Name
Abstract | ||
|---|---|---|
Scatter Plots are one of the most powerful and most widely used techniques for visual data exploration. A well-known problem is that scatter plots often have a high degree of overlap, which may occlude a significant portion of the data values shown. In this paper, we propose the generalized scatter plot technique, which allows an overlap-free representation of large data sets to fit entirely into the display. The basic idea is to allow the analyst to optimize the degree of overlap and distortion to generate the bestpossible view. To allow an effective usage, we provide the capability to zoom smoothly between the traditional and our generalized scatter plots. We identify an optimization function that takes overlap and distortion of the visualization into acccount. We evaluate the generalized scatter plots according to this optimization function, and show that there usually exists an optimal compromise between overlap and distortion. Our generalized scatter plots have been applied successfully to a number of real-world IT services applications, such as server performance monitoring, telephone service usage analysis and financial data, demonstrating the benefits of the generalized scatter plots over traditional ones. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1057/ivs.2009.34 | Information Visualization |
Keywords | Field | DocType |
high degree,generalized scatter plot,large data,optimization function,scatter plot,visual data exploration,financial data,generalized scatter plot technique,telephone service usage analysis,effective usage,interpolation,smoothing,interactions,distortion | Data mining,Data set,Computer science,Interpolation,Artificial intelligence,Distortion,Computer vision,Performance monitoring,Visualization,Algorithm,Zoom,Smoothing,Scatter plot | Journal |
Volume | Issue | ISSN |
9 | 4 | 1473-8716 |
Citations | PageRank | References |
25 | 1.02 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel A. Keim | 1 | 7704 | 1141.60 |
| Ming C. Hao | 2 | 25 | 1.02 |
| Umeshwar Dayal | 3 | 8452 | 2538.92 |
| Halldor Janetzko | 4 | 312 | 20.69 |
| Peter Bak | 5 | 276 | 18.09 |