data-driven prognosticsmodeling are highlighted as follows. 1) How to effectively process raw monitoringdata to obtain suitable features that clearly reflect evolution of degradation? 2) Howto discriminate degradation states and define failure criteria (that can vary from caseto case)? 3) How to be sure that learned-models will be robust enough to show steadyperformance over uncertain inputs that deviate from learned experiences, and to bereliable enough to encounter unknown data (i.e., operating conditions, engineering variations,etc.)? 4) How to achieve ease of application under industrial constraints andrequirements? Such issues constitute the problems addressed in this thesis and have ledto develop a novel approach beyond conventional methods of data-driven prognostics.

Abstract The purpose of this study is to investigate how big data solutions in the Swedish healthcare system can generate a revenue. As technology continues to evolve, the use of big data is beginning to transform processes in many different industries, making them more efficient and effective. The opportunities presented by big data have been researched to a large extent in commercial fields, however, research in the use of big data in healthcare is scarce and this is particularly true in the case of Sweden. Furthermore, there is a lack in research that explores the interface between big data, healthcare and revenue models. The interface between these three fields of research is important as innovation and the integration of big data in healthcare could be affected by the ability of companies to generate a revenue from developing such innovations or solutions. Thus, this thesis aims to fill this gap in research and contribute to the limited body of knowledge that exists on this topic. The study conducted in this thesis was done via qualitative methods, in which a literature search was done and interviews were conducted with individuals who hold managerial positions at Region Västerbotten. The purpose of conducting these interviews was to establish a better understanding of the Swedish healthcare system and how its structure has influenced the use, or lack thereof, of big data in the healthcare delivery process, as well as, how this structure enables the generation of revenue through big data solutions. The data collected was analysed using the grounded theory approach which includes the coding and thematising of the empirical data in order to identify the key areas of discussion. The findings revealed that the current state of the Swedish healthcare system does not present an environment in which big data solutions that have been developed for the system can thrive and generate a revenue. However, if action is taken to make some changes to the current state of the system, then revenue generation may be possible in the future. The findings from the data also identified key barriers that need to be overcome in order to increase the integration of big data into the healthcare system. These barriers included the (i) lack of big data knowledge and expertise, (ii) data protection regulations, (iii) national budget allocation and the (iv) lack of structured data. Through collaborative work between actors in both the public and private sectors, these barriers can be overcome and Sweden could be on its way to transforming its healthcare system with the use of big data solutions, thus, improving the quality of care provided to its citizens. Key words: big data, healthcare, Swedish healthcare system, AI, revenue models, data-driven revenue models

Many firms are challenged to make inventory decisions with limited data, and high customer service level requirements. This thesis focuses on heuristic solutions for inventory management problems in data-scarce environments, employing rigorous mathematical frameworks and taking advantage of the information that is available in practice but often ignored in literature. We define a class of inventory models and solutions with demonstrable value in helping firms solve these challenges.

Eftersom dagens affärsklimat ständigt utvecklas i och med utökad konkurrens behöver organisationer fatta beslut som är baserade på data i ett tidigt skede. Business Intelligence (BI) tillhandahåller beslutsfattare inom organisationer snabb och riktig information som kan användas som beslutsstöd. I och med att BI’s omfattning gått från enstaka avdelningar till att beröra hela organisationer sätter det stor press på experter inom IT-avdelningar.Det bidrar till att slutanvändare behöver en miljö som ger dem direkt åtkomst till data för egna analyser och beslut. Den miljön nås genom att implementera Self-Service Business Intelligence (SSBI). Det SSBI gör är att det effektiviserar processen för beslut. När SSBI implementeras kräver det att de slutanvändare som berörs av det behöver utöka sina kunskaper för att utnyttja potentialen vilket SSBI medför. För nuvarande saknas forskning kring vilka kunskaper slutanvändare behöver inneha vilket har bidragit till att följande frågeställning kommer att undersökas i studien:➢ Vilka grundläggande kunskaper bör en slutanvändare inneha vid användningen av Self-Service Business Intelligence?Studien grundas i en litteraturgranskning och en fallstudie där intervjuer av sex respondenter, vilka förfogar över god kunskap kring SSBI, använts för datainsamling. Resultatet framställer fyra grundkunskaper vilka slutanvändare bör inneha för att öka möjligheten att börja använda SSBI på ett mer framgångsrikt sätt.

Thesis advisor: Diana Pullin / The current high stakes testing environment has resulted in intense pressure on schools to become more data-driven. As a result, an increasing number of schools are implementing systems where teachers and school leaders collaboratively analyze assessment data and use the results to inform instructional practice. This study examined how teacher participation in the analysis of assessment data influences instructional outcomes. It also examined how levels of capacity in the areas of data use, professional learning, and leadership interact to influence the ability to respond to data. The method is a qualitative case study of an elementary school in the southeastern United States that has implemented formal structures for analyzing and collaborating around assessment data. Data collection occurred through teacher and administrator interviews, data analysis meeting observations, and through the examination of school and district documents. The school in this study responded to data analysis results through three major actions: large-scale initiatives designed to improve instruction in various content areas, remediation, and individual teacher variations in instructional practices. Findings show that while teachers express support for data analysis and suggest positive benefits for the school, they also indicate that participation in data analysis and the resultant improvement efforts have had minimal to modest impact on their teaching practices. Possibly contributing to this outcome was the finding that the school had uneven capacity in the areas of data use, professional learning, and leadership. The school has a well-developed system for data access and reporting. However, it has been less successful in providing the professional learning experiences that will enable more substantial changes in teacher beliefs and practices. Furthermore, a lack of clarity regarding the instructional purpose of data analysis from multiple levels of district and school leadership and the procedural nature of the data analysis process has reduced the ability of school leaders to effectively leverage data analysis for the purpose of substantive and sustained instructional improvement. / Thesis (PhD) — Boston College, 2011. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Leadership and Higher Education.

Thesis advisor: Henry I. Braun / Data-based instructional programs, including interim assessments, are a common tool for improving teaching and learning. However, few studies have rigorously examined whether they achieve those ends and contributors to their effectiveness. This study conducts a secondary analysis of data from a matched-pair school-randomized evaluation of the Achievement Network (ANet). Year-two teacher surveys (n=616) and interviews from a subset of ANet school leaders and teachers (n=40) are used to examine the impact of ANet on teachers’ data-based instructional practices and the mediating roles of instructional leadership, professional and achievement cultures, and teacher attitudes and confidence. Survey results showed an impact of ANet on the frequency with which teachers’ reviewed and used data, but not their instructional planning or differentiation. Consistent with the program model, ANet had a modest impact on school-mean teacher ratings of their leaders’ instructional leadership abilities and school culture, but no impact on individual teachers’ attitudes toward assessment or confidence with data-based instructional practices. Therefore, it was not surprising that these school and teacher characteristics only partially accounted for ANet’s impact on teachers’ data practices. Interview findings were consistent. Teachers described numerous opportunities to review students’ ANet assessment results and examples of how they used these data (e.g., to pinpoint skills on which their students struggled). However, there were fewer examples of strategies such as differentiated instruction. Interview findings also suggested some ways leadership, culture, and teacher characteristics influenced ANet teachers’ practices. Leaders’ roles seemed as much about holding teachers accountable for implementation as offering instructional support and, while teachers had opportunities to collaborate, a few schools’ implementation efforts were likely hampered by poor collegial trust. Teacher confidence and attitudes varied, but improved over the two years; the latter following from a perceived connection between ANet practices and better student performance. However, some teachers were concerned with the assessments being too difficult for their students or poorly aligned with the curriculum, resulting in data that were not always instructionally useful. / Thesis (PhD) — Boston College, 2016. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

The punctuality of rail traffic in Sweden has not increased in a long period of time and this causes problems for people, companies and the community as it affects everyone in different ways. How the Swedish Transport Administration and SJ work on improving the train traffic and punctuality will be addressed in this study. This study will have focus on how data-driven methods can increase the punctuality of train traffic. The study will show which factors are critical for data-driven methods using literature as well as models with descriptions, as a result, the Swedish Transport Administration and SJ will be in focus for how the development of punctuality of train traffic goes. This is a case study with a literature search as well as qualitative interviews as data collection, the literature search will primarily show which factors are necessary for datadriven methods and it will also help form the interview questions, later on the interviews will show how the Swedish Transport Administration and SJ work today so that comparisons can be drawn and a result can be produced.

Este trabalho apresenta uma extensão à formulação multivariável do método de controle baseado em dados conhecido como o Método da Referência Virtual, ou Virtual Reference Feedback Tuning (VRFT). Ao lidar com processos onde o ruído é significativo, as formulações tradicionais do VRFT, por mínimos quadrados ou variáveis instrumentais, apresentam propriedades estatísticas insatisfatórias, que acabam levando o sistema de controle em malha fechada a desempenhos muito distantes daqueles especificados pelo projetista. Portanto, visando aprimorar a qualidade destas estimativas e, consequentemente, os desempenhos em malha fechada, esta dissertação propõe a adição de regularização no método VRFT para sistemas multivariáveis. Regularização é uma ferramenta que vem sendo amplamente utilizada e desenvolvida nos últimos anos nas comunidades de Identificação de Sistemas e Machine Learning e é indicada para reduzir a alta covariância que existe nas estimativas - problema que ocorre na formulação do VRFT com variáveis instrumentais. Também, como contribuições deste trabalho destacam-se uma análise mais detalhada do problema de identificação com regularização para sistemas multivariáveis, assim como o desenvolvimento da matriz ótima de regularização para este cenário e as propriedades da nova formulação do VRFT. Para demonstrar a eficiência desta nova formulação do VRFT são desenvolvidos exemplos numéricos. / This work proposes a new extension for the multivariable formulation of the datadriven control method known as Virtual Reference Feedback Tuning. When the process to be controlled contains a significant amount of noise, the standard VRFT approach, that uses either the least squares method or the instrumental variable technique, yield estimates with very poor statistical properties, that may lead the control system to undesirible closed loop performances. Aiming to enhance these statistical properties and hence, the system’s closed loop performance, this work proposes the use of regularization on the multivariable formulation of the VRFT method. Regularization is a feature that has been widely used and researched on the System Identification and Machine Learning communities on the last few years, and it is well suited to cope the high variance issue that emerge on the VRFT method with instrumental variable. Also, a more detailed analysis on the use of regularization for identification of multivariable systems, the proof of the optimal regularization matrix and the exposure of the new regularized VRFT properties can be highlighted as novelties of this work.

Out of the campaign analysis the most important contribution is that Data-Driven Marketing makes sense only once it is already part of the marketing plan. So the team preparing the marketing plan defines the goals and sets the proper measurement matrix according to those goals. It enables to adjust the marketing plan to extract more value, watch the execution and do adjustments if necessary and evaluate at the end of the campaign.

Closing the achievement gaps in mathematics and English language arts (ELA) is an ongoing challenge for most New York City Public school administrators. One New York school experiencing this problem implemented a broad intervention including (a) the Children First Intensive (CFI) program, which includes using data to inform instructional and organizational decision-making; (b) added baseline and post assessments; and (c) differentiated instruction including student conferences. The effects of the intervention had not been evaluated within the context of implementation. The purpose of this quantitative study was to evaluate the impact of the multifaceted learning gaps' intervention on 6th grade student achievement in math and ELA. The framework used in this study was the Halverson, Grigg, Prichett, and Thomas data-driven instructional systems model. The comparative study design used paired t tests to examine the change in math and ELA achievement scores on a group of 6th grade students (N = 26), before after the intervention. Results indicated significant increases in the test scores of the students, suggesting that students' learning gaps were closed using their assessment results and differentiated instruction within the comprehensive intervention. Results were used to create a professional development handbook on using a multifaceted data-based approach to improve student achievement. Positive social change might occur by providing the local site findings on the outcomes of their approach and additional training on using the approach, which may ultimately improve the academic performance of all students.