TY - GEN
T1 - Meta parallel coordinates for visualizing features in large, high-dimensional, time-varying data
AU - Dasgupta, Aritra
AU - Kosara, Robert
AU - Gosink, Luke
PY - 2012
Y1 - 2012
N2 - Managing computational complexity and designing effective visual representations are two important challenges for the visualization of large, complex, high-dimensional datasets. Parallel coordinates are an effective technique for visualizing high-dimensional data, but do not scale well to very large datasets. The addition of the temporal dimension leads to more uncertainty due to clutter on screen. Consequently, this poses a significant challenge for visually finding trends and patterns that maximize insight about the underlying time-varying properties of the data. To address these problems, we present meta parallel coordinates, a parallel coordinates display that is guided by perceptually motivated visual metrics. These metrics describe the visual structures typically found in parallel coordinates and thus aid the user's analysis by providing meaningful views of the data. Since they are computed in screen space, our metrics are computationally more efficient than data-based metrics. Our choice of metrics is driven by the different analytical tasks that a user typically wants to perform with time-varying multivariate data. In particular, we have worked with domain scientists who performed simulations of bioremediation experiments, and use their data and results to demonstrate the usefulness of our approach.
AB - Managing computational complexity and designing effective visual representations are two important challenges for the visualization of large, complex, high-dimensional datasets. Parallel coordinates are an effective technique for visualizing high-dimensional data, but do not scale well to very large datasets. The addition of the temporal dimension leads to more uncertainty due to clutter on screen. Consequently, this poses a significant challenge for visually finding trends and patterns that maximize insight about the underlying time-varying properties of the data. To address these problems, we present meta parallel coordinates, a parallel coordinates display that is guided by perceptually motivated visual metrics. These metrics describe the visual structures typically found in parallel coordinates and thus aid the user's analysis by providing meaningful views of the data. Since they are computed in screen space, our metrics are computationally more efficient than data-based metrics. Our choice of metrics is driven by the different analytical tasks that a user typically wants to perform with time-varying multivariate data. In particular, we have worked with domain scientists who performed simulations of bioremediation experiments, and use their data and results to demonstrate the usefulness of our approach.
UR - http://www.scopus.com/inward/record.url?scp=84872174667&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872174667&partnerID=8YFLogxK
U2 - 10.1109/LDAV.2012.6378980
DO - 10.1109/LDAV.2012.6378980
M3 - Conference contribution
AN - SCOPUS:84872174667
SN - 9781467347334
T3 - IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings
SP - 85
EP - 89
BT - IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings
T2 - 2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012
Y2 - 14 October 2012 through 19 October 2012
ER -