computer science

IEEE Visualization

IEEE Visualization is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand IEEE Visualization rather than just read about it. In short: The IEEE Visualization Conference (VIS) is an annual conference on scientific visualization, information visualization, and visual analytics administrated by the IEEE Computer Society Technical Committee on Visualization and Graphics. As ranked by Google Scholar's h-index metric in 2016, VIS is the highest rated venue for visualization research and the second-highest rated conference for computer graphics over all.

Key takeaways

IEEE Visualization belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect IEEE Visualization to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of IEEE Visualization from memory before moving on to harder problems.

Reference excerpt

The IEEE Visualization Conference (VIS) is an annual conference on scientific visualization, information visualization, and visual analytics administrated by the IEEE Computer Society Technical Committee on Visualization and Graphics. As ranked by Google Scholar's h-index metric in 2016, VIS is the highest rated venue for visualization research and the second-highest rated conference for computer graphics over all. It has an 'A' rating from the Australian Ranking of ICT Conferences, an 'A' rating from the Brazilian ministry of education, and an 'A' rating from the China Computer Federation (CCF). The conference is highly selective with generally < 25% acceptance rates for all papers. An image dataset, VIS30K, has been created from figures and tables in the conference publications. In 2016, the VIS Executive Committee initiated a review of conference structures, which led to community consultations and the formation of a committee in 2019, which successfully consolidated the three conferences (SciVis, InfoVis, VAST) under one conference at VIS 2021. Since the VIS 2021 conference a new unified full paper track with six specific areas and a consolidated review process is used. The unification of the conference structure aims to streamline experiences, simplify organizational processes, enhance flexibility in the evolution of topics, and provide a cohesive view of visualization fields.

Location The conference is typically held at the end of October. In 2014, for its 25th anniversary, the conference took place for the first time outside of the US, namely in Paris. Since then, the conference typically rotates around the US and also moves international about every third year. List of conferences:

2027: Chicago, United States (planned) 2026: Boston, United States (planned) 2025: Vienna, Austria 2024: St. Pete Beach, Florida, United States (moved online due to Hurricane Milton) 2023: Melbourne, Australia 2022: Oklahoma City, United States (hybrid) 2021: New Orleans, United States (online) 2020: Salt Lake City, United States (online) 2019: Vancouver, Canada 2018: Berlin, Germany 2017: Phoenix, Arizona, United States 2016: Baltimore, Maryland, United States 2015: Chicago, Illinois, United States 2014: Paris, France 2013: Atlanta, Georgia, United States 2012: Seattle, Washington, United States 2011: Providence, Rhode Island, United States 2010: Salt Lake City, Utah, United States 2009: Atlantic City, New Jersey, United States 2008: Columbus, Ohio, United States 2007: Sacramento, California, United States 2006: Baltimore, Maryland, United States 2005: Minneapolis, Minnesota, United States 2004: Austin, Texas, United States 2003: Seattle, Washington, United States 2002: Boston, Massachusetts, United States 2001: San Diego, California, United States 2000: Salt Lake City, Utah, United States 1999: San Francisco, California, United States 1998: Research Triangle Park, North Carolina, United States 1997: Phoenix, Arizona, United States 1996: San Francisco, California, United States 1995: Atlanta, Georgia, United States 1994: Washington DC, United States 1993: San Jose, California, United States 1992: Boston, Massachusetts, United States 1991: San Diego, California, United States 1990: San Francisco, California, United States

Awards

VIS Best Paper Award 2024:

Entanglements for Visualization: Changing Research Outcomes through Feminist Theory: Derya Akbaba, Lauren Klein, Miriah Meyer Aardvark: Composite Visualizations of Trees, Time-Series, and Images: Devin Lange, Robert L Judson-Torres, Thomas A Zangle, Alexander Lex VisEval: A Benchmark for Data Visualization in the Era of Large Language Models: Nan Chen, Yuge Zhang, Jiahang Xu, Kan Ren, Yuqing Yang VADIS: A Visual Analytics Pipeline for Dynamic Document Representation and Information Seeking: Rui Qiu, Yamei Tu, Po-Yin Yen, Han-Wei Shen Rapid and Precise Topological Comparison with Merge Tree Neural Networks: Yu Qin, Brittany Terese Fasy, Carola Wenk, Brian Summa 2023:

Affective Visualization Design: Leveraging the Emotional Impact of Data: Xingyu Lan, Yanqiu Wu, Nan Cao Fast Compressed Segmentation Volumes for Scientific Visualization: Max Piochowiak, Carsten Dachsbacher Swaying the Public? Impacts of Election Forecast Visualizations on Emotion, Trust, and Intention in the 2022 U.S. Midterms: Fumeng Yang, Mandi Cai, Chloe Rose Mortenson, Hoda Fakhari, Ayse Deniz Lokmanoglu, Jessica Hullman, Steven Franconeri, Nicholas Diakopoulos, Erik Nisbet, Matthew Kay TimeSplines: Sketch-based Authoring of Flexible and Idiosyncratic Timelines: Anna Offenwanger, Matthew Brehmer, Fanny Chevalier, Theophanis Tsandilas Visualization of Discontinuous Vector Field Topology: Egzon Miftari, Daniel Durstewitz, Filip Sadlo Vortex Lens: Interactive Vortex Core Line Extraction using Observed Line Integral Convolution: Peter Rautek, Xingdi Zhang, Bernhard Woschizka, Thomas Theussl, Markus Hadwiger 2022:

Affective Learning Objectives for Communicative Visualizations: Elsie Lee-Robbins, Eytan Adar Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations: Lace Padilla, Racquel Fygenson, Spencer C. Castro, Enrico Bertini Uncertainty-Aware Multidimensional Scaling: David Hägele, Tim Krake, Daniel Weiskopf 2021:

Feature Curves and Surfaces of 3D Asymmetric Tensor Fields: Shih-Hsuan Hung, Yue Zhang, Harry Yeh, Eugene Zhang IRVINE: Using Interactive Clustering and Labeling to Analyze Correlation Patterns: A Design Study from the Manufacturing of Electrical Engines: Joscha Eirich, Jakob Bonart, Dominik Jäckle, Michael Sedlmair, Ute Schmid, Kai Fischbach, Tobias Schreck, Jürgen Bernard Perception! Immersion! Empowerment! Superpowers as Inspiration for Visualization: Wesley Willett, Bon Adriel Aseniero, Sheelagh Carpendale, Pierre Dragicevic, Yvonne Jansen, Lora Oehlberg, Petra Isenberg Simultaneous Matrix Orderings for Graph Collections: Nathan van Beusekom, Wouter Meulemans, Bettina Speckmann 2020:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with IEEE Visualization

Start with the simplest possible case. Write down what IEEE Visualization claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to IEEE Visualization before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about IEEE Visualization ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of IEEE Visualization

In research: IEEE Visualization appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses IEEE Visualization in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: IEEE Visualization is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computer science conferences, IEEE conferences, IEEE society and council awards, so understanding it makes those chapters shorter.
In everyday life: Look for IEEE Visualization outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study IEEE Visualization in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what IEEE Visualization means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain IEEE Visualization out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is IEEE Visualization in simple terms?

Why does IEEE Visualization matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study IEEE Visualization?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on IEEE Visualization.