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Narrative visualization is a young and emerging field, driven mainly by data journalists. For this reason, most data stories available today are author-driven. However, with the rise of interactive visualizations the possibilities for creating reader-driven stories have become apparent. In this thesis, we present a straigthforward prototype, AsylKoll, built to support the articulation of reader-driven stories about Swedish immigration during 2015. We test its ability to support reader-driven stories by performing two user-studies based on the Think Aloud Method. In particular, we evaluate the prototype along the dimensions of reader engagement and learning. We find that user-centric data and various effects, such as transitions and mouse-overs, have a positive impact on reader engagement. In addition, we find that typical tasks such as extracting extremes and making comparisons are very important for users to gain insight and learn from the data. Foremost, this thesis shows the potential that simple, interactive visualizations have to make people engage and gain insights from data. / Att skapa faktabaserade narrativ med hjälp av datavisualiseringar är någonting som blir allt mer vanligt idag. Utvecklingen drivs framför allt av datajournalister och av den anledningen är det typiskt sett författardrivna historier som berättas. På senare tid har det dock blivit allt lättare att utveckla avancerade, interaktiva visualinsergar och det har öppnat för skapa faktabaserade narrativ drivna av läsaren istället. Läsardrivna narrativ är det som vi utforskar i den här studien. Med hjälp av en prototyp som vi byggt, AsylKoll, som visar statistik från asylinvandringen till Sverige under 2015, undersöker vi vad som krävs av en visualisering för att användare ska kunna härleda sina egna faktabaserade historier från den. Vi kollar i synnerhet på hur man kan uppmana användaren att interagera med verktyget samt vad som krävs för att användaren ska lära sig från datan. Verktyget testas genom två användarstudier med ’Think Aloud’-metoden. I studien finner vi att data centrerad kring användaren och olika typer av effekter, såsom transitioner och mouse-overs, påverkar användarens vilja att interagera med visualiseringarna positivt. Vidare finner vi även att typiska funktioner som exempelvis möjligheten att snabbt hitta extremvärden samt att kunna göra olika jämförelser av data, är viktigt för lärandet.

Eco-visualization technologies aim to trigger more environmental behaviors by providing feedback about the usage of key resources such as energy. However, the design of these technologies to encourage energy conservation has been mainly focused on individual behaviors in a household level. Addressing a different approach researchers at KTH have designed the housing cooperative app, a web application that provides feedback about the collective energy consumption of housing cooperatives in Stockholm, aiming to reduce the cooperative's collective energy use. By using a Research Through Design approach, this thesis explores how data visualization can support amateur energy work through the housing cooperative app. For this, I identified design problems in the data visualization elements of the app, which I aimed to solve by redesigning them; then, I conducted semi structured interviews with amateur energy workers, where they interacted with the application, to generate new insights about how data visualization can be used in an amateur work context. Through the interviews it was possible to obtain qualitative answers about the challenges of amateurs energy workers and the way data visualization could be used to address theses challenges and achieve their goals in an efficient way. The interviews was divided in Background, Amateur work, Comprehension and Usefulness of the data, and were supported by a walkthrough in the application presenting to users different scenarios and features in the application. Results showed the potential that data visualizations have to support amateur energy workers to overcome their main challenges and to identify the rewards of their work. In this thesis I discuss about this potential, and about design aspects that are important to consider when designing eco-visualization technologies in amateur energy context. / I detta examensarbete undersöktes hur datavisualisering kan stödja icke-professionellt energiarbete vid användning av appen utvecklad för bostadsrättsföreningar. För att göra detta användes metoden research through design. Under arbetet identifierade jag problem i appens tidigare design och förbättrade visualiseringselementen. Efter detta utfördes en intervjustudie av semistrukturerad form med icke-professionella energiarbetare som informanter. Under dessa intervjuer interagerade informanterna med appen i ett försök att finna nya insikter om hur datavisualisering kan användas i en icke-professionel kontext.   Intervjuerna var uppdelade i de tre kategorierna: bakgrund om informanten, icke-professionellt arbete samt förståelse och användbarhet av informationen i appen. Under intervjuerna utförde jag en demonstration av appen för att presentera de olika funktioner och scenarier jag ville utvärdera. Intervjuerna gav mig ett kvalitativt resultat med insikter om de hinder som upplevs av användargruppen, och hur datavisualisering kan användas för att åtgärda dessa.   Resultaten visade att datavisualisering har potentialen att hjälpa utövare av icke-professionellt energiarbete. Detta görs genom att underlätta deras uppgifter, samt en ökad förståelse för de positiva konsekvenser det för med sig. Slutligen diskuterar jag potentialen av ekovisualisering, samt de designaspekter jag anser viktiga för utveckling i en icke-professionell kontext relaterat till energi.

Explosive growth in data size, data complexity, and data rates, triggered by emergence of high-throughput technologies such as remote sensing, crowd-sourcing, social networks, or computational advertising, in recent years has led to an increasing availability of data sets of unprecedented scales, with billions of high-dimensional data examples stored on hundreds of terabytes of memory. In order to make use of this large-scale data and extract useful knowledge, researchers in machine learning and data mining communities are faced with numerous challenges, since the data mining and machine learning tools designed for standard desktop computers are not capable of addressing these problems due to memory and time constraints. As a result, there exists an evident need for development of novel, scalable algorithms for big data. In this thesis we address these important problems, and propose both supervised and unsupervised tools for handling large-scale data. First, we consider unsupervised approach to big data analysis, and explore scalable, efficient visualization method that allows fast knowledge extraction. Next, we consider supervised learning setting and propose algorithms for fast training of accurate classification models on large data sets, capable of learning state-of-the-art classifiers on data sets with millions of examples and features within minutes. Data visualization have been used for hundreds of years in scientific research, as it allows humans to easily get a better insight into complex data they are studying. Despite its long history, there is a clear need for further development of visualization methods when working with large-scale, high-dimensional data, where commonly used visualization tools are either too simplistic to gain a deeper insight into the data properties, or are too cumbersome or computationally costly. We present a novel method for data ordering and visualization. By combining efficient clustering using k-means algorithm and near-optimal ordering of found clusters using state-of-the-art TSP-solver, we obtain efficient algorithm that achieves performance better than existing, computationally intensive methods. In addition, we present visualization method for smaller-scale problems based on object matching. The experiments show that the methods allow for fast detection of hidden patterns, even by users without expertise in the areas of data mining and machine learning. Supervised learning is another important task, often intractable in many modern applications due to time and memory constraints, considering prohibitively large scales of the data sets. To address this issue, we first consider Multi-hyperplane Machine (MM) classification model, and propose online Adaptive MM algorithm which represents a trade-off between linear and kernel Support Vector Machines (SVMs), as it trains MMs in linear time on limited memory while achieving competitive accuracies on large-scale non-linear problems. Moreover, we present a C++ toolbox for developing scalable classification models, which provides an Application Programming Interface (API) for training of large-scale classifiers, as well as highly-optimized implementations of several state-of-the-art SVM approximators. Lastly, we consider parallelization and distributed learning approaches to large-scale supervised learning, and propose AROW-MapReduce, a distributed learning algorithm for confidence-weighted models using MapReduce framework. Experimental evaluation of the proposed methods shows state-of-the-art performance on a number of synthetic and real-world data sets, further paving a way for efficient and effective knowledge extraction from big data problems. / Computer and Information Science

Heart disease is a leading cause of death in the United States, and older adults are at highest risk of being diagnosed with heart disease. Consistent physical exercise is an effective means of deterring onset of heart disease, and physical activity tracking devices can inspire greater activity in older adults. However, physical activity tracking device abandonment is quite common due to limitations on what can be learned from the activity data that is collected. Better data visualization of physical data presents an opportunity to surpass these limitations. In this thesis, a task-based human subject study was performed with three different data visualizations to gain insight into how the format of physical activity data visualizations impact older adults’ abilities to infer meaning from physical activity data. Participants (n = 30) interacted with a prototype data visualization as well as two data visualizations from popular fitness tracking applications (Fitbit and Strava) and used these visualizations to complete 11 tasks. Results from these tasks show each visualization was able to facilitate users answer some task questions effectively, though no visualizations exhibited strong performance across all tasks. From the successes and shortcomings of each visualization, three key design recommendations for the design of data visualizations for physical activity data were made: 1) make exact values available, 2) summarize data at multiple timescales, and 3) ensure accessibility for the entire population of users.