TY  - CHAP
TI  - A Layered Approach to Lightweight Toolchaining in Visual Analytics
AU  - Schulz, Hans-Jörg
AU  - Röhlig, Martin
AU  - Nonnemann, Lars
AU  - Hogräfer, Marius
AU  - Aehnelt, Mario
AU  - Urban, Bodo
AU  - Schumann, Heidrun
T2  - Computer Vision, Imaging and Computer Graphics Theory and Applications
A2  - Claudio, Ana Paula
A2  - Bouatouch, Kadi
A2  - Chessa, Manuela
A2  - Paljic, Alexis
A2  - Kerren, Andreas
A2  - Hurter, Christophe
A2  - Tremeau, Alain
A2  - Farinella, Giovanni Maria
T3  - Communications in Computer and Information Science
AB  - The ongoing proliferation and differentiation of Visual Analytics to various application domains and usage scenarios has also resulted in a fragmentation of the software landscape for data analysis. Highly specialized tools are available that focus on one particular analysis task in one particular application domain. The interoperability of these tools, which are often research prototypes without support or proper documentation, is hardly ever considered outside of the toolset they were originally intended to work with. To nevertheless use and reuse them in other settings and together with other tools, so as to realize novel analysis procedures by using them in concert, we propose an approach for loosely coupling individual visual analytics tools together into toolchains. Our approach differs from existing such mechanisms by being lightweight in realizing a pairwise coupling between tools without a central broker, and by being layered into different aspects of such a coupling: the usage flow, the data flow, and the control flow. We present a model of this approach and showcase its usefulness with three different usage examples, each focusing on one of the layers.
PY  - 2020
SP  - 313
EP  - 337
PB  - Springer
SN  - 9783030415891
SV  - 1182
UR  - https://doi.org/10.1007/978-3-030-41590-7_13
ER  -