pmc-vis as a visualization tool for probabilistic models and it supports the exploration of large multivariate decision graphs, formal verification, and debugging.


Core Publication

Run the Application / Get the Prototype

Our tool pmc-vis is free and open source.

Tool and Walkthrough Videos

The following video was used for onboarding and training purposes in a qualitative user study on pmc-vis.

Previous Versions of pmc-vis

With a previous version of pmc-vis, we also prepared videos illustrating usage scenarios.

Scenario 1: Intro to the Server Management System + Finding good initial configurations

Scenario 2: Understanding the model behavior by finding patterns

Scenario 3: Reconfiguration and schedulers

Related Student Theses

  • Vinzenz Fuhrmann

    Concepts for Progressive Large Multivariate Graph Comparison

    Vinzenz Fuhrmann September 4th, 2025 until February 5th, 2026

    Supervision: Julián Méndez, Raimund Dachselt

  • Clarisa Sanjaya

    Concepts for Linking Partial Degree-of-Interest Graphs for Large, Multivariate Graph Visualization and Exploration

    Clarisa Sanjaya October 16th, 2023 until April 1st, 2024

    Supervision: Julián Méndez, Raimund Dachselt

  • Philipp McAllister

    Design Concepts for Pathfinding and Cycle Avoidance during Exploration of Large Multivariate Graphs

    Philipp McAllister January 31st, 2025 until April 17th, 2025

    Supervision: Julián Méndez, Raimund Dachselt

  • Lea Meding

    Developing concepts for visual, interactive, and scalable graph comparison

    Lea Meding May 1st, 2023 until October 2nd, 2023

    Supervision: Julián Méndez, Raimund Dachselt

Acknowledgements

This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy: EXC-2068, 390729961 – Cluster of Excellence “Physics of Life” and EXC 2050/1, 390696704 – Cluster of Excellence “Centre for Tactile Internet” (CeTI) of TU Dresden, by DFG grant 389792660 as part of TRR 248 – CPEC (see https://cpec.science) and by the Federal Ministry of Research, Technology and Space (BMFTR, SCADS22B) and Saxon State Ministry for Science, Culture and Tourism (SMWK) by funding the competence center for Big Data and AI “ScaDS.AI Dresden/Leipzig”.