The continuously advent of new technologies have made a rich and growing type of information sources available to analyses and investigation. In this context, multidimensional data analysis is considerably important when dealing with such large and complex datasets. Among the possibilities when analyzing such kind of data, applying visualization techniques can help the user find and understand patters, trends and establish new goals. Some applications examples of visualization of multidimensional data analysis goes from image classification, semantic word clouds, cluster analysis of document collection to exploration of multimedia content. This thesis presents several visualization methods to interactively explore multidimensional datasets aimed from specialized to casual users, by making use of both static and dynamic representations created by multidimensional projections. Firstly, we present a multidimen- sional projection technique which faithfully preserves distance and can handle any type of high-dimensional data, demonstrating applications scenarios in both multimedia and text docu- ments collections. Next, we address the task of interpreting projections in 2D, by calculating neighborhood errors. Hereafter, we present a set of interactive visualizations that aim to help users with these tasks by revealing the quality of a projection in 3D, applied in different high dimensional scenarios. In the final part, we address two different approaches to get insight into multimedia data, in special soccer sport videos. While the first make use of multidimensional projections, the second uses efficient visual metaphor to help non-specialist users in browsing and getting insights in soccer matches. / O advento contínuo de novas tecnologias tem criado um tipo rico e crescente de fontes de informação disponíveis para análise e investigação. Neste contexto, a análise de dados multidi- mensional é consideravelmente importante quando se lida com grandes e complexos conjuntos de dados. Dentre as possibilidades ao analisar esses tipos de dados, a aplicação de técnicas de visualização pode auxiliar o usuário a encontrar e entender os padrões, tendências e estabelecer novas metas. Alguns exemplos de aplicações de visualização de análise de dados multidimen- sionais vão de classificação de imagens, nuvens semântica de palavras, e análise de grupos de coleção de documentos, à exploração de conteúdo multimídia. Esta tese apresenta vários métodos de visualização para explorar de forma interativa conjuntos de dados multidimensionais que visam de usuários especializados aos casuais, fazendo uso de ambas representações estáticas e dinâmicas criadas por projeções multidimensionais. Primeiramente, apresentamos uma técnica de projeção multidimensional que preserva fielmente distância e que pode lidar com qualquer tipo de dados com alta-dimensionalidade, demonstrando cenários de aplicações em ambos os casos de multimídia e coleções de documentos de texto. Em seguida, abordamos a tarefa de interpretar as projeções em 2D, calculando erros de vizinhança. Posteriormente, apresentamos um conjunto de visualizações interativas que visam ajudar os usuários com essas tarefas, revelando a qualidade de uma projeção em 3D, aplicadas em diferentes cenários de alta dimensionalidade. Na parte final, discutimos duas abordagens diferentes para obter percepções sobre dados multimídia, em particular vídeos de futebol. Enquanto a primeira abordagem utiliza projeções multidimensionais, a segunda faz uso de uma eficiente metáfora visual para auxiliar usuários não especialistas em navegar e obter conhecimento em partidas de futebol.

A crescente adoção do paradigma experimental na pesquisa em Engenharia de Software visa a obtenção de evidências experimentais sobre as tecnologias propostas para garantir sua correta avaliação e para a construção de um corpo de conhecimento sólido da disciplina. Uma das abordagens de pesquisa experimental é a revisão sistemática, um método rigoroso, planejado e auditável para a realização da coleta e análise crítica de dados experimentais disponíveis sobre um determinado tema de pesquisa. Apesar de produzir resultados confiáveis, a condução de uma revisão sistemática pode ser trabalhosa e muitas vezes demorada, principalmente quando existe um grande volume de estudos a serem considerados, selecionados e avaliados. Uma solução encontrada na literatura é a utilização de ferramentas de Mineração Visual de Textos (VTM) como a Projection Explorer (PEx) para apoiar a fase de seleção e análise de estudos primários no processo de revisão sistemática. Neste trabalho foi realizada uma reengenharia de software na ferramenta PEx com dois objetivos principais: apoiar, utilizando VTM, a fase de seleção e análise de estudos primários no processo de revisão sistemática e implementar novos requisitos não-funcionais relativos à melhoria da manutenibilidade e escalabilidade da ferramenta. Como resultado foi construída uma plataforma modular para a instanciação de ferramentas de visualização e, a partir da mesma, uma ferramenta de revisão sistemática apoiada por VTM. Os resultados de um estudo de caso executado com a ferramenta mostraram que a abordagem de aplicação de técnicas VTM usada nesse contexto é viável e promissora, melhorando tanto a performance quanto a efetividade da seleção / The increasing adoption of the experimental paradigm in Software Engineering research aims at obtaining experimental evidence of the proposed technologies to ensure their proper evaluation and to build a solid body of knowledge for the discipline. One approach of experimental research is the systematic review, a rigorous, auditable and planned method to carry out the collection and analysis of experimental data available on a particular research topic. Despite producing reliable results, conducting a systematic review can be a cumbersome and often lengthy process, especially when a large volume of studies is to be considered, selected and evaluated. One solution found in the literature is the use of Visual Text Mining (VTM) tools such as the Projection Explorer (PEx) to support the selection and analysis of primary studies in the systematic review process. In this work a software re-engineering was performed on PEx with two main goals: to support, using VTM, the stage of selection and analysis of primary studies in the systematic review process and to implement new non-functional requirements related to improving the maintainability and scalability of the tool. The results were the building of a modular platform for instantiating visualization tools and, from it, the instantiation of a systematic review tool supported by VTM. The results of a case study carried out with the tool showed that the VTM approach used in this context is feasible and promising, improving both performance and the effectiveness of selection

Les séries temporelles sont l'un des types de données les plus courants dans divers domaines scientifiques, industriels et financiers. Selon le contexte, l'analyse des séries temporelles est effectuée à diverses fins: prévision, estimation, classification et détection des tendances et des événements. Grâce aux capacités exceptionnelles de la perception visuelle humaine, la visualisation reste l'un des outils les plus puissants pour l'analyse de données, en particulier pour les données temporelles. Avec la croissance de volume et de la complexité des jeux de données, de nouvelles techniques de visualisation sont clairement nécessaires pour améliorer l'analyse des données. Elles visent à faciliter l'analyse visuelle dans le cas où des situations ou des tâches sont bien spécifiées, ou à favoriser l'analyse exploratoire non guidée.La visualisation est basée sur le "mapping visuel", un processus qui consiste à associer les valeurs de données aux canaux visuels comme la position, la taille et la couleur des éléments graphiques. A cet égard, la forme la plus connue de visualisation des séries temporelles, c'est-à-dire les graphiques linéaires ("line charts" en anglais), consiste en une mise en correspondance des valeurs de données avec la position verticale de la ligne. Cependant, un seul mapping visuel ne convient pas à toutes les situations et objectifs analytiques.Notre but est d'introduire des alternatives au mapping visuel conventionnel et de trouver des situations dans lesquelles, la nouvelle approche compense la simplicité et la familiarité des techniques existantes. Nous présentons une revue de l'état de l'art sur la visualisation des séries chronologiques, puis nous nous concentrons sur les approches existantes du mapping visuel.Ensuite, nous présentons nos contributions. Notre première contribution est une étude systématique d'un «mapping visuelle composite» qui consiste à utiliser des combinaisons de canaux visuels pour communiquer différentes facettes d'une série temporelle. Au moyen de plusieurs expériences avec des utilisateurs, nous comparons les nouveaux mappings visuels à une technique de référence existante et nous mesurons la vitesse et la précision des utilisateurs dans différentes tâches analytiques. Nos résultats montrent que les nouvelles conceptions visuelles conduisent à des performances analytiques proches de celles des techniques existantes sans être inutilement complexes ou nécessiter un entraînement. De plus, certains mappings proposés surpassent les techniques existantes dans les situations de contraintes spatiales. L'efficacité spatiale est d'une grande importance pour la visualisation simultanée de grands volumes de données ou de visualisation sur de petits écrans. Les deux scénarios font partie des défis actuels de la visualisation de l'information. / Time series are one of the most common types of recorded data in various scientific, industrial, and financial domains. Depending on the context, time series analysis are used for a variety of purposes: forecasting, estimation, classification, and trend and event detection. Thanks to the outstanding capabilities of human visual perception, visualization remains one of the most powerful tools for data analysis, particularly for time series. With the increase in data sets' volume and complexity, new visualization techniques are clearly needed to improve data analysis. They aim to facilitate visual analysis in specified situations, tasks, or for unguided exploratory analysis.Visualization is based upon visual mapping, which consists in association of data values to visual channels, e.g. position, size, and color of the graphical elements. In this regard, the most familiar form of time series visualization, i.e. line charts, consists in a mapping of data values to the vertical position of the line. However, a single visual mapping is not suitable for all situations and analytical objectives.Our goal is to introduce alternatives to the conventional visual mapping and find situations in which, the new approach compensate for the simplicity and familiarity of the existing techniques. We present a review of the existing literature on time series visualization and then, we focus on the existing approaches to visual mapping.Next, we present our contributions. Our first contribution is a systematic study of a "composite" visual mapping which consists in using combinations of visual channels to communicate different facets of a time series. By means of several user studies, we compare our new visual mappings with an existing reference technique and we measure users' speed and accuracy in different analytical tasks. Our results show that the new visual designs lead to analytical performances close to those of the existing techniques without being unnecessarily complex or requiring training. Also, some of the proposed mappings outperform the existing techniques in space constraint situations. Space efficiency is of great importance to simultaneous visualization of large volumes of data or visualization on small screens. Both scenarios are among the current challenges in information visualization.

The metabolism constitutes the universe of biochemical reactions taking place in a cell of an organism. These processes include the synthesis, transformation, and degradation of molecules for an organism to grow, to reproduce and to interact with its environment. A good way to capture the complexity of these processes is the representation as metabolic network, in which sets of molecules are transformed into products by a chemical reaction, and the products are being processed further. The underlying graph model allows a structural analysis of this network using established graphtheoretical algorithms on the one hand, and a visual representation by applying layout algorithms combined with information visualization techniques on the other. In this thesis we will take a look at three different aspects of graph visualization within the context of biochemical systems: the representation and interactive exploration of static networks, the visual analysis of dynamic networks, and the comparison of two network graphs. We will demonstrate, how established infovis techniques can be combined with new algorithms and applied to specific problems in the area of metabolic network visualization. We reconstruct the metabolic network covering the complete set of chemical reactions present in a generalized eucaryotic cell from real world data available from a popular metabolic pathway data base and present a suitable data structure. As the constructed network is very large, it is not feasible for the display as a whole. Instead, we introduce a technique to analyse this static network in a top-down approach starting with an overview and displaying detailed reaction networks on demand. This exploration method is also applied to compare metabolic networks in different species and from different resources. As for the analysis of dynamic networks, we present a framework to capture changes in the connectivity as well as changes in the attributes associated with the network’s elements.

Intermittent water supply is a major challenge in informal settlements (slums) in Nairobi, Kenya. Smart water man- agement is an emerging technology that uses sensory net- works for continuous monitoring of water infrastructure to detect anomalies and inform effective distribution of wa- ter resources. This thesis investigates how visualization of data collected from the water network can support the lo- cal water utility in the Nairobi slums in improving water distribution, including maintenance and development. An explorative qualitative study, including interviews with util- ity staff members and field observations informed the devel- opment of three conceptual prototypes to propose how the collected data could be incorporated in the water utility’s practices. The design of the prototypes lead to a discussion on how smart water management can be adapted to the specific challenges of the context. The thesis concludes by suggesting that a successful implementation of smart water management, besides supporting efforts directly related to the slum areas, must also address the problems of inefficient and inequitable water allocation concerning the whole city of Nairobi. ­­ / Oregelbunden vattenförsörjning är en stor utmaning i informella bosättningar (slumområden), i Nairobi, Kenya. Smart water management är ett ny tekniskt område som använder sensornätverk för kontinuerlig övervakning av vatteninfrastrukturer för att upptäcka avvikelser, som t.ex. läckage, och informera effektiv distribution av vattenresurser. Denna studie undersöker hur visualisering av data som samlats från vattennätet kan stödja det lokala vattenbolaget i Nairobi i arbetet med att förbättra vattendistributionen, inklusive underhåll och utbyggnad. En explorativ kvalitativ förstudie baserad på intervjuer med anställda på vattenbolaget och fältobservationer användes som underlag för en designprocess av interaktiva datavisualiseringar. I designprocessen skapades tre konceptuella prototyper som exemplifierar hur den insamlade datan skulle kunna inkorporeras i vattenbolagets praxis. Utformningen av prototyperna ledde till en diskussion om hur smart water management kan anpassas till de särskilda förutsättningarna i Nairobis informella bosättningar.  En lyckad implementation av smart water management måste, förutom att stödja de insatser direkt relaterade till de informella bosättningarna, också adressera problematiken gällande ineffektiv och ojämlik fördelningen av vattenresurser i hela staden Nairobi.

As datasets in general grow in size and complexity over time while the human cognitive ability to interpret said datasets essentially stays the same, it becomes important to enable intuitive visualization methods for analysis. Based on previous research in the field of information visualization and visual analytics, a dashboard visualization prototype handling real-time event based traffic was implemented and evaluated. The real-time data is collected by a script and sent to a self-implemented web server that opens up a websocket connection with the dashboard client where the data is then visualized. Said data consisted of transactions and related metadata of an ecommerce retail site applied to a real customer scenario. The dashboard was developed using an agile method, continuously involving the thesis supervisor in the design and functionality process. The final design also depended on the results of an interview with a representative from one of the two target groups. The two target groups consisted of 5 novice and 5 expert users to the field of information visualization and visual analytics. The intuitiveness of the dashboard visualization prototype was evaluated by conducting two user studies, one for each target group, where the test subjects were asked to interact with the dashboard visualization, answer some questions and lastly solving a predefined set of tasks. The time spent solving said tasks, the amount of serious misinterpretations and the number of wrong answers was recorded and evaluated. The results from the user study showed that the use of colors, icons, level on animation, the choice of visualization method and level of interaction were the most important aspects for carrying out an efficient analytical process according to the test subjects. The test subjects desired to zoom in on each component, to filter the contents of the dashboard and to get additional information about the components on-demand. The most important result produced from developing the dashboard was how to handle the scalability of the application. It is highly important that the websocket connection remain stable when scaling out to handle more concurrent HTTP requests. The research also conclude that the dashboard should handle visualization methods that are intuitive for all users, that the real-time data needs to be put into relation to historical data if one wishes to carry out a valid analytical process and that real-time data can be used to discover trends and patterns in an early-as-possible stage. Lastly, the research provides a set of guidelines for scalability, modularity, intuitiveness and relations between datasets.

This work presents an interactive visualization tool for video content searching for lecture videos in a flipped classroom course Visualization. The search is performed using the video caption with a client-site search engine Lunr. The tool aims to improve accessibility to course video materials and enhance learning engagement, especially when students re-watch the videos. The work focus on using interactive visualization method to help users understand the results and seek video chapters of interest. We evaluate the tool with a survey, usability tests using think-aloud protocols, and semi-structured interviews. The results show that overall students find the tool easy to learn and are willing to use it in Visualization and other future courses. The evaluation transcripts are categorized and interpreted into themes with help of content analysis. This work also presents implications in design and implementation for future projects. / Division of Computational Science and Technology, KTH Royal Institute of Technology Detta arbete presenteras ett interaktivt visualiseringsverktyg för sökning av videoinnehåll för föreläsningsvideor i en flippade klassrummet-kurs Visualisering. Sökningen utförs med hjälp av videobildtexten med en sökmotor Lunr på klientsidan. Verktyget syftar till att förbättra tillgängligheten till kursens videomaterial och öka engagemanget i inlärningen, särskilt när eleverna ser videorna på nytt. Arbetet fokuserar på att använda en interaktiv visualiseringsmetod för att hjälpa användarna att förstå resultaten och söka videoklipp av intresse. Vi utvärderar verktyget med hjälp av en enkät, användbarhetstester med hjälp av Tänk–högt–metoden och semistrukturerade intervjuer. Resultaten visar att studenterna överlag tycker att verktyget är lätt att lära sig och är villiga att använda det i visualisering och andra framtida kurser. Utvärderingsutskrifterna kategoriseras och tolkas i teman med hjälp av innehållsanalys. Detta arbete presenteras också implikationer i utformning och genomförande för framtida projekt.

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.

Computational creativity is an active area of research within the artificial intelligence domain that investigates what aspects of computing can be considered as an analogue to the human creative process. Computers can be programmed to emulate the type of things that the human mind can. Artificial creativity is worthy of study for two reasons. Firstly, it can help in understanding human creativity and secondly it can help with the design of computer programs that appear to be creative. Although the implementation of creativity in computer algorithms is an active field, much of the research fails to specify which of the known theories of creativity it is aligning with. The combination of computational creativity with computer generated visualisations has the potential to produce visualisations that are context sensitive with respect to the data and could solve some of the current automation problems that computers experience. In addition theories of creativity could theoretically compute unusual data combinations, or introducing graphical elements that draw attention to the patterns in the data. More could be learned about the creativity involved as humans go about the task of generating a visualisation. The purpose of this dissertation was to develop a computer program that can automate the generation of a visualisation, for a suitably chosen visualisation type over a small domain of knowledge, using a subset of the computational creativity criteria, in order to try and explore the effects of the introduction of conceptual blending techniques. The problem is that existing computer programs that generate visualisations are lacking the creativity, intuition, background information, and visual perception that enable a human to decide what aspects of the visualisation will expose patterns that are useful to the consumer of the visualisation. The main research question that guided this dissertation was, “How can criteria derived from theories of creativity be used in the generation of visualisations?”. In order to answer this question an analysis was done to determine which creativity theories and artificial intelligence techniques could potentially be used to implement the theories in the context of those relevant to computer generated visualisations. Measurable attributes and criteria that were sufficient for an algorithm that claims to model creativity were explored. The parts of the visualisation pipeline were identified and the aspects of visualisation generation that humans are better at than computers was explored. Themes that emerged in both the computational creativity and the visualisation literature were highlighted. Finally a prototype was built that started to investigate the use of computational creativity methods in the ‘variable choice’, and ‘aesthetics’ stages of the data visualisation pipeline. / School of Computing / M. Sc. (Computing)

Indiana University-Purdue University Indianapolis (IUPUI) / According to the US Centers for Disease Control and Prevention, less than half of all adults in the US meet basic physical activity guidelines. Physical activity can help not just improve physical and mental health but also reduce the risk of heart disease and some cancers. Researchers and companies have tried to investigate the use of modern technologies to motivate people to increase and maintain physical activities. However, in spite of these efforts, there are criticisms. Those include low dietary effectiveness of the tools, lack of sustainable effects in the long-term, and proof of effectiveness only shown in laboratory settings. To overcome these limitations, first, the author developed a framework of overarching motivation theories and HCI factors and contextualized it within the running domain. Second, the author has developed a mobile application called HamkeRun within this framework, using the concepts of information visualization, gamification, and social grouping to increase a user’s motivation to run more frequently. Third, the HamkeRun application was empirically tested through a two-month-long longitudinal experiment and follow-up interviews. The results showed that the single runner type showed significant increases in the levels of their external motivation (motivational effect of the HamkeRun application), internal motivation and satisfaction, while the team runner type showed significant increases only in internal motivation. In addition, motivational effects were also different depending on the runners’ behavior change stage. Runners at the maintenance stage showed significant increases in external motivation, internal motivation, satisfaction, and total number of running activities performed during the study. Although action stage runners showed significant increase in internal motivation, female runners at the action stage showed significant decrease in their external motivation. Gamification greatly influenced increases of external motivation, internal motivation and total number of actual activities. Although both male and female runners showed increased internal motivation, significant increase in external motivation was only found in male runners. The dissertation closes with a series of design guidelines for application developers and designers which may help develop motivational tools in other health-related domains.