In the complex landscape of healthcare, patients navigate through various institutions from hospitals to long-term care facilities, and each step of their journey plays a crucial role in their disease progression and treatment plan. Traditional analyses often focus on individual transitions, offering limited insight into the broader picture of patient care and disease progression. This thesis aims to explore the entire sequence of patient transitions within the Canadian healthcare system to uncover meaningful patterns and commonalities. This research employs an innovative approach to leveraging the Canadian Institute for Health Information (CIHI) dataset, consisting of around 250,000 patient records after data cleaning and including approximately 10-11 variables. Extracting a diverse category of features, such as temporal, semantic, and clinical information, constructs a detailed profile for each patient journey. These profiles then undergo an parallel mini-batch average agglomerative hierarchical clustering process, grouping together patients with similar healthcare trajectories to identify prevailing pathways and transitions within the system. By understanding these patterns, healthcare providers and policymakers can gain insights into the patient experience, potentially revealing areas for improvement, optimization, and personalization of care. Key findings include uncovering transitions in the healthcare environment, identifying the most common pathways, and studying the alternate level of care length of stay for each scenario. Looking ahead, the research anticipates incorporating additional layers of data, such as specific interventions and medications, to enrich the analysis. This expansion aims to offer a more comprehensive view of patient journeys, further enhancing the ability to tailor healthcare services to meet individual needs effectively. / Thesis / Master of Computer Science (MCS)

Yes / Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage and enhance high value businesses performance. The purpose of our study is to investigate the impact of BDA on operations management in the manufacturing sector, which is an acknowledged infrequently researched context. Using an interpretive qualitative approach, this empirical study leverages a comparative case study of three manufacturing companies with varying levels of BDA usage (experimental, moderate and heavy). The information technology (IT) business value literature and a resource based view informed the development of our research propositions and the conceptual framework that illuminated the relationships between BDA capability and organizational readiness and design. Our findings indicate that BDA capability (in terms of data sourcing, access, integration, and delivery, analytical capabilities, and people’s expertise) along with organizational readiness and design factors (such as BDA strategy, top management support, financial resources, and employee engagement) facilitated better utilization of BDA in manufacturing decision making, and thus enhanced high value business performance. Our results also highlight important managerial implications related to the impact of BDA on empowerment of employees, and how BDA can be integrated into organizations to augment rather than replace management capabilities. Our research will be of benefit to academics and practitioners in further aiding our understanding of BDA utilization in transforming operations and production management. It adds to the body of limited empirically based knowledge by highlighting the real business value resulting from applying BDA in manufacturing firms and thus encouraging beneficial economic societal changes.

Yes / Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape.Given the significant nature of the BDand BDA, this paper presents a state-ofthe- art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/ employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 –What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline.

Yes / In view of the burgeoning scholarly works on big data and big data analytical capabilities, there remains limited research on how different access to big data and different big data analytic capabilities possessed by firms can generate diverse conditions leading to business failure. To fill this gap in the existing literature, an integrated framework was developed that entailed two approaches to big data as an asset (i.e. threshold resource and distinctive resource) and two types of competences in big data analytics (i.e. threshold competence and distinctive/core competence). The analysis provides insights into how ordinary big data analytic capability and mere possession of big data are more likely to create conditions for business failure. The study extends the existing streams of research by shedding light on decisions and processes in facilitating or hampering firms’ ability to harness big data to mitigate the cause of business failures. The analysis led to the categorization of a number of fruitful avenues for research on data-driven approaches to business failure.

Yes / The governments, nowadays, throughout the world are increasingly becoming dependent on public opinion regarding the framing and implementation of certain policies for the welfare of the general public. The role of social media is vital to this emerging trend. Traditionally, lack of public participation in various policy making decision used to be a major cause of concern particularly when formulating and evaluating such policies. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. Cloud-based e-governance is currently being realized due to IT infrastructure availability along with mindset changes of government advisors towards realizing the various policies in a best possible manner. This paper presents a pragmatic approach that combines the capabilities of both cloud computing and social media analytics towards efficient monitoring and controlling of governmental policies through public involvement. The proposed system has provided us some encouraging results, when tested for Goods and Services Tax (GST) implementation by Indian government and established that it can be successfully implemented for efficient policy making and implementation.

Yes / Purpose This study aims to understand pumpkin waste awareness among people by converting unstructured quantitative data into insightful information to understand the public's awareness of pumpkin waste during Halloween. Design/methodology/approach To fulfil the study's purpose, we extracted Halloween-related tweets by employing #halloween and #pumpkin hashtags and then investigated Halloween-related tweets via a topic modelling approach, specifically Latent Dirichlet Allocation. The tweets were collected from the UK between October 25th and November 7th, 2020. The analysis was completed with 11,744 tweets. Findings The topic modelling results revealed that people are aware of the pumpkin waste during Halloween. Furthermore, people tweet to reduce pumpkin waste by sharing recipes for using leftover pumpkins. Originality/value The study offers a novel approach to convert social media data into meaningful knowledge about public perception of food waste. This paper contributes to food waste literature by revealing people's awareness of pumpkin waste during Halloween using social media analytics. Norm activation model and communicative ecology theory are used for the theoretical underpinning of topic modelling.

No / This book provides a review of the state of the art in computer graphics, visualization, and visual analytics by researchers and developers who are closely involved in pioneering the latest advances in the field. It is a unique presentation of multi-disciplinary aspects in visualization and visual analytics, architecture and displays, augmented reality, the use of color, user interfaces and cognitive aspects, and technology transfer. It provides readers with insights into the latest developments in areas such as new displays and new display processors, new collaboration technologies, the role of visual, multimedia, and multimodal user interfaces, visual analysis at extreme scale, and adaptive visualization.

Von den frühen Tagen der Logfile-Analysen bis hin zur heutigen Verwendung von fortschrittlichen Tracking-Systemen wie Google Analytics hat sich die Nutzerverfolgung im Netz stetig weiterentwickelt. Doch während sie Websitebetreibern und Werbedienstleistern wertvolle Informationen liefert, wirft sie auch Fragen hinsichtlich des Privatsphäre- und Datenschutzes auf. Das Sammeln von persönlichen Daten und deren anschließende Verwendung ruft bei vielen Menschen Besorgnis hervor. Die Gesetzgeber reagieren darauf mit immer strengeren Datenschutzgesetzen, die das Aggregieren, Verarbeiten und Speichern von personenbezogenen Daten in der Webanalyse einschränken. Viele Unternehmen stehen daher vor der Herausforderung, ihre Tracking-Infrastruktur zu überdenken und an die Vorgaben anzupassen. Spätestens mit der bevorstehenden Abschaffung der sogenannten Third-Party-Cookies sind Websitebetreiber gezwungen, aktiv zu werden. Diese Arbeit zielt darauf ab, Tracking-Technologien und -Strategien hinsichtlich ihrer Zukunftssicherheit zu analysieren, um einen Kompromiss zwischen den Interessen der Gesetzgebung und den Anbietern sowie Nutzern von Tracking-Tools zu finden.:Inhaltsverzeichnis Abkürzungsverzeichnis 1. Einleitung 2. Datenschutzrechtliche Rahmenbedingungen 2.1 Geschichte des Datenschutzrechts 2.2 Die DSGVO: Auswirkungen und Grundsätze 2.3 Rechtliche Einordnung von Tracking-Technologien 3. Grundlagen des Webtrackings 3.1 Cookies 3.1.1 Funktionsweise 3.1.2 Unterscheidung nach Lebensdauer 3.1.3 Unterscheidung nach Quelle 3.1.4 Unterscheidung nach Nutzungsart 3.1.5 Third-Party-Cookies in der Kritik 3.2 Tracking-Pixel 3.3 Device Fingerprinting 3.4 Datenqualität in der Krise 4. Tracking-Strategien in der Praxis 4.1 CNAME-Cloaking 4.1.1 Implementierung 4.1.2 Risiken 4.1.3 Datenschutzrechtliche Einordnung 4.2 Server Side Tracking 4.2.1 Tagging mit dem Google Tag Manager 4.2.2 Risiken 4.2.3 Datenschutzrechtliche Einordnung 4.3 Shynet 4.3.1 Implementierung und Quellcode-Analyse 4.3.2 Risiken 4.3.3 Datenschutzrechtliche Einordnung 5. Status Quo und Ausblick 5.1 Google Consent Mode v2 5.2 Browseranbieter 5.3 Cookie Pledge 5.4 E-Privacy-Verordnung 6. Fazit Literaturverzeichnis Abbildungsverzeichnis Eigenständigkeitserklärung

Managers rely on analytics to make decisions and the choice of the analytical method can influence their decision-making. This dissertation considers three cases and examines how the choice of analytical methods influence interpretations and implications. These areas are communication for health-related information in social media, health information technology investment by hospitals as it relates to patient satisfaction, and health related expenditure policies of countries. These studies develop theoretical models and empirically test them on primary or secondary data, comparing the performance of popular analytical methods. The conduct of these three studies contributes to a better understanding about the choice of analytical methods and allow development of a heuristic approach by offering guidelines for selecting an appropriate methodology. They demonstrate the value of heuristic approaches for use with non-traditional and traditional statistical methods, as the information gained from non-traditional methods (NNs) provides insights into traditional statistical methods, similar to insights gained from exploratory data analysis. The studies also show the value in examining any dataset with multiple methods because they either confirm each other or fail to confirm, providing insights.

A la différence de l'apprentissage par mémorisation (rote learning), l'apprentissage significatif (meaningful learning) vise à associer de nouvelles connaissances à des connaissances déjà acquises. La construction de cartes mentales exige et supporte la mise en place de stratégies d'apprentissage significatif, et permet de rendre visible la structure de connaissances de l'apprenant. Cependant, les enseignants qui intègrent la construction de cartes mentales dans leurs activités pédagogiques doivent se contenter du rendu final des cartes mentales, et risquent de faire de mauvaises interprétations et évaluations de celles-ci par manque d'information sur leur processus de construction. Dans cette thèse, nous nous intéressons à l'observation a posteriori de l'engagement de l'apprenant dans ses dimensions comportementale et cognitive, afin de proposer des indicateurs orientés processus qui permettent de comprendre ses actions et ses choix de construction de carte mentale. Nous avons suivi la méthodologie de recherche orientée par la conception (Design Based Research), qui nous a permis de proposer 3 niveaux de contributions : 1) un modèle théorique d'engagement (comportemental et cognitive) pour les activités de construction de cartes mentales, 2) un ensemble d'indicateurs d'engagement de l'apprenant à partir de traces capturées automatiquement lors de la construction de cartes mentales, et 3) un tableau de bord appelé MindMap Monitor présentant différents indicateurs à l'enseignant pour le suivi de la classe et des apprenants. Le modèle a été obtenu à partir d'une étude de littérature sur les théories de l'engagement de l'apprenant issues notamment de la recherche en psychologie de l'éducation. Les indicateurs ont été définis en croisant le modèle avec les résultats de plusieurs études de terrain avec les enseignants. Le tableau de bord implémentant les indicateurs a été construit en trois itérations. Son interface présente des vues synthétiques permettant de comparer les élèves de la classe et d'identifier ceux en difficulté lors de la construction de leur carte, et des vues détaillées décrivant l'activité de construction d'une carte pour chaque élève. Notre tableau de bord a été évalué au cours d'une expérimentation avec 12 enseignants en comparant son utilisation avec celle des cartes mentales finales associées aux vidéos de leur processus de construction. Les résultats montrent que nos indicateurs sur MindMap Monitor permettent de mieux identifier les élèves en difficulté, les difficultés partagées, ainsi que les difficultés pour un élève. Les résultats concernant la compréhension du processus de construction de la carte mentale sont plus nuancés. Nous avons également pu identifier plusieurs pistes d'amélioration sur le contenu du tableau de bord et sa présentation. Les perspectives de notre travail concernent principalement le suivi de l'engagement des apprenants en temps réel pour l'intervention et l'adaptation de la stratégie pédagogique / In contrast to rote learning, meaningful learning aims to associate new knowledge with knowledge already acquired. Mind mapping activities require and support the implementation of meaningful learning strategies and enlighten the knowledge structure of the learner. However, teachers who integrate mind mapping into their educational activities have to deal with the final rendering of maps, and risk to misinterpret and wrongly evaluate them due to the lack of information about their construction process. In this thesis, we are interested in a posteriori observation of the engagement of learners along its behavioral and cognitive dimensions, in order to propose processoriented indicators that help to understand actions and construction choices of mind maps. We followed the Design Based Research methodology, that allowed us to propose three levels of contributions : 1) a theoretical model of engagement (behavioral and cognitive) for mind mapping activities, 2) a set of indicators of learner engagement constructed from automatically captured map building traces, and 3) a dashboard called MindMap Monitor presenting various indicators to teachers for class and learners monitoring. The model was obtained from a literature review on theories of engagement, including research in educational psychology. The indicators have been defined by comparing the model with the results of several field studies with teachers. The dashboard implementing the indicators was developed following three iterations. Its interface presents synthetic views allowing the comparison of students in the class, the identification of those in difficulty, and detailed views describing the mind mapping activity for each student. Our dashboard was evaluated with an experiment involving 12 teachers. We compared its use with that of final mind maps associated with videos of their construction process. Results show that our indicators on MindMap Monitor are useful to better identify students in difficulty, shared difficulties, as well as difficulties for individual students. Results concerning the understanding of the mind maps construction process are more balanced. We were also able to identify several ways to improve both the content and the visualizations of the dashboard. The perspectives of our work are mainly related to monitoring learners’ engagement in real time for the intervention and adaptation of the teachers’ educational strategies