Visual Methods for Analyzing Time-Oriented Data
By Wolfgang Aigner, Silvia Miksch, Wolfgang Müller, H. Schumann, Christian Tominski. IEEE Transactions on Visualization and Computer Graphics, 2008
328 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/2716ecc96daf5ac9dedd8805845a2691df968f92
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- H. Schumann
- Christian Tominski
- Natalia V. Andrienko
- Florian Mansmann
- Geoffrey P. Ellis
- Gennady L. Andrienko
- Daniel A. Keim
- Christian Beecks
In Computer Vision and Pattern Recognition
- Gennady L. Andrienko
- Natalia V. Andrienko
- Daniel A. Keim
- Geoffrey P. Ellis
- Florian Mansmann
- H. Schumann
- Jörn Kohlhammer
- Christian Tominski
In Signal Processing
- Gennady L. Andrienko
- Natalia V. Andrienko
- Daniel A. Keim
- Geoffrey P. Ellis
- H. Schumann
- Christian Tominski
- Niklas Elmqvist
- Jean-Daniel Fekete
Citations in the corpus
- Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, unknown): “A number of surveys give a good overview [13], [14], [15], [16], [17], [18].”
- Space, time and visual analytics (2010, background): “A wide repertoire of interactive techniques for visualizing datasets with temporal components is available in the field of information visualisation (Aigner et al. 2008).”
- Visual Analytics: Definition, Process, and Challenges (2008, unknown): “Again, visualization of time-related data is important to arrive at good analysis results [1].”
- Mastering the Information Age - Solving Problems with Visual Analytics (2010, background): “Switching to a cyclic spiral representation (right) makes an inherent cyclic (weekly) pattern apparent[3]”