IEEEVis Summary

• Main Visualization conference, along with CHI, and Eurovis
• Organized by IEEE
• About 1000+ attendees

Subconferences

• Infovis -> Techniques
• VAST -> The process
• Scivis -> Rainbows
• VizSec -> Security
• VDS -> Big data
• Workshops/Tutorials -> Special interests
• Panels -> Debate
  • There was a special panel on how Scivis is dying

Observed themes

• VegaLite
• Network Visualization (Edge Bundling)
• Text Visualization
• Time Series
• Machine Learning
• Education, Design, Evaluation

Network Visualization

** Evaluation of Graph Sampling: A Visualization Perspective (J)

Authors: Yanhong Wu, Nan Cao, Daniel Archambault, Qiaomu Shen, Huamin Qu, and Weiwei Cui

Graph sampling, how good are them for showing visual properties

* CUBu: Universal real-time bundling for large graphs (T)

Authors: Matthew van der Zwan, Valeriu Codreanu, Alexandru Telea

Very fast GPU based edge bundling, that works with millions of nodes

Geo

Surprise! Bayesian Weighting for De-Biasing Thematic Maps (J)

Authors: Michael Correll and Jeffrey Heer

*** Many-to-Many Geographically-Embedded Flow Visualisation: An Evaluation (J) (Best Paper Honorable Mention)

Authors: Yalong Yang, Tim Dwyer, Sarah Goodwin, and Kim Marriott

Very novel. Good evaluation

*** Small Multiples with Gaps (J)

Authors: Wouter Meulemans, Jason Dykes, Aidan Slingsby, Cagatay Turkay, and Jo Wood

Small Multiples

Text Visualization

TextTile: An Interactive Visualization Tool for Seamless Exploratory Analysis of Structured Data and Unstructured Text (J)

Authors: Cristian Felix, Anshul Vikram Pandey, and Enrico Bertini

Good replacement for wordclouds, ML

Time series

*** The Connected Scatterplot for Presenting Paired Time Series (T)

Authors: Steve Haroz, Robert Kosara, Steven L. Franconeri

** Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus (T)

Authors: Fan Du, Ben Shneiderman, Catherine Plaisant, Sana Malik, Adam Perer

List of strategies on how to declutter time series

Others Time Series

Visplause: Visual Data Quality Assessment of Many Time Series Using Plausibility Checks (J)

Authors: Clemens Arbesser, Florian Spechtenhauser, Thomas Mühlbacher, and Harald Piringer

Multi-Granular Trend Detection for Time-Series Analysis (J)

van Goethem, Frank Staals, Maarten Löffler, Jason Dykes, and Bettina Speckmann

Shape Grammar Extraction for Efficient Query-by-Sketch Pattern Matching in Long Time Series (C)

Muthumanickam, Katerina Vrotsou, Matthew Cooper, Jimmy Johansson

The Semantics of Sketch: A Visual Query System for Time Series Data (C)

Correll, Michael Gleicher

- SocialBrands: Visual Analysis of Public Perceptions of Brands on Social Media (C)

Liu, Anbang Xu, Liang Gou, Haibin Liu, Rama Akkiraju, Han-Wei Shen

Not the best visual encoding

Education, Design, Evaluation

Design

*** Iterating Between Tools to Create and Edit Visualizations (J)

Authors: Alex Bigelow, Steven Drucker, Danyel Fisher, and Miriah Meyer

** Data-Driven Guides: Supporting Expressive Design for Information Graphics (J)

Authors: Nam Wook Kim, Eston Schweickart, Zhicheng Liu, Mira Dontcheva, Wilmot Li, Jovan Popovic, and Hanspeter Pfister

Education

VIZITCARDS: A Card-Based Toolkit for Infovis Design Education (J)

Authors: Shiqing He and Eytan Adar

A framework for applying viz

VLAT: Development of a Visualization Literacy Assessment Test (J)

Authors: Sukwon Lee, Sung-Hee Kim, and Bum Chul Kwon

A test for visualization literacy, first approach, incomplete

Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations (J)

Authors: Mona Hosseinkhani Loorak, Charles Perin, Christopher Collins, and Sheelagh Carpendale

Interaction

** Visualization by Demonstration: An Interaction Paradigm for Visual Data Exploration (J)

Authors: Bahador Saket, Hannah Kim, Eli T. Brown, and Alex Endert

** HindSight: Encouraging Exploration through Direct Encoding of Personal Interaction History (J)

Authors: Mi Feng, Cheng Deng, Evan M. Peck, and Lane Harrison

Evaluation

The Elicitation Interview Technique: Capturing People’s Experiences of Data Representations (T)

Authors: Trevor Hogan, Uta Hinrichs, Eva Hornecker

Machine Learning

• Topic Modeling
• Dimmensionality Reduction