ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making Paths

In problem-solving, a path towards solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. By means of dimensionality reduction, these trajectories can be visualized in lower-dimensional space. Such embedded trajectories have previously been applied to a wide variety of data, but analysis has focused almost exclusively on the self-similarity of single trajectories. In contrast, we describe patterns emerging from drawing many trajectories---for different initial conditions, end states, and solution strategies---in the same embedding space. We argue that general statements about the problem-solving tasks and solving strategies can be made by interpreting these patterns. We explore and characterize such patterns in trajectories resulting from human and machine-made decisions in a variety of application domains: logic puzzles (Rubik's cube), strategy games (chess), and optimization problems (neural network training). We also discuss the importance of suitably chosen representation spaces and similarity metrics for the embedding.Comment: Final version; accepted for publication in the ACM TiiS Special Issue on "Interactive Visual Analytics for Making Explainable and Accountable Decisions

VisAhoi: Towards a Library to Generate and Integrate Visualization Onboarding Using High-level Visualization Grammars

Visualization onboarding supports users in reading, interpreting, and extracting information from visual data representations. General-purpose onboarding tools and libraries are applicable for explaining a wide range of graphical user interfaces but cannot handle specific visualization requirements. This paper describes a first step towards developing an onboarding library called VisAhoi, which is easy to integrate, extend, semi-automate, reuse, and customize. VisAhoi supports the creation of onboarding elements for different visualization types and datasets. We demonstrate how to extract and describe onboarding instructions using three well-known high-level descriptive visualization grammars - Vega-Lite, Plotly.js, and ECharts. We show the applicability of our library by performing two usage scenarios that describe the integration of VisAhoi into a VA tool for the analysis of high-throughput screening (HTS) data and, second, into a Flourish template to provide an authoring tool for data journalists for a treemap visualization. We provide a supplementary website that demonstrates the applicability of VisAhoi to various visualizations, including a bar chart, a horizon graph, a change matrix or heatmap, a scatterplot, and a treemap visualization

Peptide-equipped tobacco mosaic virus templates for selective and controllable biomineral deposition

The coating of regular-shaped, readily available nanorod biotemplates with inorganic compounds has attracted increasing interest during recent years. The goal is an effective, bioinspired fabrication of fiber-reinforced composites and robust, miniaturized technical devices. Major challenges in the synthesis of applicable mineralized nanorods lie in selectivity and adjustability of the inorganic material deposited on the biological, rod-shaped backbones, with respect to thickness and surface profile of the resulting coating, as well as the avoidance of aggregation into extended superstructures. Nanotubular tobacco mosaic virus (TMV) templates have proved particularly suitable towards this goal: Their multivalent protein coating can be modified by high-surface-density conjugation of peptides, inducing and governing silica deposition from precursor solutions in vitro. In this study, TMV has been equipped with mineralization-directing peptides designed to yield silica coatings in a reliable and predictable manner via precipitation from tetraethoxysilane (TEOS) precursors. Three peptide groups were compared regarding their influence on silica polymerization: (i) two peptide variants with alternating basic and acidic residues, i.e. lysine–aspartic acid (KD)$_{χ}$ motifs expected to act as charge-relay systems promoting TEOS hydrolysis and silica polymerization; (ii) a tetrahistidine-exposing polypeptide (CA$_{4}$H$_{4}$) known to induce silicification due to the positive charge of its clustered imidazole side chains; and (iii) two peptides with high ZnO binding affinity. Differential effects on the mineralization of the TMV surface were demonstrated, where a (KD)$_{χ}$ charge-relay peptide (designed in this study) led to the most reproducible and selective silica deposition. A homogenous coating of the biotemplate and tight control of shell thickness were achieved

Interactive Focus+Context Analysis of Time-Series and Provenance Data

Die effiziente Exploration großer und komplexer Datensätze ist eine fortwährende Herausforderung in Visual Analytics. Visualisiert man solche Datensätze als Ganzes führt das in der Regel zu einer chaotischen Darstellung, die es für den Anwender schwierig macht, potenziell interessante Teile aus den Daten zu identifizieren. Eine mögliche Lösung zur Reduzierung der Unordnung sind Fokus- und Kontexttechniken, welche ausgewählte Regionen detaillierter darstellen und gleichzeitig eine Übersicht mit reduzierten Details geben. Für große Datensätze bleibt die Auswahl der Fokusbereiche jedoch eine zeitaufwändige Aufgabe für den Anwender, sofern diese einzeln ausgewählt werden müssen. Des Weiteren ist das Interesse für eine bestimmte Teilmenge im Fall von zeitbasierten Daten möglicherweise nicht konstant, sondern verschiebt sich über die Zeit oder wechselt zu einer anderen Teilmenge. Folglich ist es notwendig, Fokus- und Kontexttechniken zu entwickeln, die speziell auf große, zeitbasierte Daten zugeschnitten sind. Diese Arbeit stellt vier interaktive Visualisierungsansätze zur Hervorhebung interessanter Teilmengen aus Zeitreihen und Provenienzdaten vor. Die Lösungen verwenden modulare Funktionen mit denen der Anwender das Interesse für die Teilmengen definieren kann. Die Funktionen können von einem oder mehreren Datenattributen, der Topologie des Graphen oder einer Kombination aus beidem gesteuert werden. Die praktische Anwendbarkeit wird anhand von verschiedenen Fallstudien aus den Bereichen Cloud Computing, Finanzwesen und biomedizinische Forschung gezeigt.Efficient exploration of large and complex datasets, as for instance, time-series and provenance data, is an ongoing research challenge in visual analytics. Visualizing such datasets in one go often leads to visual clutter, making it hard for users to identify potentially interesting data subsets. A possible solution to reduce the clutter is Focus+Context techniques, which visualize selected regions in greater detail while preserving an overview with reduced details. For large datasets, however, selecting focus regions can become a time-consuming task if each region must be selected individually. Furthermore, in the case of temporal data, the interest in a particular data subset might not remain constant but, rather, shift over time or switch to other data subsets. Consequently, it is necessary to develop Focus+Context solutions tailored to large temporal data. This thesis presents four interactive visualization approaches for highlighting potentially interesting subsets in time-series and provenance data. The solutions utilize modular degree of interest functions that are driven by one or multiple data attributes, the topology of the graph, or a combination of both. The practical applicability of these approaches is demonstrated by means of different case studies from cloud computing, Finance, and biomedical research.submitted by Holger Stitz, MSc.Universität Linz, Dissertation, 2019OeBB(VLID)366368

Peptide-equipped tobacco mosaic virus templates for selective and controllable biomineral deposition

The coating of regular-shaped, readily available nanorod biotemplates with inorganic compounds has attracted increasing interest during recent years. The goal is an effective, bioinspired fabrication of fiber-reinforced composites and robust, miniaturized technical devices. Major challenges in the synthesis of applicable mineralized nanorods lie in selectivity and adjustability of the inorganic material deposited on the biological, rod-shaped backbones, with respect to thickness and surface profile of the resulting coating, as well as the avoidance of aggregation into extended superstructures. Nanotubular tobacco mosaic virus (TMV) templates have proved particularly suitable towards this goal: Their multivalent protein coating can be modified by high-surface-density conjugation of peptides, inducing and governing silica deposition from precursor solutions in vitro. In this study, TMV has been equipped with mineralization-directing peptides designed to yield silica coatings in a reliable and predictable manner via precipitation from tetraethoxysilane (TEOS) precursors. Three peptide groups were compared regarding their influence on silica polymerization: (i) two peptide variants with alternating basic and acidic residues, i.e. lysine–aspartic acid (KD)x motifs expected to act as charge-relay systems promoting TEOS hydrolysis and silica polymerization; (ii) a tetrahistidine-exposing polypeptide (CA4H4) known to induce silicification due to the positive charge of its clustered imidazole side chains; and (iii) two peptides with high ZnO binding affinity. Differential effects on the mineralization of the TMV surface were demonstrated, where a (KD)x charge-relay peptide (designed in this study) led to the most reproducible and selective silica deposition. A homogenous coating of the biotemplate and tight control of shell thickness were achieved