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.
- G. Burtini, S. Fazackerley, and R. Lawrence. Time series compression for adaptive chart generation. In CCECE, pages 1--6. IEEE, 2013.Google Scholar
- S. G. Eick and A. F. Karr. Visual scalability. Journal of Computational and Graphical Statistics, 11(1):22--43, 2002.Google ScholarCross Ref
- T. Fu. A review on time series data mining. EAAI Journal, 24(1):164--181, 2011. Google ScholarDigital Library
- J. Heer and B. Shneiderman. Interactive dynamics for visual analysis. ACM Queue, 10(2):30, 2012. Google ScholarDigital Library
- J. Hershberger and J. Snoeyink. Speeding up the Douglas-Peucker line-simplification algorithm. University of British Columbia, Department of Computer Science, 1992.Google ScholarDigital Library
- Z. Jerzak, T. Heinze, M. Fehr, D. Gröber, R. Hartung, and N. Stojanovic. The DEBS 2012 Grand Challenge. In DEBS, pages 393--398. ACM, 2012. Google ScholarDigital Library
- U. Jugel, Z. Jerzak, G. Hackenbroich, and V. Markl. M4: A visualization-oriented time series data aggregation. In VLDB. VLDB Endowment, 2014. (submitted, review pending). Google ScholarDigital Library
- C. Mutschler, H. Ziekow, and Z. Jerzak. The DEBS 2013 Grand Challenge. In DEBS, pages 289--294. ACM, 2013. Google ScholarDigital Library
- W. Shi and C. Cheung. Performance evaluation of line simplification algorithms for vector generalization. The Cartographic Journal, 43(1):27--44, 2006.Google ScholarCross Ref
- B. Shneiderman. Extreme visualization: squeezing a billion records into a million pixels. In SIGMOD, pages 3--12. ACM, 2008. Google ScholarDigital Library
- M. Visvalingam and J. Whyatt. Line generalisation by repeated elimination of points. The Cartographic Journal, 30(1):46--51, 1993.Google ScholarCross Ref
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600--612, 2004. Google ScholarDigital Library
Recommendations
Visual Analytics Infrastructures: From Data Management to Exploration
Analysts exploring big data require more from information visualization, data analysis, and data management than these components can now deliver. New infrastructures must address the nature of exploration as well as data scale. The Web extra at http://...
An Insight-Based Longitudinal Study of Visual Analytics
Visualization tools are typically evaluated in controlled studies that observe the short-term usage of these tools by participants on preselected data sets and benchmark tasks. Though such studies provide useful suggestions, they miss the long-term ...
A Visual Analytics Approach to Understanding Spatiotemporal Hotspots
As data sources become larger and more complex, the ability to effectively explore and analyze patterns among varying sources becomes a critical bottleneck in analytic reasoning. Incoming data contain multiple variables, high signal-to-noise ratio, and a ...
Comments