Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts

Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic egonetworks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we developed Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2018

Interactive Visual Analysis of Networked Systems: Workflows for Two Industrial Domains

We report on a first study of interactive visual analysis of networked systems. Working with ABB Corporate Research and Ericsson Research, we have created workflows which demonstrate the potential of visualization in the domains of industrial automation and telecommunications. By a workflow in this context, we mean a sequence of visualizations and the actions for generating them. Visualizations can be any images that represent properties of the data sets analyzed, and actions typically either change the selection of data visualized or change the visualization by choice of technique or change of parameters

Unterstützung des Editierens von Graphen in Visuellen Repräsentationen

The goal of this thesis is to provide solutions for supporting the direct editing of graphs in visual representations for analyzing graphs. For that, a conceptual view on the user's tasks is established first. On this basis, several novel approaches to "visually edit" the different data aspects of graphs - the graph's structure and associated attribute values - are introduced. Thereby, different visual graph representations suitable for communicating the data are considered.Das Ziel der vorliegenden Dissertation ist, Lösungen zur Unterstützung des direkten Editierens von Graphen in visuellen Repräsentationen zur Analyse von Graphen bereitzustellen. Dafür wird zunächst eine konzeptuelle Sicht auf die Aufgaben des Nutzers entwickelt. Auf dieser Basis werden anschließend mehrere neue Verfahren eingeführt, welche das "visuelle Editieren" der verschiedenen Datenaspekte von Graphen - der Struktur sowie dazu assoziierte Attributwerte - ermöglichen. Dabei werden verschiedene visuelle Graphrepräsentationen berücksichtigt, welche die Daten in geeigneter Form kommunizieren

Touch-Based Ontology Browsing on Tablets and Surfaces

Semantic technologies and Linked Data are increasingly adopted as core application modules, in many knowledge domains and involving various stakeholders: ontology engineers, software architects, doctors, employees, etc. Such a diffusion calls for better access to models and data, which should be direct, mobile, visual and time effective. While a relevant core of research efforts investigated the problem of ontology visualization, discovering different paradigms, layouts, and interaction modalities, a few approaches target mobile devices such as tablets and smartphones. Touch interaction, indeed, has the potential of dramatically improving usability of Linked Data and of semantic-based solutions in real-world applications and mash-ups, by enabling direct and tactile interactions with involved knowledge objects. In this paper, we move a step towards touch-based, mobile interfaces for semantic models by presenting an ontology browsing platform for Android devices. We exploit state of the art touch-based interaction paradigms, e.g., pie menus, pinch-to-zoom, etc., to empower effective ontology browsing. Our research mainly focuses on interactions, yet providing support to different visualization approaches thanks to a clear decoupling between model-level operation and visual representations. Presented results include the design and implementation of a working prototype application, as well as a first validation involving habitual users of semantic technologies. Results show a low learning curve and positive reactions to the proposed paradigms, which are perceived as both innovative and useful

An integrated computer-aided modular fixture design system for machining semi-circular parts

Productivity is one of the most important factors in manufacturing processes because of the high level of market competition. In this regard, modular fixtures (MFs) play an important role in practically improving productivity in flexible manufacturing systems (FMSs) due to this technology using highly productive computer numerical control (CNC) machines. MFs consist of devices called jigs and fixtures for accurately holding the workpiece during different machining operations. The design process is complex, and traditional methods of MF design were not sufficiently productive. Computer-aided design (CAD) software has rapidly improved as a result of the development of computer technology, and has provided huge opportunities for modular fixture designers to use its 3D modelling capabilities to develop more automated systems. Computer-aided fixture design (CAFD) systems have become automated by the use of artificial intelligence (AI) technology. This study will investigate the further improvement of automated CAFD systems by using AI tools. In this research, an integrated CAFD is developed by considering four main requirements: · a 3D model of the workpiece, · an expert system, · assembly automation of MFs, · an efficient feature library. The 3D model is an important factor that can provide the appropriate specification of the workpiece; SolidWorks is used the CAD environment for undertaking the 3D modelling in this study. The expert system is applied as a tool to make right decisions about the CAFD planning process, including locating and clamping methods and their related element selection. This helps achieve a feasible fixture design layout. SolidWorks API and Visual Basic programming language are employed for the automating and simulation of the assembly process of MFs. A feature library of modular fixture elements is constructed as a means to simplify the fixture design process

Maleku: an evolutionary visual software analytics tool for providing insights into software evolution

Software maintenance is a complex process that requires the understanding and comprehension of software project details. It involves the understanding of the evolution of the software project, hundreds of software components and the relationships among software items in the form of inheritance, interface implementation, coupling and cohesion. Consequently, the aim of evolutionary visual software analytics is to support software project managers and developers during software maintenance. It takes into account the mining of evolutionary data, the subsequent analysis of the results produced by the mining process for producing evolution facts, the use of visualizations supported by interaction techniques and the active participation of users. Hence, this paper proposes an evolutionary visual software analytics tool for the exploration and comparison of project structural, interface implementation and class hierarchy data, and the correlation of structural data with metrics, as well as socio-technical relationships. Its main contribution is a tool that automatically retrieves evolutionary software facts and represent them using a scalable visualization design

Interactive Visualization for Deep Organizational data

During the last decade, there has been a growing interest in investigating how and why people use organizational data to solve problems, make decisions, and perform other cognitive activities, especially in the social network, healthcare, and education domains. Working with organizational data is challenging because of the complex and multi-structured nature of it. One way to support cognitive activities with organizational data is through the use of interactive visualization tools that provide different representations and mechanisms for interacting with deep layers of the data. In this research, we have deep organizational data which is mainly about collaborations inside universities. The thesis goal is making an interactive visualization tool to support complex cognitive activities with this database. The generated visualization tool has an expandable and reusable structure as well as innovative representations and interactions designed to allow navigating through the data intuitively

CleanGraph - Graph viewing and editing for family trees and UML class diagrams

Traditionally, different types of diagrams are used to represent varying types of data in a fast and easy way to read. Their usage includes describing systems, understanding their features and presenting how different parts are interconnected, making them useful for many applications. Unfortunately, as the complexity of the data showed increases, so does the complexity of the diagram, making it more difficult to read and interpret. Since interactions are usually limited to panning and zooming, there is room for improvement with more interaction and different input methods. The goal is to tackle these issues in two types of diagrams -- Family Trees (FT) and Universal Modeling Language (UML) class diagrams. To achieve this, a new platform will be created, capable of generating diagrams and displaying them in a way where overlapping and unrelated data is minimised alongside having better methods for interacting with and manipulating information. These diagrams will prioritise visual clarity by grouping together elements with relevant or important information, as well as having new ways of interaction, such as selecting elements to receive more information and manually grouping them to better explore the data. Guaranteeing the viability of the solution will require two main issues to be addressed: (1) Defining a fitting diagram disposition and why it is the more advantageous; (2) Understanding the best way to create interactions with diagrams while making it intuitive and keeping them readable. Finding the best platform for the solution to reach its intended audience while providing the desired ease of use is one of the priorities. The evaluation of this work will be attained through previously defined metrics, to ensure that the generated diagram is adequate. The quality of the diagrams will be defined by characteristics such as overlapping connections between elements, related or unrelated data of adjacent elements to the selected one and the amount of displayed data at once. The result of the proposal aims to give a better tool to users seeking improved methods to view their ancestry data, as well as software architects, project managers and system analysts, who would benefit from a better platform for system representation. The developed features can help them convey their ideas easily to developers or anyone interested in the data represented, improving simplicity, workflow and potentially bringing new and improved methods of viewing and displaying information in these areas

Fauxvea: Crowdsourcing Gaze Location Estimates for Visualization Analysis Tasks

We present the design and evaluation of a method for estimating gaze locations during the analysis of static visualizations using crowdsourcing. Understanding gaze patterns is helpful for evaluating visualizations and user behaviors, but traditional eye-tracking studies require specialized hardware and local users. To avoid these constraints, we developed a method called Fauxvea, which crowdsources visualization tasks on the Web and estimates gaze fixations through cursor interactions without eye-tracking hardware. We ran experiments to evaluate how gaze estimates from our method compare with eye-tracking data. First, we evaluated crowdsourced estimates for three common types of information visualizations and basic visualization tasks using Amazon Mechanical Turk (MTurk). In another, we reproduced findings from a previous eye-tracking study on tree layouts using our method on MTurk. Results from these experiments show that fixation estimates using Fauxvea are qualitatively and quantitatively similar to eye tracking on the same stimulus-task pairs. These findings suggest that crowdsourcing visual analysis tasks with static information visualizations could be a viable alternative to traditional eye-tracking studies for visualization research and design