An Exploratory Study of Word-Scale Graphics in Data-Rich Text Documents

International audienceWe contribute an investigation of the design and function of word-scale graphics and visualizations embedded in text documents. Word-scale graphics include both data-driven representations such as word-scale visualizations and sparklines, and non-data-driven visual marks. Their design, function, and use has so far received little research attention. We present the results of an open ended exploratory study with 9 graphic designers. The study resulted in a rich collection of different types of graphics, data provenance, and relationships between text, graphics, and data. Based on this corpus, we present a systematic overview of word-scale graphic designs, and examine how designers used them. We also discuss the designers’ goals in creating their graphics, and characterize how they used word-scale graphics to visualize data, add emphasis, and create alternative narratives. Building on these examples, we discuss implications for the design of authoring tools for word-scale graphics and visualizations, and explore how new authoring environments could make it easier for designers to integrate them into documents

Empirically measuring soft knowledge in visualization

In this paper, we present an empirical study designed to evaluate the hypothesis that humans’ soft knowledge can enhance the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e., in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control variables, and used real-world time series data to construct stimuli. The results of the study confirmed the positive contribution of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents distortion more effectively than the other two classes

Textension: Digitally Augmenting Document Spaces in Analog Texts

In this paper, we present a system that automatically adds visualizations and natural language processing applications to analog texts, using any web-based device with a camera. After taking a picture of a particular page or set of pages from a book or uploading an existing image, our system builds an interactive digital object that automatically inserts modular elements in a digital space. Leveraging the findings of previous studies, our framework augments the reading of analog texts with digital tools, making it possible to work with texts in both a digital and analog environment

The Impact of Visual Aesthetics on the Utility, Affordance, and Readability of Network Graphs

The readability of networks – how different visual design elements affect the understanding of network data – has been central in network visualization research. However, existing studies have mainly focused on readability induced by topological mapping (based on different layouts) and overlooked the effect of visual aesthetics. Proposed is a novel experimental framework to study how different network aesthetic choices affect users' abilities of understanding the network structures. The visual aesthetics are grouped in two forms: 1) visual encoding (where the aesthetic mapping depends on the underlying network data) and 2) visual styling (where the aesthetics are applied independent of underlying data). Users are given a simple task – identifying most connected nodes in a network – in a hybrid experimental setting where the visual aesthetic choices are tested in a within-subject manner while the network topologies are tested in a between-subject manner based on a randomized blocking design. This novel experimental design ensures an efficient decoupling of the influence of network topology on readability tests. The utility of different visual aesthetics is measured comprehensively based on task performance (accuracy and time), eye-tracking data, and user feedback (perceived affordance). The results show differential readability effects among choices of visual aesthetics. Particularly, node based visual encoding significantly enhances network readability; specifically, glyphs allow participants to create more robust strategies in their utilization. The study contributes to both the understanding of the role of visual aesthetics in network visualization design and the experimental design for testing the network readability

Quality-Aware Tooling

Programming is a fascinating activity that can yield results capable of changing people lives by automating daily tasks or even completely reimagining how we perform certain activities. Such a great power comes with a handful of challenges, with software maintainability being one of them. Maintainability cannot be validated by executing the program but has to be assessed by analyzing the codebase. This tedious task can be also automated by the means of software development. Programs called static analyzers can process source code and try to detect suspicious patterns. While these programs were proven to be useful, there is also an evidence that they are not used in practice. In this dissertation we discuss the concept of quality-aware tooling —- an approach that seeks a promotion of static analysis by seamlessly integrating it into development tools. We describe our experience of applying quality-aware tooling on a core distribution of a development environment. Our main focus is to provide live quality feedback in the code editor, but we also integrate static analysis into other tools based on our code quality model. We analyzed the attitude of the developers towards the integrated static analysis and assessed the impact of the integration on the development ecosystem. As a result 90% of software developers find the live feedback useful, quality rules received an overhaul to better match the contemporary development practices, and some developers even experimented with a custom analysis implementations. We discovered that live feedback helped developers to avoid dangerous mistakes, saved time, and taught valuable concepts. But most importantly we changed the developers' attitude towards static analysis from viewing it as just another tool to seeing it as an integral part of their toolset

Visualisation Support for Biological Bayesian Network Inference

Extracting valuable information from the visualisation of biological data and turning it into a network model is the main challenge addressed in this thesis. Biological networks are mathematical models that describe biological entities as nodes and their relationships as edges. Because they describe patterns of relationships, networks can show how a biological system works as a whole. However, network inference is a challenging optimisation problem impossible to resolve computationally in polynomial time. Therefore, the computational biologists (i.e. modellers) combine clustering and heuristic search algorithms with their tacit knowledge to infer networks. Visualisation can play an important role in supporting them in their network inference workflow. The main research question is: “How can visualisation support modellers in their workflow to infer networks from biological data?” To answer this question, it was required to collaborate with computational biologists to understand the challenges in their workflow and form research questions. Following the nested model methodology helped to characterise the domain problem, abstract data and tasks, design effective visualisation components and implement efficient algorithms. Those steps correspond to the four levels of the nested model for collaborating with domain experts to design effective visualisations. We found that visualisation can support modellers in three steps of their workflow. (a) To select variables, (b) to infer a consensus network and (c) to incorporate information about its dynamics.To select variables (a), modellers first apply a hierarchical clustering algorithm which produces a dendrogram (i.e. a tree structure). Then they select a similarity threshold (height) to cut the tree so that branches correspond to clusters. However, applying a single-height similarity threshold is not effective for clustering heterogeneous multidimensional data because clusters may exist at different heights. The research question is: Q1 “How to provide visual support for the effective hierarchical clustering of many multidimensional variables?” To answer this question, MLCut, a novel visualisation tool was developed to enable the application of multiple similarity thresholds. Users can interact with a representation of the dendrogram, which is coordinated with a view of the original multidimensional data, select branches of the tree at different heights and explore different clustering scenarios. Using MLCut in two case studies has shown that this method provides transparency in the clustering process and enables the effective allocation of variables into clusters.Selected variables and clusters constitute nodes in the inferred network. In the second step (b), modellers apply heuristic search algorithms which sample a solution space consisting of all possible networks. The result of each execution of the algorithm is a collection of high-scoring Bayesian networks. The task is to guide the heuristic search and help construct a consensus network. However, this is challenging because many network results contain different scores produced by different executions of the algorithm. The research question is: Q2 “How to support the visual analysis of heuristic search results, to infer representative models for biological systems?” BayesPiles, a novel interactive visual analytics tool, was developed and evaluated in three case studies to support modellers explore, combine and compare results, to understand the structure of the solution space and to construct a consensus network.As part of the third step (c), when the biological data contain measurements over time, heuristics can also infer information about the dynamics of the interactions encoded as different types of edges in the inferred networks. However, representing such multivariate networks is a challenging visualisation problem. The research question is: Q3 “How to effectively represent information related to the dynamics of biological systems, encoded in the edges of inferred networks?” To help modellers explore their results and to answer Q3, a human-centred crowdsourcing experiment took place to evaluate the effectiveness of four visual encodings for multiple edge types in matrices. The design of the tested encodings combines three visual variables: position, orientation, and colour. The study showed that orientation outperforms position and that colour is helpful in most tasks. The results informed an extension to the design of BayePiles, which modellers evaluated exploring dynamic Bayesian networks. The feedback of most participants confirmed the results of the crowdsourcing experiment.This thesis focuses on the investigation, design, and application of visualisation approaches for gaining insights from biological data to infer network models. It shows how visualisation can help modellers in their workflow to select variables, to construct representative network models and to explore their different types of interactions, contributing in gaining a better understanding of how biological processes within living organisms work

Micro Visualizations: Design and Analysis of Visualizations for Small Display Spaces

The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.Le sujet de cette habilitation est l'étude de très petites visualisations de données, les micro visualisations, dans des contextes d'affichage qui ne peuvent consacrer qu'un espace de rendu minimal aux représentations de données. Depuis plusieurs années, avec mes collaborateurs, j'étudie la perception humaine, l'interaction et l'analyse conduite avec des micro visualisations dans de multiples contextes.Dans ce document, je rassemble trois de mes axes de recherche liés aux micro visualisations~: les glyphes de données, où ma recherche s'est concentrée sur l'étude de la perception de micro visualisations dans un context \textit{small-multiple}, les \textit{word-scale visualizations}, où ma recherche s'est concentrée sur les petites visualisations intégrées dans les documents textuels, et les petites visualisations de données mobiles pour les montres connectées. Je considère ces types de petites visualisations sous le terme générique de ``micro visualisations.'' Les micro visualisations sont utiles dans de multiples contextes de visualisation et j'ai travaillé à une meilleure compréhension de la complexité des conceptions et utilisations des micro visualisations. Je définirai ici le terme de micro visualisation, je résumerai mes propres recherches et celles d'autres chercheurs, ainsi que les directives de conception, et j'esquisserai plusieurs espaces de conception pour différents types de micro visualisations, sur la base de certains des travaux auxquels j'ai participé depuis mon doctorat

行列技術を用いた動的ネットワーク可視化

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 大澤 幸生, 東京大学教授 青山 和浩, 東京大学教授 和泉 潔, 東京大学准教授 森 純一郎, 首都大学東京教授 高間 康史University of Tokyo(東京大学