With the increasing pace of development, the process of interpreting software test results data has become more challenging and time-consuming. While the test results provide valuable insights into the software product, the increasing complexity of software systems and the growing volume of test data pose challenges in effectively analyzing this data to ensure quality. To address these challenges, organizations are adopting various tools. Visualization dashboards are a common approach used to streamline the analysis process. By aggregating and visualizing test results data, these dashboards enable easier identification of patterns and trends, facilitating informed decision-making. This study proposes a management dashboard with visualizations of test results data as a decision support system. A case study was conducted involving eleven quality assurance experts with a number of various roles, including managers, directors, testers, and project managers. User interviews were conducted to evaluate the need for a dashboard and identify relevant test results data to visualize. The participants expressed the need for a dashboard, which would benefit both newcomers and experienced employees. A low-fidelity prototype of the dashboard was created and A/B testing was performed through a survey to prioritize features and choose the preferred version of the prototype. The results of the user interviews highlighted pass-rate, executed test cases, and failed test cases as the most important features. However, different professions showed interest in different test result metrics, leading to the creation of multiple views in the prototype to accommodate varying needs. A high-fidelity prototype was implemented based on feedback and underwent user testing, leading to iterative improvements. Despite the numerous advantages of a dashboard, integrating it into an organization can pose challenges due to variations in testing processes and guidelines across companies and teams. Hence, the dashboards require customization. The main contribution of this study is twofold. Firstly, it provides recommendations for relevant test result metrics and suitable visualizations to effectively communicate test results. Secondly, it offers insights into the visualization preferences of different professions within a quality assurance team that were missing in previous studies. / Med den ökande utvecklingstakten har processen att tolka testresultatdata för programvara blivit mer utmanande och tidskrävande. Även om testresultaten ger värdefulla insikter i mjukvaruprodukten, innebär den ökande komplexiteten hos mjukvarusystemen och den växande volymen testdata utmaningar när det gäller att effektivt analysera dessa data för att säkerställa kvalitet. För att möta dessa utmaningar använder organisationer olika verktyg. Visualiseringspaneler är ett vanligt tillvägagångssätt som används för att effektivisera analysprocessen. Genom att aggregera och visualisera testresultatdata möjliggör dessa instrumentpaneler enklare identifiering av mönster och trender, vilket underlättar välgrundat beslutsfattande. Den här studien föreslår en management-panel med visualiseringar av testresultatdata som ett beslutsstödssystem. En fallstudie genomfördes med elva experter inom kvalitetssäkring med olika roller, inklusive chefer, direktörer, testare och projektledare. Användarintervjuer genomfördes för att utvärdera behovet av en panel och identifiera relevanta testresultatdata att visualisera. Deltagarna uttryckte behovet av en visualiseringspanel, som skulle gynna både nyanställda och erfarna medarbetare. En prototyp av panelen med låg detaljnivå skapades och A/B-testning genomfördes genom en enkät för att prioritera funktioner och välja den föredragna versionen av prototypen. Resultaten av användarintervjuerna lyfte fram andel av godkända testresultat, exekverade testfall och misslyckade testfall som de viktigaste egenskaperna. Men olika yrkesgrupper visade intresse för olika testresultatmått, vilket ledde till skapandet av flera vyer i prototypen för att tillgodose olika behov. En prototyp med hög detaljnivå implementerades baserat på feedback och genomgick användartestning, vilket ledde till iterativa förbättringar. Trots de många fördelarna med en instrumentpanel kan det innebära utmaningar att integrera den i en organisation på grund av variationer i testprocesser och riktlinjer mellan företag och team. Därför kräver paneler anpassning. Det huvudsakliga bidraget från denna studie är dubbelt. För det första ger den rekommendationer för relevanta testresultatmått och lämpliga visualiseringar för att effektivt kommunicera testresultat. För det andra ger den insikter i visualiseringspreferenser för olika yrken inom ett kvalitetssäkringsteam vilket saknats i tidigare studier.

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Diese Arbeit untersucht, inwiefern Visual Analytics die Analyse von Prozessen in geowissenschaftlichen raum-zeitlichen Daten unterstützen kann. Hierzu wurden drei neuartige Visual Analytics Ansätze entwickelt. Jeder Ansatz addressiert eine wichtige Analyseperspektive. Der erste Ansatz erlaubt es, wichtige räumliche Zustände in den Daten sowie deren auftreten in der Zeit zu untersuchen. Mittels hierarchischem Clustering werden alle in den Daten enthaltenen räumlichen Zustände in einer Clusterhierarchie verortet. Interaktive visuelle Analyse ermöglicht es, verschiedene räumliche Zustände aus den Daten zu extrahieren und die dazugehörigen raum-zeitlichen Muster zu interpretieren und zu bewerten. Der zweite Ansatz unterstützt die systematische Analyse des in den Daten zu beobachtenden zeitlichen Verhaltens sowie dessen Auftreten im geographischen Raum mittels einer Kombination aus Cluster Ensembles und interaktiver visueller Exploration. Der dritte Ansatz gestattet die Detektion und Analyse von zeitlichen Zusammenhängen in den Daten. Hierzu wurde eine etablierte Methode zur Analyse von zeitlichen Zusammenhängen zwischen zwei einzelnen Zeitreihen, gefensterte Kreuzkorrelation, durch Visual Analytics auf den Vergleich von Zeitreihenensembles erweitert. Dadurch ist es nicht nur möglich, Zusammenhänge zwischen Zeitreihen zu untersuchen, sondern auch Unsicherheiten in den Daten zu berücksichtigen. Alle Ansätze wurden anhand einer nutzer- und aufgabenorientierten Methodik entwickelt und erfolgreich in Anwendungsfällen aus der Erdsystem-Modellierung, der Ozeanmodellierung, der Paläoklimatologie und sogar den Kognitionswissenschaften eingesetzt. Diese Dissertation zeigt, dass Visual Analytics einen wertvollen Ansatz zur Analyse von Prozess-bezogenen Mustern in raum-zeitlichen Daten darstellt. Es kann die Grenzen existierender Analysemethoden erweitern und ermöglicht Geowissenschaftlern neue, aufschlussreiche Sichtweisen auf Daten und die darin beschriebenen Prozesse. / This thesis studied how visual analytics can facilitate the analysis of processes in geoscientific spatiotemporal data. Three novel visual analytics solutions were developed, each addressing an important analysis perspective. The first solution addresses the analysis of prominent spatial situations in the data and their occurrence over time. Hierarchical clustering is used to arrange all spatial situations in the data in a hierarchy of clusters. The combination with interactive visual analysis enables geoscientists to explore and alter the resulting hierarchy, to extract different sets of representative spatial situations, and to interpret and assess the corresponding spatiotemporal patterns. The second solution supports geoscientists in the analysis of prominent types of temporal behavior and their location in geographic space. Cluster ensembles are integrated with interactive visual exploration to enable users to systematically detect and interpret various types of temporal behavior in different data sets and to use this information for assessment of simulation model output. The third solution enables geoscientists to detect and analyze interrelations of temporal behavior in the data. Windowed cross-correlation, a technique for comparison of two individual time series, was extended to the comparison of entire ensembles of time series through visual analytics. This not only allows scientists to study interrelations, but also to assess how much these interrelations vary between two ensembles. All visual analytics solutions were developed following a rigorous user- and task-centered methodology and successfully applied to use cases in Earth system modeling, ocean modeling, paleoclimatology, and even cognitive science. The results of this thesis demonstrate that visual analytics successfully addresses important analysis perspectives and that it is a valuable approach to the analysis of process-related patterns in geoscientific spatiotemporal data.

La Science des réseaux est apparue comme un domaine d'étude fondamental pour modéliser un grand nombre de systèmes synthétiques ou du monde réel.La découverte du graphe petit monde et du graphe sans échelle dans ces réseaux a révolutionné la façon d'étudier, d'analyser, de modéliser et de traiter ces réseaux. Dans cette thèse, nous nous intéressons à l'étude des réseaux ayant ces propriétés et souvent qualifiés de réseaux complexes.A notre avis, les recherches menées dans ce domaine peuvent être regroupées en quatre catégories: l'analyse, la structure, le processus/organisation et la visualisation.Nous abordons des problèmes relatifs à chacune de ces catégories tout au long de cette thèse. (...) / Network science has emerged as a fundamental field of study to model many physicaland real world systems around us. The discovery of small world and scale free propertiesof these real world networks has revolutionized the way we study, analyze, model andprocess these networks. In this thesis, we are interested in the study of networks havingthese properties often termed as complex networks. In our opinion, research conducted inthis field can be grouped into four categories, Analysis, Structure, Processes-Organizationand Visualization. We address problems pertaining to each of these categories throughoutthis thesis. (...)

We propose the concept of spatial analytic interfaces (SAIs) as a tool for performing in-situ, everyday analytic tasks. Mobile computing is now ubiquitous and provides access to information at nearly any time or place. However, current mobile interfaces do not easily enable the type of sophisticated analytic tasks that are now well-supported by desktop computers. Conversely, desktop computers, with large available screen space to view multiple data visualizations, are not always available at the ideal time and place for a particular task. Spatial user interfaces, leveraging state-of-the-art miniature and wearable technologies, can potentially provide intuitive computer interfaces to deal with the complexity needed to support everyday analytic tasks. These interfaces can be implemented with versatile form factors that provide mobility for doing such taskwork in-situ, that is, at the ideal time and place. We explore the design of spatial analytic interfaces for in-situ analytic tasks, that leverage the benefits of an upcoming generation of light-weight, see-through, head-worn displays. We propose how such a platform can meet the five primary design requirements for personal visual analytics: mobility, integration, interpretation, multiple views and interactivity. We begin with a design framework for spatial analytic interfaces based on a survey of existing designs of spatial user interfaces. We then explore how to best meet these requirements through a series of design concepts, user studies and prototype implementations. Our result is a holistic exploration of the spatial analytic concept on a head-worn display platform. / October 2016

Die vorliegende Masterarbeit behandelt den Entwurf und die exemplarische Umsetzung eines Arbeitsablaufs zur Aufbereitung von georeferenziertem Microblogging Content. Als beispielhafte Datenquelle wurde Twitter herangezogen. Darauf basierend, wurden Überlegungen angestellt, welche Arbeitsschritte nötig und mit welchen Mitteln sie am besten realisiert werden können. Dabei zeigte sich, dass eine ganze Reihe von Bausteinen aus dem Bereich des Data Mining und des Text Mining für eine Pipeline bereits vorhanden sind und diese zum Teil nur noch mit den richtigen Einstellungen aneinandergereiht werden müssen. Zwar kann eine logische Reihenfolge definiert werden, aber weitere Anpassungen auf die Fragestellung und die verwendeten Daten können notwendig sein. Unterstützt wird dieser Prozess durch verschiedenen Visualisierungen mittels Histogrammen, Wortwolken und Kartendarstellungen. So kann neues Wissen entdeckt und nach und nach die Parametrisierung der Schritte gemäß den Prinzipien des Geovisual Analytics verfeinert werden. Für eine exemplarische Umsetzung wurde nach der Betrachtung verschiedener Softwareprodukte die für statistische Anwendungen optimierte Programmiersprache R ausgewählt. Abschließend wurden die Software mit Daten von Twitter und Flickr evaluiert. / This Master's Thesis deals with the conception and exemplary implementation of a workflow for georeferenced Microblogging Content. Data from Twitter is used as an example and as a starting point to think about how to build that workflow. In the field of Data Mining and Text Mining, there was found a whole range of useful software modules that already exist. Mostly, they only need to get lined up to a process pipeline using appropriate preferences. Although a logical order can be defined, further adjustments according to the research question and the data are required. The process is supported by different forms of visualizations such as histograms, tag clouds and maps. This way new knowledge can be discovered and the options for the preparation can be improved. This way of knowledge discovery is already known as Geovisual Analytics. After a review of multiple existing software tools, the programming language R is used to implement the workflow as this language is optimized for solving statistical problems. Finally, the workflow has been tested using data from Twitter and Flickr.

Ionospheric scintillation is a rapid variation in the amplitude and/or phase of radio signals traveling through the ionosphere. This spatial and time-varying phenomenon is of interest because its occurrence may affect the reception quality of satellite signals. Specialized receivers at strategic regions can track multiple variables related to the phenomenon, generating a database of historical observations on the regional behavior of ionospheric scintillation. The analysis of such data is very challenging, since it consists of time-varying measurements of many variables which are heterogeneous in nature and with possibly many missing values, recorded over extensive time periods. There is a need to introduce alternative intuitive strategies that contribute to experts acquiring further knowledge from the ionospheric scintillation data. Such challenges motivated a study on the applicability of visualization techniques to support tasks of identification of relevant attributes in the study of the behavior of phenomena described by multiple time-varying variables, of which the ionospheric scintillation is a good example. In particular, this thesis introduces a visual analytics framework, named TV-MV Analytics, that supports exploratory tasks on time-varying multivariate data and was developed following the requirements of experts on ionospheric scintillation from the Faculty of Science and Technology of UNESP at Presidente Prudente, Brazil. TV-MV Analytics provides an interactive visual exploration loop to analysts inspecting the behavior of multiple variables at different temporal scales, through temporal representations associated with clustering and multidimensional projection techniques. Analysts can also assess how different feature sub-spaces contribute to characterizing a certain behavior, where they may direct the analysis process and include their domain knowledge in the exploratory analysis. We also illustrate the application of TV-MV Analytics on multivariate time-varying data sets from three alternative application domains. Experimental results indicate the proposed solutions show good potential on assisting time-varying multivariate data mining tasks, since it reduces the effort required from experts to gain deeper insight into the historical behavior of the variables describing a phenomenon or domain. / A cintilação ionosférica é uma variação rápida na amplitude e/ou na fase dos sinais de rádio que viajam através da ionosfera. Este fenômeno espacial e variante no tempo é de grande interesse, pois pode afetar a qualidade de recepção dos sinais de satélite. Receptores especializados em regiões estratégicas podem rastrear múltiplas variáveis relacionadas ao fenômeno, gerando um banco de dados de observações históricas sobre o comportamento regional da cintilação. O estudo do comportamento da cintilação é desafiador, uma vez que requer a análise extensiva de dados multivariados e variantes no tempo, coletados por longos períodos. Medições são registradas continuamente, e são de natureza heterogênea, compreendendo múltiplas variáveis de diferentes categorias e possivelmente com muitos valores faltantes. Portanto, existe a necessidade de introduzir estratégias alternativas, eficientes e intuitivas, que contribuam para a adquisição de conhecimento, a partir dos dados, por especialistas que estudam a cintilação ionosférica. Tais desafios motivaram o estudo da aplicabilidade de técnicas de visualização para apoiar tarefas de identificação de atributos relevantes no estudo do comportamento de fenômenos ou domínios que envolvem múltiplas variáveis, como a cintilação. Em particular, esta tese introduz um arcabouço visual, o qual foi denominado TV-MV Analytics, que apoia tarefas de análise exploratória sobre dados multivariados e variáveis no tempo, inspirado em requisitos de especialistas no estudo da cintilação, vinculados à Faculdade de Ciências e Tecnologia da UNESP de Presidente Prudente, Brasil. O TV-MV Analytics fornece aos analistas um ciclo de interativo de exploração que apoia a inspeção do comportamento temporal de múltiplas variáveis, em diferentes escalas temporais, por meio de representações visuais temporais associadas a técnicas de agrupamento e de projeção multidimensional. Também permite avaliar como diferentes sub-espaços de atributos caracterizam um determinado comportamento, podendo direcionar o processo de análise e inserir seu conhecimento do domínio no processo de análise exploratória. As funcionalidades do TV-MV Analytics também são ilustradas em dados variantes no tempo oriundos de outros três domínios de aplicação. Os resultados experimentais indicaram que as soluções propostas têm bom potencial em tarefas de mineração de dados multivariados e variantes no tempo, uma vez que reduz o esforço e contribui para os especialistas obterem informações detalhadas sobre o comportamento histórico das variáveis que descrevem um determinado fenômeno ou domínio.

Signal processing is used in a wide variety of applications, ranging from digital image processing to biomedicine. Recently, some tools from signal processing have been extended to the context of graphs, allowing its use on irregular domains. Among others, the Fourier Transform and the Wavelet Transform have been adapted to such context. Graph signal processing (GSP) is a new field with many potential applications on data exploration. In this dissertation we show how tools from graph signal processing can be used for visual analysis. Specifically, we proposed a data filtering method, based on spectral graph filtering, that led to high quality visualizations which were attested qualitatively and quantitatively. On the other hand, we relied on the graph wavelet transform to enable the visual analysis of massive time-varying data revealing interesting phenomena and events. The proposed applications of GSP to visually analyze data are a first step towards incorporating the use of this theory into information visualization methods. Many possibilities from GSP can be explored by improving the understanding of static and time-varying phenomena that are yet to be uncovered. / O processamento de sinais é usado em uma ampla variedade de aplicações, desde o processamento digital de imagens até a biomedicina. Recentemente, algumas ferramentas do processamento de sinais foram estendidas ao contexto de grafos, permitindo seu uso em domínios irregulares. Entre outros, a Transformada de Fourier e a Transformada Wavelet foram adaptadas nesse contexto. O Processamento de Sinais em Grafos (PSG) é um novo campo com muitos aplicativos potenciais na exploração de dados. Nesta dissertação mostramos como ferramentas de processamento de sinal gráfico podem ser usadas para análise visual. Especificamente, o método de filtragem de dados porposto, baseado na filtragem de grafos espectrais, levou a visualizações de alta qualidade que foram atestadas qualitativa e quantitativamente. Por outro lado, usamos a transformada de wavelet em grafos para permitir a análise visual de dados massivos variantes no tempo, revelando fenômenos e eventos interessantes. As aplicações propostas do PSG para analisar visualmente os dados são um primeiro passo para incorporar o uso desta teoria nos métodos de visualização da informação. Muitas possibilidades do PSG podem ser exploradas melhorando a compreensão de fenômenos estáticos e variantes no tempo que ainda não foram descobertos.

Ionospheric scintillation is a rapid variation in the amplitude and/or phase of radio signals traveling through the ionosphere. This spatial and time-varying phenomenon is of interest because its occurrence may affect the reception quality of satellite signals. Specialized receivers at strategic regions can track multiple variables related to the phenomenon, generating a database of historical observations on the regional behavior of ionospheric scintillation. The analysis of such data is very challenging, since it consists of time-varying measurements of many variables which are heterogeneous in nature and with possibly many missing values, recorded over extensive time periods. There is a need to introduce alternative intuitive strategies that contribute to experts acquiring further knowledge from the ionospheric scintillation data. Such challenges motivated a study on the applicability of visualization techniques to support tasks of identification of relevant attributes in the study of the behavior of phenomena described by multiple time-varying variables, of which the ionospheric scintillation is a good example. In particular, this thesis introduces a visual analytics framework, named TV-MV Analytics, that supports exploratory tasks on time-varying multivariate data and was developed following the requirements of experts on ionospheric scintillation from the Faculty of Science and Technology of UNESP at Presidente Prudente, Brazil. TV-MV Analytics provides an interactive visual exploration loop to analysts inspecting the behavior of multiple variables at different temporal scales, through temporal representations associated with clustering and multidimensional projection techniques. Analysts can also assess how different feature sub-spaces contribute to characterizing a certain behavior, where they may direct the analysis process and include their domain knowledge in the exploratory analysis. We also illustrate the application of TV-MV Analytics on multivariate time-varying data sets from three alternative application domains. Experimental results indicate the proposed solutions show good potential on assisting time-varying multivariate data mining tasks, since it reduces the effort required from experts to gain deeper insight into the historical behavior of the variables describing a phenomenon or domain. / A cintilação ionosférica é uma variação rápida na amplitude e/ou na fase dos sinais de rádio que viajam através da ionosfera. Este fenômeno espacial e variante no tempo é de grande interesse, pois pode afetar a qualidade de recepção dos sinais de satélite. Receptores especializados em regiões estratégicas podem rastrear múltiplas variáveis relacionadas ao fenômeno, gerando um banco de dados de observações históricas sobre o comportamento regional da cintilação. O estudo do comportamento da cintilação é desafiador, uma vez que requer a análise extensiva de dados multivariados e variantes no tempo, coletados por longos períodos. Medições são registradas continuamente, e são de natureza heterogênea, compreendendo múltiplas variáveis de diferentes categorias e possivelmente com muitos valores faltantes. Portanto, existe a necessidade de introduzir estratégias alternativas, eficientes e intuitivas, que contribuam para a adquisição de conhecimento, a partir dos dados, por especialistas que estudam a cintilação ionosférica. Tais desafios motivaram o estudo da aplicabilidade de técnicas de visualização para apoiar tarefas de identificação de atributos relevantes no estudo do comportamento de fenômenos ou domínios que envolvem múltiplas variáveis, como a cintilação. Em particular, esta tese introduz um arcabouço visual, o qual foi denominado TV-MV Analytics, que apoia tarefas de análise exploratória sobre dados multivariados e variáveis no tempo, inspirado em requisitos de especialistas no estudo da cintilação, vinculados à Faculdade de Ciências e Tecnologia da UNESP de Presidente Prudente, Brasil. O TV-MV Analytics fornece aos analistas um ciclo de interativo de exploração que apoia a inspeção do comportamento temporal de múltiplas variáveis, em diferentes escalas temporais, por meio de representações visuais temporais associadas a técnicas de agrupamento e de projeção multidimensional. Também permite avaliar como diferentes sub-espaços de atributos caracterizam um determinado comportamento, podendo direcionar o processo de análise e inserir seu conhecimento do domínio no processo de análise exploratória. As funcionalidades do TV-MV Analytics também são ilustradas em dados variantes no tempo oriundos de outros três domínios de aplicação. Os resultados experimentais indicaram que as soluções propostas têm bom potencial em tarefas de mineração de dados multivariados e variantes no tempo, uma vez que reduz o esforço e contribui para os especialistas obterem informações detalhadas sobre o comportamento histórico das variáveis que descrevem um determinado fenômeno ou domínio.

<p>The Ethiopia Commodity Exchange (ECX), like many other data intensive organizations, is having difficulties making full use of the vast amounts of data that it collects. This MSc thesis identifies areas within the organization where concepts from the academic fields of information visualization and visual analytics can be applied to address this issue.Software solutions are designed and implemented in two areas with the purpose of evaluating the approach and to demonstrate to potential users, developers and managers what can be achieved using this method. A number of presentation methods are proposed for the ECX website, which previously contained no graphing functionality for market data, to make it easier for users to find trends, patterns and outliers in prices and trade volumes of commodieties traded at the exchange. A software application is also developed to support the ECX market surveillance team by drastically improving its capabilities of investigating complex trader relationships.Finally, as ECX lacked previous experiences with visualization, one software developer was trained in computer graphics and involved in the work, to enable continued maintenance and future development of new visualization solutions within the organization.</p>