User Interfaces and Difference Visualizations for Alternatives

Designers often create multiple iterations to evaluate alternatives. Todays computer-based tools do not support such easy exploration of a design space, despite the fact that such support has been advocated. This dissertation is centered on this. I begin by investigating the effectiveness of various forms of difference visualizations and support for merging changes within a system targeted at diagrams with node and edge attributes. I evaluated the benefits of the introduced difference visualization techniques in two user studies. I found that the basic side-by-side juxtaposition visualization was not effective and also not well received. For comparing diagrams with matching node positions, participants preferred the side-by-side option with a difference layer. For diagrams with non-matching positions animation was beneficial, but the combination with a difference layer was preferred. Thus, the difference layer technique was useful and a good complement to animation. I continue by investigating if explicit support for design alternatives better supports exploration and creativity in a generative design system. To investigate the new techniques to better support exploration, I built a new system that supports parallel exploration of alternative designs and generation of new structural combinations. I investigate the usefulness of my prototype in two user studies and interviews. The results and feedback suggest and confirm that supporting design alternatives explicitly enables designers to work more creatively. Generative models are often represented as DAGs (directed acyclic graphs) in a dataflow programming environment. Existing approaches to compare such DAGs do not generalize to multiple alternatives. Informed by and building on the first part of my dissertation, I introduce a novel user interface that enables visual differencing and editing alternative graphsspecifically more than two alternatives simultaneously, something that has not been presented before. I also explore multi-monitor support to demonstrate that the difference visualization technique scales well to up to 18 alternatives. The novel jamming space feature makes organizing alternatives on a 23 monitor system easier. To investigate the usability of the new difference visualization method I conducted an exploratory interview with three expert designers. The received comments confirmed that it meets their design goals

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices

A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes

International audienceWe present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo-spatial, videos, networks, multivariate data). The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al.'s (2014) work

Spectron: Graphical Model for Interacting With Timbre

Los algoritmos para crear y manipular el sonido por medios electrónicos o digitales han crecido en cantidad y complejidad desde la creación de los primeros sintetizadores análogos. Sin embargo, las técnicas para visualizar estos modelos de síntesis no han crecido a la par de los sintetizadores hardware o software. En este artículo se muestran posibilidades para representar y controlar gráficamente el timbre, basadas en la visualización de los parámetros involucrados en su modelo de síntesis. Un grupo de datos muy simple fue extraído de un sintetizador substractivo comercial y analizado con dos aproximaciones diferentes, reducción dimensional y visualización abstracta de datos. Los resultados de estas aproximaciones diferentes fueron usados como lineamientos para crear un prototipo de sintetizador digital: el sintetizador Spectron. Este prototipo usa el gráfico de Amplitud vs. Frecuencia como su principal herramienta para informar a cerca del timbre e interactuar con el, fue desarrollado en PureData y su control plantea una simplificación en la cantidad de variables de un oscilador clásico al mismo tiempo que expande las posibilidades para generar timbres adicionales a los de estos osciladores clásicos.The algorithms for creating and manipulating sound by electronic or digital means have grown in number and complexity since the creation of the first analog synthesizers. The techniques for visualizing these synthesis models have not increasingly grown with synthesizers, neither in hardware nor in software. In this paper, the possibilities to graphically represent and control timbre are presented, based on displaying the parameters involved in its synthesis model. A very simple data set was extracted from a commercial subtractive synthesizer and analyzed in two different approaches, dimensionality reduction and abstract data visualization. The results of these two different approaches were used as leads to design a synthesizer prototype: the Spectron synthesizer. This prototype uses an Amplitude vs. Frequency graphic as it´s main interface to give information about the timbre and to interact with it, it´s control offers a simplification in the amount of variables of a classic oscillator and expands its possibilities to generate additional timbre

Metric Evolution Maps:Multidimensional Attribute-driven Exploration of Software Repositories

Understanding how software entities in a repository evolve over time is challenging, as an entity has many aspects that undergo such changes. We cast this problem in a multidimensional visualization context: First, we capture change by extracting quality metrics from all software entities in all revisions in a software repository, yielding a multidimensional time-dependent dataset. Next, we propose Metric Evolution Maps (MEMs), a new visual approach to create dynamic maps that show the similarity of entities in a revision and changes across revisions. We enrich MEMs with visual cues to show which metrics and metric values are key to formation of similar-entity patterns. Additionally, we show how entities change between revisions, and due to which metrics. We illustrate our approach by exploring changes in two real-world software repositories

A Review of Temporal Data Visualizations Based on Space-Time Cube Operations

International audienceWe review a range of temporal data visualization techniques through a new lens, by describing them as series of op- erations performed on a conceptual space-time cube. These operations include extracting subparts of a space-time cube, flattening it across space or time, or transforming the cube's geometry or content. We introduce a taxonomy of elementary space-time cube operations, and explain how they can be combined to turn a three-dimensional space-time cube into an easily-readable two-dimensional visualization. Our model captures most visualizations showing two or more data dimensions in addition to time, such as geotemporal visualizations, dynamic networks, time-evolving scatterplots, or videos. We finally review interactive systems that support a range of operations. By introducing this conceptual framework we hope to facilitate the description, criticism and comparison of existing temporal data visualizations, as well as encourage the exploration of new techniques and systems

Visualisations novatrices pour la compréhension de réseaux et de logiciels complexes

La visualisation d’information a le potentiel de pouvoir exploiter nos capacités visuelles, acquises au fil de centaines de millions d’années d’évolution, afin de faciliter la découverte de secrets enfouis dans les données, de nouveaux patrons ou de relations insoupçonnées. Il existe toutefois une grande variété de données, plus ou moins structurées, que l’on cherche à comprendre sous diverses perspectives. En particulier, les données sous forme de réseaux servent à modéliser des phénomènes importants, tels que les communautés sociales ou les transactions financières, mais peuvent être difficiles à représenter si les réseaux sont grands, hiérarchiques, et/ou dynamiques. Cette thèse se concentre sur la conception de nouvelles techniques de visualisation de réseaux, dans le but de faciliter la compréhension de données. Les techniques de visualisation présentes dans la littérature sont utiles dans certains contextes et comportent chacune des limitations. Néanmoins, il existe encore des possibilités inexplorées pour créer des nouvelles façons de représenter des données. La validation de ces nouvelles techniques demeure un défi. En outre, les interfaces doivent être simples à utiliser, mais aussi faciliter l’analyse et l’exploration de données. Dans le but d’étudier de nouvelles options de visualisations pour faciliter des tâches de compréhension des données, nous avons d’abord classifié les travaux antérieurs avec des taxonomies. De cette manière, nous avons aussi pu mettre en lumière des nouvelles pistes d’hybrides (c’est-à-dire, des combinaisons d’approches) potentiellement intéressantes pour visualiser des réseaux statiques et dynamiques. Les contributions présentées dans cette thèse couvrent différents aspects de la visualisation de réseaux complexes et dynamiques. D’abord, le premier chapitre se concentre sur la visualisation de réseaux statiques comportant des hiérarchies, par la combinaison d’approches. Le prototype décrit dans le deuxième chapitre permet également de combiner des représentations visuelles, mais peut être aussi utilisé afin de modéliser des graphes dynamiques. Enfin, le troisième chapitre présente une nouvelle méthode visuelle appliquée afin de tracer l’évolution de structures de conception complexes dans des logiciels (modélisés par des réseaux). Ainsi, dans le premier prototype (TreeMatrix), des parties de graphes sont montrées avec des matrices et des diagrammes noeuds-liens, alors que les arborescences sont représentées par des diagrammes en glaçons et des regroupements. Contrairement aux autres visualisations dans la littérature, cette nouvelle technique aide à montrer des réseaux denses, sans nuire à la compréhension des liens à plus haut niveau. Une expérience avec des utilisateurs a montré certains avantages afin de découvrir et organiser les liens de modules au sein d’un logiciel, en comparaison avec le logiciel commercial Lattix. Nous avons également combiné des approches de manière novatrice pour notre second prototype (DiffAni) afin de visualiser des réseaux qui évoluent dans le temps. DiffAni est le premier hybride interactif de graphes dynamiques et sa validation avec des participants a permis de faire ressortir certains avantages. Ainsi, l’utilisation d’animation doit être modérée et est surtout utile lors de mouvements significatifs. Ces résultats, avec nos taxonomies, pourraient contribuer à guider la création de nouveaux hybrides dans le futur. Le troisième prototype (IHVis) a facilité l’exploration et le traçage de structures de conception dans des logiciels en évolution (modélisés par des réseaux) à partir de répertoires de code source. Cette nouvelle visualisation a notamment révélé des cas d’introduction de points de stabilité et des refactorings, et certains participants ont aussi trouvé d’autres informations intéressantes, telles que l’extension de fonctionnalités par l’implémentation d’interfaces. En résumé, cette thèse présente des façons novatrices et utiles de visualiser des réseaux complexes et dynamiques. Nos principales contributions sont (1) l’exploration d’espaces de conception de nouvelles visualisations de réseaux à l’aide de taxonomies, (2) la conception de prototypes combinant des approches pour visualiser des réseaux hiérarchiques et dynamiques, (3) la conception d’une nouvelle méthode visuelle d’exploration des variations et des instabilités au sein de logiciels en évolution, (4) l’évaluation de ces techniques à l’aide d’expériences avec des participants

An Uncertainty Visual Analytics Framework for Functional Magnetic Resonance Imaging

Improving understanding of the human brain is one of the leading pursuits of modern scientific research. Functional magnetic resonance imaging (fMRI) is a foundational technique for advanced analysis and exploration of the human brain. The modality scans the brain in a series of temporal frames which provide an indication of the brain activity either at rest or during a task. The images can be used to study the workings of the brain, leading to the development of an understanding of healthy brain function, as well as characterising diseases such as schizophrenia and bipolar disorder. Extracting meaning from fMRI relies on an analysis pipeline which can be broadly categorised into three phases: (i) data acquisition and image processing; (ii) image analysis; and (iii) visualisation and human interpretation. The modality and analysis pipeline, however, are hampered by a range of uncertainties which can greatly impact the study of the brain function. Each phase contains a set of required and optional steps, containing inherent limitations and complex parameter selection. These aspects lead to the uncertainty that impacts the outcome of studies. Moreover, the uncertainties that arise early in the pipeline, are compounded by decisions and limitations further along in the process. While a large amount of research has been undertaken to examine the limitations and variable parameter selection, statistical approaches designed to address the uncertainty have not managed to mitigate the issues. Visual analytics, meanwhile, is a research domain which seeks to combine advanced visual interfaces with specialised interaction and automated statistical processing designed to exploit human expertise and understanding. Uncertainty visual analytics (UVA) tools, which aim to minimise and mitigate uncertainties, have been proposed for a variety of data, including astronomical, financial, weather and crime. Importantly, UVA approaches have also seen success in medical imaging and analysis. However, there are many challenges surrounding the application of UVA to each research domain. Principally, these involve understanding what the uncertainties are and the possible effects so they may be connected to visualisation and interaction approaches. With fMRI, the breadth of uncertainty arising in multiple stages along the pipeline and the compound effects, make it challenging to propose UVAs which meaningfully integrate into pipeline. In this thesis, we seek to address this challenge by proposing a unified UVA framework for fMRI. To do so, we first examine the state-of-the-art landscape of fMRI uncertainties, including the compound effects, and explore how they are currently addressed. This forms the basis of a field we term fMRI-UVA. We then present our overall framework, which is designed to meet the requirements of fMRI visual analysis, while also providing an indication and understanding of the effects of uncertainties on the data. Our framework consists of components designed for the spatial, temporal and processed imaging data. Alongside the framework, we propose two visual extensions which can be used as standalone UVA applications or be integrated into the framework. Finally, we describe a conceptual algorithmic approach which incorporates more data into an existing measure used in the fMRI analysis pipeline