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Introduction to IATK: An Immersive Visual Analytics toolkit

Sunday, Oct 21: 9:00am-12:40pm

Maxime Cordeil, Monash University, Melbourne, Australia
Andrew Cunningham, University of South Australia, Adelaide, Australia
Tim Dwyer, Monash University, Melbourne, Australia
Kim Marriott, Monash University, Melbourne, Australia
Bruce H. Thomas, University of South Australia, Mawson Lakes, South Australia, Australia

Immersive Analytics is an emerging interdisciplinary research area that investigates the use of nontraditional display and input technology to immerse users in their data. A prominent aspect this research investigates is immersive data visualization, which uses augmented and virtual reality technology to visually immerse users in their data to facilitate collaboration, exploration and understanding. Currently, there is a lack of simple tools to build interactive data visualizations in immersive environments. The de facto approach is to use off-the-shelf game engines because they allow easy prototyping of 3D user interfaces in these immersive environments. However, game engines do not consider the specialized requirements of data visualization, such as the type and structure of datasets, the breadth of data, especially in the age of ``big data’’, and typical information visualization tasks. IATK: Immersive Analytics Toolkit is an open source visualization toolkit for the Unity game engine that fills this gap. Specifically, IATK:

  • supports an infovis pipeline for virtual and augmented reality environments;
  • visualizes large (up to 1 million) data points at an optimal framerate for immersive applications; and,
  • provides a technology-agnostic model for user interactions with immersive visualizations. This tutorial will introduce participants to the Unity game engine and teach practical skills for implementing immersive data visualisations using IATK.

Everything except the chart 2018

Sunday, Oct 21: 9:00am-12:40pm

Dominikus Baur, Independent Researcher, Munich, Germany
Moritz Stefaner

Browsers have become a popular platform for native visualizations. Frameworks such as d3.js enable the creation of interactive graphics that are easy to distribute, can reach a wide audience and need no additional software installed. However, once one moves beyond simple charts and aims to implement and evaluate novel visualization techniques - as is often the case in research - knowing one’s way around d3.js is no longer enough. Seemingly secondary aspects such as optimizing graphical performance, handling application states, making visualizations shareable, findable and useable across devices often make or break a successful visualization. While there are many support and training materials available for d3.js and related frameworks, information on these surrounding details is harder to find. Researchers and practitioners benefit from them as they speed up the process of building prototypes, can improve the user experience, e.g., when setting up an online study, enable easier sharing and make presentations appear more professional.

This half-day tutorial is an updated version of the very popular ”Everything except the chart” tutorial which happened for the first time at VIS 2014 in Paris. Since the web is a fast-paced medium, the 2018 state of the art brings many important changes. With several years of experience as freelance visualization developers and on the basis of their successful Better Life Index, Rhythm of Food and Wahl 2Q17 projects, the authors will provide insights into details to consider when building a successful web-based visualization. This tutorial is aimed at researchers and practitioners with intermediate experience in web-based visualizations, but can also provide an entry point for novices to this topic, to learn the proper environment before diving into creating the actual content.

Tutorial on Comparative Visualization: Interactive Designs and Algorithms Depending on Data and Tasks

Sunday, Oct 21: 2:20pm-6:00pm

Tatiana von Landesberger, TU Darmstadt, Germany
Kathrin Ballweg, Technische Universität, Darmstadt, Germany
Hans-Jörg Schulz, Aarhus University, Aarhus, Denmark
Natalie Kerracher, Edinburgh Napier University, Edinburgh, United Kingdom
Margit Pohl, Vienna University of Technology, Vienna, Austria

Data comparison in various domains can be effectively supported by visual analytics solutions combining interactive visualization and algorithmic analysis. The design of such solutions should match the comparison problem at hand: the input data and the task specification. This requires several choices from algorithm to visual design and interaction. Such design choices also need to consider human perception capabilities. Our tutorial presents how the differences in data and task characterizations influence visual-analytical solution designs. We will first present a conceptual framework, which defines a set of dimensions along which the comparison problem is defined. We then show how this specification influences the comparative solution design both in theory and using real world examples. Our tutorial provides visualization designers with a means to systematize domain problem analysis and to learn which algorithms, visual designs and interactions to use when, also taking into consideration human perception and cognition capabilities. The tutorial is held at a beginner to an intermediate level.

Tutorial on Recent Feature Tracking Techniques

Sunday, Oct 21: 2:20pm-6:00pm

Hanqi Guo, Argonne National Laboratory, Lemont, Illinois, United States
Harsh Bhatia, Lawrence Livermore National Laboratory, Livermore, California, United States
Tino Weinkauf, KTH Royal Institute of Technology, Stockholm, Sweden
Gunther H. Weber, Lawrence Berkeley National Laboratory, Berkeley, California, United States
Han-Wei Shen, The Ohio State University, Columbus, Ohio, United States

The purpose of this tutorial is to review feature tracking, which is a traditional but still core research topic in scientific visualization. In general, feature tracking aims at characterizing and understanding the evolution of features in time-varying scientific datasets, including scalar and vector fields. The tutorial has two main sessions: the first session will review general techniques including statistics, topology, and combinatorial based feature tracking algorithms; the second session will then present specific feature tracking algorithms for complex-valued scalar fields and flow fields. The tutorial will also organize a short panel discussion on future research directions on feature tracking.

Topological Data Analysis Made Easy with the Topology ToolKit (TTK)

Sunday, Oct 21: 2:20pm-6:00pm

Guillaume Favelier, LIP6, Paris, France
Charles Gueunet Kitware, Lyon, France
Attila Gyulassy, University of Utah, Salt Lake City, Utah, United States
Julien, Jomier, Kitware, Lyon, France
Joshua A. Levine, University of Arizona, Tucson, Arizona, United States
Jonas Lukasczyk, Technische Universität Kaiserslautern, Kaiserslautern, Germany
Daisuke Sakurai, Zuse Institute, Berlin, Germany
Maxime Soler, Total S.A., Pau
Julien Tierny, CNRS, Paris, France
Will Usher, University of Utah, Salt Lake City, Utah, United States
Qi Wu, University of Utah, Salt Lake City, Utah, United States

This tutorial presents topological methods for the analysis and visualization of scientific data from a user’s perspective, with the Topology ToolKit (TTK), a recently released open-source library for topological data analysis. Topological methods have gained considerably in popularity and maturity over the last twenty years and success stories of established methods have been documented in a wide range of applications (combustion, chemistry, astrophysics, material sciences, etc.) with both acquired and simulated data, in both post-hoc and in-situ contexts. While reference textbooks have been published on the topic, no tutorial at IEEE VIS has covered this area in recent years, and never at a software level and from a user’s point-of-view. This tutorial fills this gap by providing a beginner’s introduction to topological methods for practitioners, researchers, students, and lecturers. In particular, instead of focusing on theoretical aspects and algorithmic details, this tutorial focuses on how topological methods can be useful in practice for concrete data analysis tasks such as segmentation, feature extraction or tracking. This tutorial mostly targets students, practitioners and researchers who are not experts in topological methods but who are interested in using them in their daily tasks. We also target researchers already familiar to topological methods and who are interested in using or contributing to TTK.

Urban Trajectory Data Visualization

Monday, Oct 22: 9:00am-12:40pm

Ye Zhao, Kent State University, Kent, Ohio, United States
Jing Yang, UNCC, Charlotte, North Carolina, United States
Wei Chen, Zhejiang University, Hangzhou, China
Shamal AL-Dohuki, Kent State University, Kent, Ohio, United States

Advanced sensing technologies and computing infrastructures are producing massive trajectory data of people and vehicles in urban spaces at an unprecedented scale and speed. With the prevalent GPS, Wi-Fi, Cellular, and RFID devices, population mobility information is accurately recorded as the moving paths of taxis, fleets, public transits, and mobile phones. The information can be utilized in the studies of urban systems, environment, economy, and citizens to optimize urban planning, improve human life quality and environment, and amend city operations.

In this tutorial, our major goal is to help visualization researchers and practitioners in the development of visualization systems of big trajectory datasets. Our tutorial contents will focus on important and practical topics people usually face when developing a visualization system of urban trajectories including:

  • Trajectory data representation, processing, indexing, and data queries
  • Trajectory data visualization tasks, challenges, and techniques
  • Developing web-based interactive visualization system
  • Case studies of urban visual analytics with shared source codes and examples We expect the audience of the tutorial not only gain knowledge about the visualization of urban trajectory data but also achieve experiences in implementing real-world visualization systems.

Cost-benefit Analysis in Visualization: Theory and Practice

Monday, Oct 22: 2:20pm-6:00pm

Min Chen, University of Oxford, Oxford, United Kingdom

In this half-day tutorial (level: beginning to intermediate), we focus on the topic of analyzing the cost-benefit of visualization and visual analytics systems. The tutorial is built on the recent informationtheoretic developments in visualization, including the paper on the cost-benefit metric by Chen and Golan (2016) and the subsequent research effort to evident, falsify, and apply this proposition in more experimental and practical contexts. The main aim of this tutorial is to introduce this new theoretical concept from practical perspectives. The delivery of the tutorial will be structured in the order of “from practice to theory and then to practice again”. It will begin with examples of questions and problems arising from practical studies and applications, demonstrating the common trade-offs in designing visualization and visual analytics solutions. It will then move to a brief introduction of the main concepts of information theory and the cost-benefit metric by Chen and Golan (2016) with the aid of illustrative examples closely related to visualization. This is followed by a detailed examination of the symptoms, causes, and potential treatments of some common problems identified by the cost-benefit analysis. The audience will be invited to pose their own case studies and offer their cost-benefit analysis. The application of information theory to visualization is often regarded as an advanced topic and the previous tutorials on this topic were all categorized as “intermediate to advanced”. One objective of this tutorial is to help remove the barrier for visualization researchers to enter this arena, while examining the broad scope of future directions of research.

Storyboards for Science: Combining the Visual and Verbal to Create Engaging Communication

Monday, Oct 22: 2:20pm-6:00pm

David Rogers, Los Alamos National Laboratory, Los Alamos, New Mexico, United States
Francesca Samsel, University of Texas, Austin, Texas, United States
Sean Cunningham, The University of Texas, Austin, Texas, United States Benjamin Bach, University of Edinburgh

Communicating science - the results from experiments, simulations and visualizations is challenging on many levels, but it is critical to everything we do. If it is true that we are ‘wired for story’, then developing narratives to connect people with your science is critical. But what are the elements of narrative - of story - that can be used to present science this way? Drawing on the experience of writers, artists, and videographers with decades of experience in science communication, we present hands-on, example-based approach to developing narratives through storyboarding - a visual method of developing the structure, imagery and narrative of engaging stories. Participants gain experience in the skills and processes needed to develop engaging stories about their science. This includes not just building a narrative but also the visualizations and supporting visual elements that help connect with an audience. This methodology can be applied to a variety of presentations - everything from videos to scientific papers.