Grand Unified Grammar of Graphics (GUGOG)
A Workshop at IEEE VIS 2026
Declarative, modular, and compositional approaches to visualization specification—collectively known as graphical or visualization grammars—have proliferated across visualization research and practice. However, despite sharing intellectual roots in Wilkinson’s Grammar of Graphics, these systems differ substantially in their abstractions, primitives, design goals, and target users.
GUGOG is a half-day hybrid workshop at IEEE VIS 2026 that brings together researchers and practitioners working on grammar-based visualization to seek a unified view of this diverse landscape. The workshop provides a dedicated forum for cross-system comparison, meta-theoretical reflection, community consensus building, and practitioner perspectives—structured opportunities that the main conference program rarely affords.
Read the full workshop proposal here.
GUGOG is held in conjunction with IEEE VIS 2026 and co-organized with the Munich Center for Machine Learning (MCML).
Tentative Schedule
The workshop runs as a half-day hybrid session on November 9, 2026. The programme below is tentative and subject to change.
Session 1 — 1:00-2:30 PM
| Time | Item |
|---|---|
| 1:00 | Paper Talks |
| 1:30 | Panel discussion — Towards a Universal Visualisation Language |
| 2:05 | Audience questions |
| 2:20 | Discussion group topic collection |
Coffee break — 2:30-3:00 PM
Discussion topics collection continues through the break.
Session 2 — 3:00-4:30 PM
| Time | Item |
|---|---|
| 3:00 | Paper Talks |
| 3:25 | Discussion group formation |
| 3:35 | Discussion groups |
| 4:10 | Report back, synthesis, call to action |
| 4:25 | Closing |
Call for Submissions
The call for submissions closed on August 1, 2026, and accepted authors have been notified. The call is kept below for reference. The workshop itself remains open to all IEEE VIS attendees, in person and remote — no submission required to attend.
We invited submissions to the Grand Unified Grammar of Graphics (GUGOG) workshop at IEEE VIS 2026. This is a half-day hybrid workshop open to both in-person and remote participants.
Scope of Topics
Workshop topics include, but are not limited to:
- Grammar evaluation methodology
- Comparative analyses of existing grammar systems
- User perspectives on grammar-based tools
- Implementation and design reflections
- Critical reflections on
- Domain-specific grammars
- Formal and algebraic foundations
- Cross-disciplinary perspectives
- Novel research directions
- Understanding grammar extensions and ecosystems
- Learnability, adoption, and natural language interfaces
Submission Types
The workshop accepted two types of non-archival submissions:
- Short position papers (less than 4 pages)
- Conceptual or philosophical arguments about grammar-based visualization. System descriptions will only be considered if they are illustrative prototypes closely tied to a specific conceptual argument.
- Discussion abstracts (up to 500 words)
- Provocations, opinions, and observations on all aspects of grammar-based visualization research.
Submission Guidelines
- Submissions are not anonymous: authors must include their full names, emails, and affiliations.
- Accepted submissions will be hosted on this website and are not considered archival—authors are encouraged to reuse content in follow-up publications.
- Authors are encouraged to share their work via open-access services.
- At least one author per accepted submission must register for the conference.
Timeline
| Call opens | May 15, 2026 |
| Submission deadline | Aug 1, 2026 |
| Notifications | Sep 1, 2026 |
| Camera-ready | Oct 7, 2026 |
| Workshop | Nov 9, 2026 |
Submission Portal
The PCS submission system is no longer accepting submissions for the VIS 2026 GUGOG track. Accepted authors should submit camera-ready versions there by October 7, 2026.
In Conjunction With
GUGOG is a half-day workshop held in conjunction with IEEE VIS 2026 in Boston, Massachusetts, November 9, 2026. The workshop is open to all IEEE VIS attendees, in person and remote.
GUGOG is co-organized with and supported by the Munich Center for Machine Learning (MCML), the joint AI research center of LMU Munich and TU Munich, and one of six national AI Competence Centers in Germany, funded by the German Federal Ministry of Research, Technology and Space and the Free State of Bavaria.