Tree visualization with tree-maps: 2-d space-filling approach
ACM Transactions on Graphics (TOG)
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics
The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics
Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
InfoCrible: edition interactive de visualisations compactes
IHM '02 Proceedings of the 14th French-speaking conference on Human-computer interaction (Conférence Francophone sur l'Interaction Homme-Machine)
A Framework for Visualizing Information (Human-Computer Interaction Series)
A Framework for Visualizing Information (Human-Computer Interaction Series)
Automated Analysis of CLP(FD) Program Execution Traces
ICLP '02 Proceedings of the 18th International Conference on Logic Programming
InfoCrible: edition interactive de visualisations compactes
IHM '02 Proceedings of the 14th French-speaking conference on Human-computer interaction (Conférence Francophone sur l'Interaction Homme-Machine)
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
Taxinomie de représentations graphiques dynamiques
IHM '07 Proceedings of the 19th International Conference of the Association Francophone d'Interaction Homme-Machine
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We introduce a descriptive model that allows the definition of a large class of information visualization algorithms with a small number of parameters. Compact visualizations, which we conjecture is equivalent to the class of visualizations that can be rendered in a time directly proportional to the size of the input data, are defined by a fixed dataflow architecture: clustering and subclustering of input data, sort, graphic primitives and graphic attributes generation. At each step, the parameters are expressions of the host programming language, which include input attribute names and local variable names. Local variables are a specific concept that allows us to extend the expressiveness of the dataflow architecture. We introduce our model formally, and then show its expressiveness with a few examples.