| Autor: |
Lars Nonnemann, Marius Hogräfer, Martin Röhlig, Heidrun Schumann, Bodo Urban, Hans-Jörg Schulz |
| Přispěvatelé: |
Publica |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Nonnemann, L, Hogräfer, M, Röhlig, M, Schumann, H, Urban, B & Schulz, H-J 2022, ' A Data-Driven Platform for the Coordination of Independent Visual Analytics Tools ', Computers & Graphics, vol. 106, pp. 152-160 . https://doi.org/10.1016/j.cag.2022.05.023 |
| ISSN: |
0097-8493 |
| DOI: |
10.1016/j.cag.2022.05.023 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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Full Text from ScienceDirect