Interactive Visualization of Streaming Data with Kernel Density Estimation
Ove Daae Lampe, Helwig Hauser
INPROCEEDINGS,
Proceedings of the IEEE Pacific Visualization Symposium (PacificVis 2011),
March, 2011
Abstract
In this paper, we discuss the extension and integration
of the statistical concept of Kernel Density Estimation (KDE) in a
scatterplot-like visualization for dynamic data at interactive rates.
We present a line kernel for representing streaming data, we discuss
how the concept of KDE can be adapted to enable a continuous
representation of the distribution of a dependent variable of a
2D domain. We propose to automatically adapt the kernel bandwith of
KDE to the viewport settings, in an interactive visualization
environment that allows zooming and panning. We also present a
GPU-based realization of KDE that leads to interactive frame rates,
even for comparably large datasets. Finally, we demonstrate the
usefulness of our approach in the context of three application
scenarios -- one studying streaming ship traffic data, another one
from the oil and gas domain, where process data from the operation
of an oil rig is streaming in to an on-shore operational center, and
a third one studying commercial air traffic in the US spanning 1987 to 2008.
Published
Proceedings of the IEEE Pacific Visualization Symposium (PacificVis 2011)
Media
BibTeX
@inproceedings{lampe11kde,
title = {Interactive Visualization of Streaming Data with Kernel Density Estimation},
author = {Ove Daae Lampe and Helwig Hauser},
year = {2011},
booktitle = {Proceedings of the IEEE Pacific Visualization Symposium (PacificVis 2011)},
abstract = {In this paper, we discuss the extension and integration
of the statistical concept of Kernel Density Estimation (KDE) in a
scatterplot-like visualization for dynamic data at interactive rates.
We present a line kernel for representing streaming data, we discuss
how the concept of KDE can be adapted to enable a continuous
representation of the distribution of a dependent variable of a
2D domain. We propose to automatically adapt the kernel bandwith of
KDE to the viewport settings, in an interactive visualization
environment that allows zooming and panning. We also present a
GPU-based realization of KDE that leads to interactive frame rates,
even for comparably large datasets. Finally, we demonstrate the
usefulness of our approach in the context of three application
scenarios -- one studying streaming ship traffic data, another one
from the oil and gas domain, where process data from the operation
of an oil rig is streaming in to an on-shore operational center, and
a third one studying commercial air traffic in the US spanning 1987 to 2008.},
pages = {171--178},
month = {March},
location = {Hong Kong},
URL = {http://dx.doi.org/10.1109/PACIFICVIS.2011.5742387},
}
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