Virtual reality-based parallel coordinates plots enhanced with explainable ai and data-science analytics for decision-making processes

We present a refinement of the Immersive Parallel Coordinates Plots (IPCP) system for Virtual Reality (VR). The evolved system provides data-science analytics built around a well-known method for visualization of multidimensional datasets in VR. The data-science analytics enhancements consist of importance analysis and a number of clustering algorithms including a novel SuMC (Subspace Memory Clustering) solution. These analytical methods were applied to both the main visualizations and supporting cross-dimensional scatter plots. They automate part of the analytical work that in the previous version of IPCP had to be done by an expert. We test the refined system with two sample datasets that represent the optimum solutions of two different multi-objective optimization studies in turbomachinery. The first one describes 54 data items with 29 dimensions (DS1), and the second 166 data items with 39 dimensions (DS2). We include the details of these methods as well as the reasoning behind selecting some methods over others.</jats:p

Addressing the unmet need for visualizing Conditional Random Fields in Biological Data

Background: The biological world is replete with phenomena that appear to be ideally modeled and analyzed by one archetypal statistical framework - the Graphical Probabilistic Model (GPM). The structure of GPMs is a uniquely good match for biological problems that range from aligning sequences to modeling the genome-to-phenome relationship. The fundamental questions that GPMs address involve making decisions based on a complex web of interacting factors. Unfortunately, while GPMs ideally fit many questions in biology, they are not an easy solution to apply. Building a GPM is not a simple task for an end user. Moreover, applying GPMs is also impeded by the insidious fact that the complex web of interacting factors inherent to a problem might be easy to define and also intractable to compute upon. Discussion: We propose that the visualization sciences can contribute to many domains of the bio-sciences, by developing tools to address archetypal representation and user interaction issues in GPMs, and in particular a variety of GPM called a Conditional Random Field(CRF). CRFs bring additional power, and additional complexity, because the CRF dependency network can be conditioned on the query data. Conclusions: In this manuscript we examine the shared features of several biological problems that are amenable to modeling with CRFs, highlight the challenges that existing visualization and visual analytics paradigms induce for these data, and document an experimental solution called StickWRLD which, while leaving room for improvement, has been successfully applied in several biological research projects.Comment: BioVis 2014 conferenc

Doctor of Philosophy

dissertationCorrelation is a powerful relationship measure used in many fields to estimate trends and make forecasts. When the data are complex, large, and high dimensional, correlation identification is challenging. Several visualization methods have been proposed to solve these problems, but they all have limitations in accuracy, speed, or scalability. In this dissertation, we propose a methodology that provides new visual designs that show details when possible and aggregates when necessary, along with robust interactive mechanisms that together enable quick identification and investigation of meaningful relationships in large and high-dimensional data. We propose four techniques using this methodology. Depending on data size and dimensionality, the most appropriate visualization technique can be provided to optimize the analysis performance. First, to improve correlation identification tasks between two dimensions, we propose a new correlation task-specific visualization method called correlation coordinate plot (CCP). CCP transforms data into a powerful coordinate system for estimating the direction and strength of correlations among dimensions. Next, we propose three visualization designs to optimize correlation identification tasks in large and multidimensional data. The first is snowflake visualization (Snowflake), a focus+context layout for exploring all pairwise correlations. The next proposed design is a new interactive design for representing and exploring data relationships in parallel coordinate plots (PCPs) for large data, called data scalable parallel coordinate plots (DSPCP). Finally, we propose a novel technique for storing and accessing the multiway dependencies through visualization (MultiDepViz). We evaluate these approaches by using various use cases, compare them to prior work, and generate user studies to demonstrate how our proposed approaches help users explore correlation in large data efficiently. Our results confirmed that CCP/Snowflake, DSPCP, and MultiDepViz methods outperform some current visualization techniques such as scatterplots (SCPs), PCPs, SCP matrix, Corrgram, Angular Histogram, and UntangleMap in both accuracy and timing. Finally, these approaches are applied in real-world applications such as a debugging tool, large-scale code performance data, and large-scale climate data

Hybrid visualizations for data exploration

Information Visualization (Infovis) graphically encodes information to help a user explore a data set visually and interactively. This graphical encoding can take the form of widespread visualizations such as bar charts and scatterplots. Multiple visualizations can share the same functional space to form complete tools for visual exploration or for communicating information. There is multiple ways of combining these visualizations. The assembly of multiple visualizations can give some complex assemblies sometimes called hybrid visualizations. A hybrid visualization is the result of assembling multiple simpler visualizations. For example, NodeTrix (Henry et al., 2007a) is composed of a node-link diagram and an adjacency matrix, and MatLink (Henry and Fekete, 2007a) adds arc links to an adjacency matrix. This integration of multiple visualizations can be a way to combine their advantages into a coherent structure. The integration can be achieved, for example, through color coding, or through explicit linking (such as with arrows), or through interaction (such as when different visualizations respond to the manipulation of others). Recent literature contains several examples of new hybrid visualizations, most often to deal with complex datasets where the user can benefit from multiple, complementary visual encodings of the same data. However, to date, there is almost no theory or framework to help researchers understand and characterize existing hybrids or design new ones. This thesis advances the state of the art in hybrid visualizations in two ways: first, by developing a framework that defines and characterizes hybrid visualizations to help better identify, describe and design them, and second, by demonstrating a variety of novel hybrids. The hybrid visualizations we explored cover a wide range of possibilities. Two of the most general and widely used data types in Infovis, multidimensional multivariate data and graph (i.e., network) data, are each the subject of a chapter in the thesis, with novel hybrid visualization techniques presented for each. A wide range of possibilities for integration is also presented using a pipeline model. After some preliminary material, chapter 2 of the thesis presents a conceptual framework that defines and characterizes hybrid visualizations. This framework was itself derived from experience designing the hybrid visualizations presented in the subsequent chapters. A hybrid visualization is described as a graphical encoding using other visualizations as building blocks. We present a pipeline to illustrate the assembly of a visualization, starting from the generation of basic shapes or glyphs, then placed on a layout, embellished by adding other graphical elements, then sent to some view transform operators and assembled on the same space. Simple charts can be described with this pipeline as well as more complex assembly and new hybrids are described. Chapter 3 presents ConnectedCharts, an example of a hybrid assembled on the assembly level of the pipeline, made of multiple multidimensional and multivariate charts explicitly connected by lines or curves showing the relationship between their elements. A user interface enables the interactive assembly of ConnectedCharts, including a wide range of previously-published hybrid visualizations, as well as novel hybrid arrangements. ConnectedCharts serve as an illustration of the conceptual framework in chapter 2, by exploring possible connections between different graphics depending on the relationship of their encoded data types. Chapter 4 presents another user interface, this time for graph exploration, that incorporates several highly integrated hybrid visualizations. A Parallel Scatter Plot Matrix (P-SPLOM) is presented that constitutes a fusion of a Scatter Plot Matrix (SPLOM) and a Parallel Coordinates Plot (PCP). A radial menu called the FlowVizMenu enables the modification of a visualization integrated at the center of the menu. This menu is also used to select the dimensions for configuring a third hybrid based on an Attribute-Driven Layout (ADL) that combines a nodelink diagram and a scatterplot. The characterization of hybrid visualizations offered by the conceptual framework, as well as the illustration of the framework by innovative hybrid visualizations, are the main contributions of this thesis to the Infovis community

Visualisation interactive de graphes (élaboration et optimisation d'algortihmes à coûts computationnels élevés)

Un graphe est un objet mathématique modélisant des relations sur un ensemble d'éléments. Il est utilisé dans de nombreux domaines à des fins de modélisation. La taille et la complexité des graphes manipulés de nos jours entraînentdes besoins de visualisation afin de mieux les analyser. Dans cette thèse, nous présentons différents travaux en visualisation interactive de graphes qui s'attachent à exploiter les architectures de calcul parallèle (CPU et GPU) disponibles sur les stations de travail contemporaines. Un premier ensemble de travaux s'intéresse à des problématiques de dessin de graphes. Dessiner un graphe consiste à le plonger visuellement dans un plan ou un espace. La première contribution dans cette thématique est un algorithmede regroupement d'arêtes en faisceaux appelé Winding Roads.Cet algorithme intuitif, facilement implémentable et parallélisable permet de réduireconsidérablement les problèmes d'occlusion dans un dessin de graphedus aux nombreux croisements d'arêtes.La seconde contribution est une méthode permettant dedessiner un réseau métabolique complet. Ce type deréseau modélise l'ensemble des réactions biochimiquesse produisant dans les cellules d'un organise vivant.L'avantage de la méthode est de prendre en compte la décompositiondu réseau en sous-ensembles fonctionnels ainsi que de respecterles conventions de dessin biologique.Un second ensemble de travaux porte sur des techniques d'infographiepour la visualisation interactive de graphes. La première contribution dans cette thématique est une technique de rendude courbes paramétriques exploitant pleinement le processeur graphique. La seconde contribution est une méthodede rendu nommée Edge splatting permettant de visualiserla densité des faisceaux d'arêtes dans un dessin de grapheavec regroupement d'arêtes. La dernière contribution portesur des techniques permettant de mettre en évidence des sous-graphesd'intérêt dans le contexte global d'une visualisation de graphes.A graph is a mathematical object used to model relations over a set of elements.It is used in numerous fields for modeling purposes. The size and complexityof graphs manipulated today call a need for visualization to better analyze them.In that thesis, we introducedifferent works in interactive graph visualisation which aim at exploiting parallel computing architectures (CPU and GPU) available on contemporary workstations.A first set of works focuses on graph drawing problems.Drawing a graph consists of embedding him in a plane or a space.The first contribution in that theme is an edge bundling algorithmnamed Winding Roads. That intuitive, easyly implementable and parallelizable algorithmallows to considerably reduce clutter due to numerous edge crossings in a graph drawing.The second contribution is a method to draw a complete metabolicnetwork. That kind of network models the whole set of biochemical reactionsoccurring within cells of a living organism. The advantage of the methodis to take into account the decomposition of the network into functionnal subsetsbut also to respect biological drawing conventions.A second set of works focuses on computer graphics techniquesfor interactive graph visualisation. The first contributionin that theme is a technique for rendering parametric curvesthat fully exploits the graphical processor unit. The second contributionis a rendering technique named Edge splatting that allowsto visualize the bundles densities in an edge bundled layout. Thelast contribution introduces some techniques for emphasizingsub-graphs of interest in the global context of a graph visualization.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Visualizing Evaluative Language in Relation to Constructing Identity in English Editorials and Op-Eds

This thesis is concerned with the problem of managing complexity in Systemic Functional Linguistic (SFL) analyses of language, particularly at the discourse semantics level. To deal with this complexity, the thesis develops AppAnn, a suite of linguistic visualization techniques that are specifically designed to provide both synoptic and dynamic views on discourse semantic patterns in text and corpus. Moreover, AppAnn visualizations are illustrated in a series of explorations of identity in a corpus of editorials and op-eds about the bin Laden killing. The findings suggest that the intriguing intricacies of discourse semantic meanings can be successfully discerned and more readily understood through linguistic visualization. The findings also provide insightful implications for discourse analysis by contributing to our understanding of a number of underdeveloped concepts of SFL, including coupling, commitment, instantiation, affiliation and individuation