Cheating, beguiling, and misleading information exist all around us; understanding deception and its consequences is crucial in our information environment. This study investigates deception in phishing emails that successfully bypassed Microsoft 365 filtering system. We devised a model that explains why some people are deceived and how targeted individuals and organizations can prevent or counter attacks. The theoretical framework used in this study is Anderson's functional ontology construction (FOC). The methodology involves quantitative and qualitative descriptive design, where the data source is the set of phishing emails archived from a Tier 1 University. We looked for term frequency-inverse document frequency (Tf-idf) and the distribution of words over documents (topic modeling) and found the subjects of phishing emails that targeted educational organizations are related to finances, jobs, and technologies. Also, our analysis shows the phishing emails in the dataset come under six categories; reward, urgency, curiosity, fear, job, and entertainment. Results indicate that staff and students were primarily targeted, and a list of the most used verbs for deception was compiled. We uncovered the stimuli being used by scammers and types of reinforcements used to misinform the target to ensure successful trapping via phishing emails. We identified how scammers pick their targets and how they tailor and systematically orchestrate individual attack on targets. The limitations of this study pertain to the sample size and the collection method. Future work will focus on implementing the derived model into software that can perform deception identification, target alerting and protection against advanced email phishing.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Process Mining is a discipline that recognizes three types of analysis: Discovery, monitoring, and process improvement. Organizations are focusing on redesigning and automating their major processes, according to a report published in 2018 [1]. In this way, a challenge n process mining is to show the results of the process analysis in a way that is understandable to non-expert users. Therefore, this research paper introduces a matrix of guidelines to guide process mining specialists/tool developers to improve the results of the analysis in process mining projects. This matrix is composed of 2 study fields that throughout the literature have been merging their virtues. First, process mining under 2 of its 3 types of projects: (1) based on objectives and (2) based on questions. The last type is based on data (exploratory analysis). Second, visualization of data with its techniques to represent data graphically. This research proposes a matrix of guidelines that integrates the discipline of process mining and the set of data visualization techniques based on the purpose of each graph (technique), the question / objective to be achieved and the importance that colors take in the analysis results in the process mining projects. / Revisión por pares

This study wants to investigate how Interaction Design techniques can contribute to giving meaning to data visualization in a syndemic dashboard and to gain understanding from it. I am going to present to you a Syndemic Dashboard that has the goal of helping researchers to find trends, patterns and make predictions of the spread of Covid-19 in the Swedish context, collaborating with K3, IUR, DVMT, and the University of Oxford. In order to do this, I will first give an overview of what a dashboard is, dashboarding practices and interaction techniques, cognitive aspects involved to generate meaning, and relevant theories to gain understanding from Big Data. Consequently, I will explain the process and the methodologies applied to achieve the final result. The thesis ends with a discussion about the final result and proposes future investigations.

El presente trabajo propone un modelo para la evaluación de variables en el sector salud utilizando Process Mining y Data Visualization soportado por la herramienta Celonis. Esto surge ante la problemática orientada a la dificultad en la comprensión de las actividades que están involucradas en los procesos negocios y los resultados de este. El proyecto se centra en la investigación de dos disciplinas emergentes. Una de estas disciplinas es Process Mining y se enfoca principalmente en los procesos, en los datos por cada evento, esto con el fin de descubrir un modelo, ver conformidad de los procesos o mejorarlos (Process Mining: Una técnica innovadora para la mejora de los procesos, 2016). La segunda disciplina es Data Visualization, esta permite presentar los datos en un formato gráfico o pictórico ("Data Visualization: What it is and why it matters", 2016). El proyecto implica principalmente investigación, en primer lugar, se analizan las técnicas de Process Mining y Data Visualization. En segundo lugar, se separan las características y cualidades de las disciplinas, y se diseña un modelo para la evaluación de variables en el Sector Salud utilizando Process Mining y Data Visualization, generando un valor agregado, dado que al tener un formato gráfico o pictórico que representa adecuadamente los resultados de usar una técnica de minería de procesos, la comprensión y el análisis en la toma de decisiones es más precisa. En tercer lugar, se valida el modelo en una institución que brinda servicios en el Sector Salud, analizando uno de los procesos core. Finalmente, se elabora un plan de continuidad para que el modelo propuesto se aplique en técnicas de optimización de procesos en las organizaciones. / The present work proposes a model for the evaluation of variables in the health sector using Process Mining and Data Visualization supported by the Celonis tool. This arises from the problem oriented to the difficulty in understanding the activities that are involved in business processes and their results. The project focuses on the investigation of two emerging disciplines. One of these disciplines is Process Mining and it focuses mainly on the processes, on the data for each event, this in order to discover a model, see conformity of the processes or improve them (Process Mining: An innovative technique for the improvement of the processes, 2016). The second discipline is Data Visualization, this allows data to be presented in a graphic or pictorial format ("Data Visualization: What it is and why it matters", 2016). This project mainly involves research, first, Process Mining and Data Visualization techniques are analyzed. Second, the characteristics and qualities of the disciplines are separated, and a model is designed for the evaluation of variables in the Health Sector using Process Mining and Data Visualization, generating added value, given that by having a graphic or pictorial format that adequately represents the results of using a process mining technique, understanding and analysis in decision making is more accurate. Third, the model is validated in an institution that provides services in the Health Sector, analyzing one of the core processes. Finally, a continuity plan is drawn up so that the proposed model can be applied to process optimization techniques in organizations. / Tesis

The first reported case of COVID-19 in Tennessee (TN) occurred on March 5, 2020, growing to 580,809 cases state-wide by the end of 2020. A GIS dashboard was developed using data from the TN Department of Health to communicate state-wide COVID-19 spread, and a relationship between pandemic development and rurality was observed during the first wave (through September 2020), noted in other US and global research. Because > 90% of TN counties are designated rural or mixed-rural, we examined metrics to describe development as it relates to rurality. Metrics included days to the first case/hospitalization/fatality, days between state and county peak, and days to an incidence rate of ten per 100,000. Metrics were compared within different classes of rurality, using seven rurality classification schemes. Significant differences were noted in four of the five metrics between classes of rurality. Rural counties in TN experienced significant lags to the first case, hospitalization, and fatality, and the peak cases in rural counties were delayed relative to urban counties when outlier counties with early state prison outbreaks were excluded. In rural TN counties, regardless of rurality definition, cases, hospitalizations, and fatalities were slower to appear. However, once community spread was established, rurality no longer had a protective effect.

Background. In today’s competitive world, dealing with real-time streaming data is a difficult task to be achieved by many organizations. The importance of real time streaming data is rapidly increasing in all software industries by passing time. For quick growth of the companies, the data should be analysed immediately as data will be changing in fraction of second. The huge data will be generated every day and it will lead to problems such as overload of resources, Performance delays etc.., Which in turn will impact behaviour of the system. Finding the problem area in real time is difficult task to achieve as the data changes every second. Dealing with detection of bottlenecks and making decisions to handle the problem area, based on the real time data has been slow over the past years. It is also complicated due to time and effort required for storing and analysing. Organizations are not intended to wait for decision making information up to weeks or months. Organizations need to make an timely-accurate decisions by detecting problem area, in real time to improve their business support systems behaviour and performance. One of the better solutions is through data visualization as an approach. The visualizations are developed and evaluated by using task based approach. The data is collected using interviews and paper survey, to obtain the effective and efficient visualization in detecting bottlenecks. Objectives. The main objective is to find the most effective and efficient data visualization technique for real time streaming data to detect potential bottlenecks. Methods. In this research study, an action research is opted to answer the objectives. We have used interviews and paper survey to collect data in the terms of performance time, accuracy rate and user preference. Data analysis is performed using the Statistical tests and Narrative analysis method. Results. The final results obtained are the effective and efficient visualization techniques based on less performance time, higher accuracy rate and better user preference. Conclusions. An effective and efficient visualization technique for detection of bottlenecks is obtained for real time streaming data. Different categories of tasks has been used to obtain accurate results.

This thesis explores the topic of how one can streamline a data journalists analytical workflow in a graph visualization environment. Interactive graph visualizations have been used recently by data journalists to investigate the biggest leaks of data in history. Graph visualizations empower users to find patterns in their connected data, and as the world continuously produces more data, the more important it becomes to make sense of it. The exploration was done by conducting semi-structured interviews with users, which illuminated three categories of insights called Graph Readability, Charts in Graphs and Temporality. Graph Readability was the category that were conceptualized and designed by integrating user research and data visualization best practises. The design process was concluded with a usability test with graph visualization developers, followed by a final iteration of the concept. The outcome resulted in a module that lets users simplify their graph and preserve information by aggregating nodes with similar attributes.

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The benefits of being able to receive individualized guidance to support learning have been shown in various studies. However, most teachers are too pressured by time and budget constraints to be able to offer this. Thus, personalized learning has become a luxury that most teachers cannot provide. However, with the rapid development of digital technologies, new opportunities are arising within this field. Scalability challenges are being overcome when digital learning platforms now can collect data and use it to generate insights concerning each student’s individual progress and challenges for the teacher to access through a dashboard. The aim of this case study is to contribute to the formation of a best practice for the design of teacher-facing dashboards (TFD) to improve learning outcomes. A user-centric approach was selected to identify obstacles and areas of improvement. These were identified by conducting interviews and think-alouds to evaluate an existing dashboard designed by the language platform Astrid Education. The results and insights are demonstrated through a redesign suggestion of the dashboard along with the teacher's evaluation of it. / Många studier har påvisat positiva effekter på lärandet när språkundervisningen är anpassad till den studerandes individuella behov. Ändå har språklärare sällan möjlighet att erbjuda ett individanpassat stöd för varje elev på grund av tids- och budgetbegränsningar. Tack vare teknikens utveckling har nya möjligheter skapats inom lärandeformer. I allt större utsträckning övergår utbildning till digitala läromedel vilket medför en tillgänglighet till information om elevers kunnande och prestation. Denna data kan sammanställas och presenteras för lärare för att erbjuda en djupare förståelse för varje elevs behov. Syftet med denna studie är att bidra till bildandet av bästa praxis för dessa dataöversikter för lärare, sk. teacher-facing dashboards (TFD) med avsikt att förbättra resultaten inom språkinlärning. En användarcentrerad designmetod valdes för att identifiera begränsningar och förbättringsområden i en digital lärplattform för engelsk språkinlärning tillhörande Astrid Education. Testanvändare intervjuades och observerades. Resultat och insikter demonstrerades genom ett designförslag tillsammans med lärares utvärdering av denna.

Datenvisualisierungen und Infografiken sind spätestens seit Ausbruch der Corona-Pandemie in aller Munde oder besser gesagt „in aller Augen“. Kaum ein News-Portal, kaum eine Online-Ausgabe renommierter Zeitungen kommt ohne die fast schon obligatorische interaktive Datenvisualisierung über den Verlauf der Pandemie, die Entwicklung der Infektionszahlen oder einen Ländervergleich aus. Der vorliegende erste Sammelband zum Thema möchte (interaktive) Datenvisualisierung praxisorientiert aufgreifen, um sowohl die grundlagen-orientierte wie auch die angewandte Forschung zu inspirieren, näher zusammenzuführen, zukünftige Forschung zu unterstützen sowie für offene Fragen in diesem dynamischen Prozess zu sensibilisieren.:O. Tabino, C. M. Stützer & A. Wachenfeld-Schell, Editorial Board: Data Visualization and Information Design: Bringing Data to Life B. Wiederkehr: Interactive Things Data Visualization for Exploration and Explanation S. Sieben & P. Simmering, Q | Agentur für Forschung GmbH: Storytelling vs. Dashboards – Wie Sie die richtige Methode zur Datenvisualisierung auswählen M. Bonera, The Visual Agency | Politecnico di Milano: Data Visualization as a Tool to Access Leonardo da Vinci’s Greatest Work: The Codex Atlanticus P. Blau, GIM Gesellschaft für Innovative Marktforschung mbH: Visualisierung qualitativer Daten: Die Komplexität des Einfachen / Since the outbreak of the Corona pandemic at the latest, data visualisations and infographics have been on everyone's mind, or rather 'in everyone's eyes'. Barely any news portal or online edition of well-known newspapers can do without the almost obligatory interactive data visualisation on the path of the pandemic, the development of infection figures or a comparison of countries. This first volume on this topic aims to take up (interactive) data visualisation in a practice-oriented way in order to inspire both fundamentally-oriented and applied research, to bring it closer together, to support future research as well as to sensitise for open questions in this dynamic process.:O. Tabino, C. M. Stützer & A. Wachenfeld-Schell, Editorial Board: Data Visualization and Information Design: Bringing Data to Life B. Wiederkehr: Interactive Things Data Visualization for Exploration and Explanation S. Sieben & P. Simmering, Q | Agentur für Forschung GmbH: Storytelling vs. Dashboards – Wie Sie die richtige Methode zur Datenvisualisierung auswählen M. Bonera, The Visual Agency | Politecnico di Milano: Data Visualization as a Tool to Access Leonardo da Vinci’s Greatest Work: The Codex Atlanticus P. Blau, GIM Gesellschaft für Innovative Marktforschung mbH: Visualisierung qualitativer Daten: Die Komplexität des Einfachen