A Data-Driven Platform for the Coordination of Independent Visual Analytics Tools

By Lars Nonnemann, Marius Hogräfer, Martin Röhlig, Bodo Urban, Heidrun Schumann, and Hans-Jörg Schulz (2022)

This paper is an extended version of our prior work (see below).


Visual analysis of unknown data requires the combined use of various functions that are often part of standalone visual analytics (VA) tools. Performing cross-tool visual analysis with standalone VA tools, however, is a challenging and cumbersome endeavor. Some dedicated frameworks address this issue, yet in order to utilize any of them, a visual analytics tool needs to support their required API or architecture.

Contrary to most existing frameworks, we present an approach that does not rely on a single predefined interchange mechanism for the entire ensemble of VA tools. Instead, we propose using any available channel for data exchange between two consecutive VA tools. This allows mixing and matching of different data exchange strategies over the course of a cross- tool analysis.

In this paper, we identify the challenges associated with establishing such tool chaining platform for data-driven coordination. We further describe the structure and capabilities of data exchange and explain various functionalities of our platform in detail. Based on a demonstrating example, we discuss the limitations of our approach and elaborate new insight for the coordination of the visual output of multiple VA tools.

Cite this work:

  author = {Lars Nonnemann and Marius Hogr\"{a}fer and Martin R\"{o}hlig and Heidrun Schumann and Bodo Urban and Hans-J\"{o}rg Schulz},
  title = {A data-driven platform for the coordination of independent Visual Analytics tools},
  journal = {Computers \& Graphics},
  year = {2022},
  issn = {0097-8493},
  doi = {10.1016/j.cag.2022.05.023},
  keywords = {Human-centered computing, Visualization systems and tools, Information systems, Data exchange}

Customizable Coordination of Independent Visual Analytics Tools

By Lars Nonnemann, Marius Hogräfer, Bodo Urban, Heidrun Schumann, and Hans-Jörg Schulz (2021)


While it is common to use multiple independent analysis tools in combination, it is still cumbersome to carry out a cross-toolvisual analysis. Some dedicated frameworks addressing this issue exist, yet in order to use them, a Visual Analytics tool mustsupport their API or architecture.

In our paper, we do not rely on a single predetermined exchange mechanism for the wholeensemble of VA tools. Instead, we propose using any available channel for exchanging data between two subsequently usedVA tools. This effectively allows to mix and match different data exchange strategies within one cross-tool analysis, whichconsiderably reduces the overhead of adding a new VA tool to a given tool ensemble. We demonstrate our approach with a firstimplementation called AnyProc and its application to a use case of three VA tools in a Health IT data analysis scenario.


An integral component to our use case are ReVize, a visualization toolchaining library based on Vega-Lite, and ReVizeServer, an between-tool exchange mechanism for Vega-Lite specifications. Below, you find links to the repositories of both components.

Toolchain-Enabled Tools

Below, we list links to code of the toolchaining-enabled tools we used in our use case.

About this Project

We presented our concepts in our paper "Customizable Coordination of Independent Visual Analytics Tools" on EuroVA 2021. We kindly ask you to refer to the following citation in your papers referring to this work.

  title = {Customizable Coordination of Independent Visual Analytics Tools},
  booktitle = {Proceedings of the 12th International {EuroVis} Workshop on Visual Analytics ({EuroVA}'21)},
  publisher = {Eurographics Association},
  author = {Nonnemann, Lars and Hogr\"afer, Marius and Urban, Bodo and Schumann, Heidrun and Schulz, Hans-J\"org},
  editor = {Bernard, J\"urgen and Vrotsou, Katerina},
  year = {2021},
  pages = {25--29}