Name
Abstract | ||
|---|---|---|
State-of-the-art visual data analysis tools ignore bandwidth limitations. They fetch millions of records of high-volume time series data from an underlying RDBMS to eventually draw only a few thousand pixels on the screen. In this work, we demonstrate a pixel-aware big data visualization system that dynamically adapts the number of data points transmitted and thus the data rate, while preserving pixel-perfect visualizations. We show how to carefully select the data points to fetch for each pixel of a visualization, using a visualization-driven data aggregation that models the visualization process. Defining all required data reduction operators at the query level, our system trades off a few milliseconds of query execution time for dozens of seconds of data transfer time. The results are significantly reduced response times and a near real-time visualization of millions of data points. Using our pixel-aware system, the audience will be able to enjoy the speed and ease of big data visualizations and learn about the scientific background of our system through an interactive evaluation component, allowing the visitor to measure, visualize, and compare competing visualization-related data reduction techniques. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.14778/2733004.2733066 | PVLDB |
Field | DocType | Volume |
Data point,Data mining,Computer science,Visualization,Visual analytics,Interactive visual analysis,Pixel,Big data,Data aggregator,Database,Data reduction | Journal | 7 |
Issue | ISSN | Citations |
13 | 2150-8097 | 5 |
PageRank | References | Authors |
0.38 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Uwe Jugel | 1 | 84 | 7.94 |
| Zbigniew Jerzak | 2 | 308 | 22.62 |
| Gregor Hackenbroich | 3 | 178 | 16.18 |
| Volker Markl | 4 | 2245 | 182.37 |