Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations
In this paper, we describe our concepts to visualize very large amounts of multidimensional data. Ou...
For decades, researchers in information visualisation and graph drawing have focused on developing t...
This thesis project set out to find and implement a comfortable way to explore vast, multidimensiona...
Abstract — Larger, higher resolution displays can be used to increase the scalability of information...
Abstract: During the last decade Visual Exploration and Visual Data Mining techniques have proven to...
International audienceWe introduce a conceptual model for scalability designed for visualization res...
During the last decade Visual Exploration and Visual Data Mining techniques have proven to be of hig...
Making sense of large graph datasets is a fundamental and challenging process that advances science,...
Many real-world networks are multivariate, i.e., they have attributes associated with nodes and/or e...
In this era of information explosion, data analysis plays a crucial role in decision making across d...
Many real-world networks are multivariate, i.e., they have attributes associated with nodes and/or e...
In this paper, we describe our concepts to visualize very large amounts of multidimensional data. Ou...
To extend the scope of multivariate data visualization, the notion of comparative visualization is i...
Interaction is a vital component in the visualization of multivariate networks. It enables greater a...
The sizes of today\u27s scientific datasets range from megabytes to terabytes, making it impossible ...
In this paper, we describe our concepts to visualize very large amounts of multidimensional data. Ou...
For decades, researchers in information visualisation and graph drawing have focused on developing t...
This thesis project set out to find and implement a comfortable way to explore vast, multidimensiona...
Abstract — Larger, higher resolution displays can be used to increase the scalability of information...
Abstract: During the last decade Visual Exploration and Visual Data Mining techniques have proven to...
International audienceWe introduce a conceptual model for scalability designed for visualization res...
During the last decade Visual Exploration and Visual Data Mining techniques have proven to be of hig...
Making sense of large graph datasets is a fundamental and challenging process that advances science,...
Many real-world networks are multivariate, i.e., they have attributes associated with nodes and/or e...
In this era of information explosion, data analysis plays a crucial role in decision making across d...
Many real-world networks are multivariate, i.e., they have attributes associated with nodes and/or e...
In this paper, we describe our concepts to visualize very large amounts of multidimensional data. Ou...
To extend the scope of multivariate data visualization, the notion of comparative visualization is i...
Interaction is a vital component in the visualization of multivariate networks. It enables greater a...
The sizes of today\u27s scientific datasets range from megabytes to terabytes, making it impossible ...
In this paper, we describe our concepts to visualize very large amounts of multidimensional data. Ou...
For decades, researchers in information visualisation and graph drawing have focused on developing t...
This thesis project set out to find and implement a comfortable way to explore vast, multidimensiona...