Many business intelligence tools aim to digest data into easy, understandable and visualizable information for helping decision-making, while they are still lack of ability to support visualizing the history of selections. This limitation concerns the coming future when everything is about data. Due to it, users are not able to share their thinking paths to the decision. Here a history selection path means a sequence of previous selections. As an approach, it helps users in decision-making and discovery insight. This study investigated an efficient graphical visualization system of history selection paths to support communicating and iterative analysis. We selected tree representation as the main visualization model and also propose features needed for the system. Specifically, we researched the significance of this study, existing solutions and also the proper designs and functions for the idea. It is initiated by user research including targeting users and scenario mapping. Based on the understanding, we applied a parallel design to narroww down the suitable design. As a result, tree representation was selected as the visualization model. To evaluate whether it touched user needs or not, we applied usability test to collect quantitative data and qualitative comments. For making the test environment as real as possible, a webbased interactive prototype supported by D3.js library was implemented for testing. We analyzed the user experience and also consolidated improvements. As a case study, we implemented the solution on Qlik Sense to verify the possibility to place this solution into real data visualization tool. Generally, the result of this study formed a valuable initiative for further development and we saw potentials of this tree model system to be used in other areas when it comes to reviewing history as well. / Många verktyg för affärsintelligens avser till att bryta ner data till enkel, förståelig och visualiserar information för att hjälpa till beslutsantagande, medan det fortfarande saknar förmåga att stödja visualiseringen av urvalens historik. Den här begränsningen berör framtiden när allt är om data. På grund av det, användaren är inte kunniga till att dela deras sökväg till beslutet. Här menas historik urvalsväg en sekvens av tidigare val. Som ett tillvägagångsätt, hjälper det användare att fatta beslut och upptäcka insikt. Denna studie undersökte ett effektivt grafiskt visualiseringssystem av historik urvalsvägar för att stödja kommunikation och iterativ analys. Vi valde trädrepresentation som huvudligavisualisering modell och föreslår också funktioner som behövs för systemet. Specifikt har vi undersökt betydelsen av denna studie, befintliga lösningar och även rätt design och funktioner för denna idé. Det initieras av användare undersökningar inklusive målriktning av användare och scenariokartläggning. Baserat på förståelsen använde vi en parallell design för att begränsa den lämpliga designen. Som ett resultat, valdes trädrepresentation som visualiseringsmodell. För att utvärdera om det rörde användarnas behov eller inte, använde vi användbarhetstest för att samla in kvantitativa data och kvalitativa kommentarer. För att testmiljön ska bli så verklig som möjligt implementerades en webbaserad interaktiv prototyp som stöds av D3.js biblioteket för testning. Vi analyserade användare upplevelsen och konsoliderade förbättringar. Som en fallstudie implementerade vi lösningen på Qlik Sense för att verifiera möjligheten att placera denna lösning i ett verkligt data visualiseringsverktyg. I allmänhet bildade resultatet av den här studien ett värdefullt initiativ för vidare utveckling och vi såg potentialerna i detta trädmodellsystem som kan användas på andra områden när det gäller till att granska historik.

In the age of Big data and exponential digitalization, data visualization is becoming a ubiquitous tool to understand trends, patterns and identify deviations that better help in decision making. The purpose of this thesis is to explore how a scalable data visualization interface can be designed with the open-source web library D3.js. The interface is designed to display a range of patients’ physiological measurements to help healthcare professionals with Covid-19 diagnosis. Several prerequisites were identified through a qualitative study, which proved to alleviate the implementation process, such as choosing a robust model that can support visualizations despite discontinuous and incomplete datasets. Since faulty visualizations may lead to potential harm in the highly sensitive medical setting, a dedicated risk analysis was deemed beneficial and thus formulated. The design of the interface also revealed functionality that could be considered when implementing any visualization interface, such as the rendering of different views and features that can further assist the user in interpreting the visualizations. / I en tid med Big Data och en exponentiellt växande digitalisering, blir datavisualisering ett mer förekommande verktyg för att förstå trender, mönster och identifiera avvikelser för att underlätta beslutsfattande. Syftet med studien är att utforska hur ett skalbart datavisualiseringsgränssnitt kan utformas med hjälp av det webbaserade biblioteket D3.js. Gränssnittet är utformat för att visa ett omfång av patienters fysiologiska mätvärden med syftet att hjälpa sjukvårdspersonal med diagnostiken av Covid-19. Flera förutsättningar kunde upptäckas med hjälp av en kvalitativ förstudie. Denna studie visade sig underlätta implementeringsprocessen, där bland annat en robust modell som stödjer visualiseringar trots diskontinuerliga och ofullständiga dataserier identifierades. Eftersom felaktiga, eller delvis fungerande visualiseringar kan leda till potentiell skada i den mycket känsliga medicinska miljön, ansågs en riskanalys vara fördelaktig. Därför utformades en sådan analys, som dessutom visade sig sedan kunna vara användbar i flera sammanhang. Gränssnittets design visade också på gemensam funktionalitet som kan övervägas vid implementeringen av andra visualiseringsgränssnitt, bland annat hur vyer renderas men även funktioner som vägleder användaren till att lättare kunna tolka de olika visualiseringarna.

Medical health providers use electronic health records (EHRs) to store information about patient treatment to support patient care management and securely share health information among healthcare organizations. EHRs have also been used in healthcare research in problems such as patient phenotyping, health risk prediction, and medical entity extraction. In this thesis, we focus on several important issues: (1) how to convert natural text from medical notes to vector representations suitable for deep learning algorithms, (2) how to help healthcare researchers select a patient cohort from EHRs, and (3) how to use EHRs to identify patient diagnoses and treatments. In the first part of the thesis, we present a new method for learning vector representations of medical terms. Learning vector representations of words is an important pre-processing step in many natural language processing applications. For example, EHRs contain clinical notes that describe patient health conditions and course of treatment in a narrative style. The notes contain specialized medical terminology and many abbreviations. Learning good vector representations of specialized medical terms can improve the quality of downstream data analysis tasks on EHR data. However, the traditional approaches struggle to learn vector representations of rarely used medical terms. To overcome this problem, we developed a neural network-based approach, called definition2vec, that uses external knowledge contained in medical vocabularies. We performed quantitative and qualitative analysis to measure the usefulness of the learned representations. The results demonstrate that definition2vec is superior to the state-of-the-art algorithms. In the second part of the thesis, we describe a new visual interface that helps healthcare researchers select patient cohorts from EHR data. Process of identifying patients of interest for observational studies from EHR data is known as cohort selection, a challenging research problem. We considered a problem of cohort selection from medical claim data, which requires identifying a set of medical codes for selection. However, there are tens of thousands of unique medical codes, and it becomes very difficult for any human to decide which codes identify patients of interest. To help users in defining a set of codes for cohort identification, we developed an interactive system, called Medical Claim Visualization system (MedCV), which visualizes medical code representations. MedCV analyzes a medical claim database and allows users to reason about medical code relationships and define inclusion rules for the selection by visualizing medical codes, claims, and patient timelines. Evaluation of our system through a user study indicates that MedCV enables domain experts to define inclusion rules efficiently and with high quality. The third part of the thesis is a study of the definition of acute kidney injury (AKI), which is a condition where kidneys suddenly cannot filter waste from the blood. AKI is a major cause of patient death in intensive care units (ICU) and it is critical to detect it early. Recently published KDIGO medical guideline proposed a clinical definition of AKI using blood serum creatinine and urine output. The KDIGO definition was developed based on the expert knowledge, but very little is known about how well it matches the medical practice. In this study, we investigated publicly available EHR data from 47,499 ICU admissions to determine the concordance between the KDIGO definition and AKI determination by the medical provider. We show that it is possible to find a formula using machine learning with much higher concordance with the medical provider AKI coding than KDIGO and discuss the medical relevance of this finding. / Computer and Information Science

Cellular network deployment scenarios refer to how cellular networks are implementedand deployed by network operators to provide wireless connectivity to end users.These scenarios can vary based on capacity requirements, type of geographical area, populationdensity, and specific use cases. Radio Access Networks of different generations,such as 4G and 5G, may also have different deployments. Network deployment scenarioscover many aspects, but two major components are Configuration settings and PerformanceMeasures which refer to the network nodes, hardware build-up and softwaresettings, and the end user behavior and connectivity experience in the area covered by thewireless network.In this master thesis, the aim is to understand how different area types - such as Rural,Suburban, and Urban – affect the cellular network deployment in such areas. A novelframework was developed to label each node (base station) with the area type it is associatedwith. The framework utilizes spatial analytics on the dataset provided by Ericsson forthe LTE nodes working with 4G technology in combination with open-source libraries anddatasets such as GeoPy and H3 Kontur population dataset respectively, to create area typelabels. The area types are labeled based on the calculated population density served byeach node and are considered true labels based on manual sanity checks performed. A supervisedmachine learning model was used to predict the nodes based on the CM and PMdata to understand the strength of the relationship between the features and true labels.This thesis also includes analysis and insights about characteristic deployment scenariosunder different area types. The main goal of this master thesis is to utilize machinelearning to uncover the characteristic features of a variety of node groups inherent in atelecom network, which, in the long run, contributes to better service operation and optimizationof existing cellular infrastructure. Nodes (base station) are labeled in the datato be able to distinguish their associated area-type. In addition to this clustering is performedto uncover the inherent characteristic behavior groups in the data and comparethem against the output from the classification model. Lastly, the investigation was doneon the potential impact of node placements such as indoor or outdoor, on the correspondingfeatures.In conclusion, the study’s results showed us that a correlation exists between deploymentscenarios and the different areas. There are a few prevalent common denominatorsbetween the node groups such as Pathloss and NR Cell Relations that drive the classificationmodel to a better classification metric, F1 score. Clustering of CM and PM data uncoversinherent patterns in different node groups under different area types and providesinformation about characteristic features of the groups such as CM data displaying twoconfiguration setting clusters, and PM data showing three different user behavior patterns.

The surging global trend in waste generation, currently surpassing 2 billion tons annually in cities,stands as a pressing concern amid escalating climate change threats. This master's thesis aims todesign a digital dashboard tailored to the needs of the Stockholm Municipality's waste managementsystem and its different stakeholders. While digitalization in the waste management sector remains inits early stages globally, this thesis presents an innovative approach to foster more efficient wastemanagement practices and advance sustainability goals through various data visualization techniques. To ensure the dashboard's effectiveness and relevance, this thesis revolves around two pivotalresearch questions. The first research question is: What are the needs and requirements of the wastemanagement sector in the Stockholm municipality for a digital waste dashboard? The second researchquestion is: How to visualize the performance of the waste management in the Stockholm municipalitythrough a digital dashboard? To answer these 2 questions, a research approach was employed. Sevenin-depth interviews were conducted with key stakeholders within the waste management sector inStockholm, revealing insights into their needs and requirements. These insights served as thefoundation for an initial prototype of the dashboard. This Initial prototype was followed by a designthinking workshop, held in collaboration with LocalLife, a startup working with climate digital solutionsfor local neighborhoods, to refine the initial prototype and generate new design ideas for thedashboard. The outcomes of this workshop were implemented on the initial prototype resulting in afinal set of interactive mockups of a waste management digital dashboard. The resulting digital dashboard design offers an interface with several data visualization capabilities.This empowers stakeholders, including waste management officials, city planners, and environmentalpolicymakers, to gain valuable insights into waste behavior. By analyzing real-time and historical data,these stakeholders can track progress, identify patterns, and anticipate future trends in wastegeneration, collection, and disposal. The dashboard facilitates informed decision-making by enablingusers to optimize resource allocation, implement targeted interventions, and develop evidence-basedwaste management strategies. This thesis demonstrates the capabilities of digitalization in wastemanagement and provides a tangible solution tailored to the unique challenges faced by the wastemanagement stakeholders in the Stockholm municipality. / Den växande globala trenden inom avfallsgenerering, som för närvarande överstiger 2 miljarder tonårligen i städer, utgör ett akut problem i ljuset av de eskalerande hoten mot klimatförändringarna.Dettaexamensarbete syftar till att designa en digital instrumentpanel skräddarsydd för behoven inomStockholms kommuns avfallshanteringssystem och dess olika intressenter. Även om digitaliseringeninom avfallshanteringsbranschen fortfarande befinner sig i ett tidigt skede globalt, presenterar dennaavhandling ett innovativt tillvägagångssätt för att främja effektivare avfallshanteringsmetoder ochfrämja hållbarhetsmål genom olika datavisualiseringstekniker. För att säkerställa instrumentpanelens effektivitet och relevans fokuserar denna avhandling på tvåcentrala forskningsfrågor. Den första forskningsfrågan är: Vilka är behoven och kraven iavfallshanteringssektorn i Stockholms kommun för en instrumentbräda för digitalt avfall? Den andraforskningsfrågan: Hur visualiserar du prestandan för avfallshantering i Stockholms kommun genom endigital instrumentbräda? För att besvara dessa två frågeställningar användes en forskningsansats. Sjudjupintervjuer genomfördes med nyckelaktörer inom avfallshanteringsbranschen i Stockholm, vilketgav insikter om deras behov och krav. Dessa insikter låg till grund för den första prototypen avinstrumentpanelen. Denna första prototyp följdes av en workshop om designtänkande, som hölls isamarbete med LocalLife, en startup som arbetar med digitala klimatlösningar för lokala stadsdelar, föratt förfina den första prototypen och generera nya designidéer för instrumentpanelen. Resultaten avdenna workshop implementerades på den ursprungliga prototypen, vilket resulterade i en slutliguppsättning interaktiva mockups av en digital instrumentpanel för avfallshantering. Den resulterande digitala instrumentpanelen erbjuder ett gränssnitt med flera datavisualiseringfunktioner. Detta gör det möjligt för intressenter, inklusive avfallshanteringstjänstemän, stadsplanerareoch miljöbeslutsfattare, att få värdefulla insikter om avfallsbeteende. Genom att analysera realtidsdataoch historiska data kan dessa intressenter spåra framsteg, identifiera mönster och förutse framtidatrender inom generering, insamling och bortskaffande av avfall. Instrumentpanelen underlättarvälgrundat beslutsfattande genom att göra det möjligt för användare att optimera resursfördelningen,genomföra riktade insatser och utveckla evidensbaserade strategier för avfallshantering. Dennaavhandling demonstrerar digitaliseringens möjligheter inom avfallshantering och ger en konkret lösningsom är skräddarsydd för de unika utmaningar som avfallshanteringsintressenterna i Stockholmskommun står inför.

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Data visualization is a field dedicated to effectively showing large amounts of collected data. A field where data visualization has shown promising results in its ways to effectively answer questions is the fundamental research of the universe. This thesis describes how to visualize the Earth’s magnetosphere as it interacts with the solar wind, using numerical methods, semi-transparent surfaces, and contours in OpenSpace. A magnetosphere module was implemented into OpenSpace, and the OpenSpace GUI was extended to give the user a possibility to interact with the visualization. The implemented algorithm in the magnetosphere module was measured in terms of speed, robustness, and user understanding. The implementation made it possible to visualize a simple model of the Earth’s magnetosphere, both when it interacts and when it does not interact with the solar wind. The measured speed showed a trend of a linear increase when more magnetic field lines were added to the visualization, where the run time was low for all tests. The algorithm was shown to be robust in its creation of the magnetosphere. When asked about what the users thought of the implemented visualization, a majority were positive and thought it to be a good complement to learning about the Earth’s magnetosphere. For a realistic model, there is a possibility that the speed and robustness may get worse, but the results are good for a simple model. To make the visualization itself more informative, more highlights are needed for important parts of the structure, as well as different colors that differ on which surfaces belong to which geographical pole. / Creative Exploration of the Atmosphere

This thesis project presents a research through design process, that has aimed to investigate and challenge internet users’ perception and awareness around the theme of online privacy and third-party trackers. This has been done by designing a critical design artifact called PrivacyLamp which takes form as a classic lamp, that through a secondary (dis)functionality is designed to work as an mediation of potential third-par- ty-trackers activity on the user’s local network. PrivacyLamp has been developed through an iterative design process, guid- ed by relevant literature and works within the eld of critical design, physical data visualization, and design for re ection, which all have worked as a foundation for the design of such an artefact. The prototype has been evaluated together with six participants, who all adopted the prototype into their domestic settings to experience it as a part of their everyday life for a few days. The aim of this qualitative study has been to investigate how a defamiliarized domestic object can work as an ambient display to question the invisible ow of connec- tivity and its complication within online privacy, as well as the narratives and experiences users develops in relation to this.

In this study, I took the quantitative research approach of film statistical stylistic analysis to examine the editing pattern evolution of 130 Hollywood movie trailers over the past 60 years from 1951 to 2015; the prior studies on the overall evolution of the Hollywood movies’ editing pattern are compared and discussed. The results suggest that although the movie trailers are much shorter than the whole movies, the average shot lengths of the trailers still display a declining trend over the past 60 years, and the variations in the shot lengths are also decreasing. Second, the motions within each framedo not change significantly over the years, while the correlation coefficients between the shot lengths and the motions within the shots are moving toward a more negative correlation relationship over time, suggesting that the trailers are subject to an editing evolution trend that the shorter the shot is, the more motions there are within it, and this also aligns with the overall movies’ editing pattern evolution trend. Last, the luminance of the trailers remains almost the same over time, which does not align with the overall movies’ editing pattern evolution of becoming darker and darker over decades. Together these findings suggest that the movie trailers’ editing rhythm evolution in general aligns with that of overall movies over time while the visual editing pattern evolution of color luminance does not. The study results will improve our understanding on how the Hollywood movie trailers’ editing pattern and style have evolved over time and pave the way for future advertising studies and cognitive psychology studies on the audience’s attention, immersion and emotional response to various editing patterns of movie trailers. / Media Studies & Production

Affiliates of the United States settlement house movement provided a historical precedent for engaged, community-centered museum practice. Their innovations upon the social survey, a key sociological data collection and data visualization tool, as well as their efforts to interpret results via innovative, culturally democratic exhibition techniques, had a contemporary impact on both museum practice and the history of social work. This impact resonates in the socially-responsive work of community museums of the recent past. The ethics of settlement methodology- including flexibility, experimentalism, empathetic practice, local community focus, and social justice activism- foreshadow the precepts and practices of what is now known as public history. / History