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Feature-Rich, GPU-Assisted Scatterplots for Millions of Call Events

Department of Computer Science, Swansea University, Bay Campus, Swansea SA1 8EN, UK
QPC Ltd., The Harlech Building, Theater Clwyd Complex, Flintshire CH7 1YA, UK
Author to whom correspondence should be addressed.
Computers 2019, 8(1), 12;
Received: 14 January 2019 / Revised: 1 February 2019 / Accepted: 2 February 2019 / Published: 5 February 2019
The contact center industry represents a large proportion of many country’s economies. For example, 4% of the entire United States and UK’s working population is employed in this sector. As in most modern industries, contact centers generate gigabytes of operational data that require analysis to provide insight and to improve efficiency. Visualization is a valuable approach to data analysis, enabling trends and correlations to be discovered, particularly when using scatterplots. We present a feature-rich application that visualizes large call center data sets using scatterplots that support millions of points. The application features a scatterplot matrix to provide an overview of the call center data attributes, animation of call start and end times, and utilizes both the CPU and GPU acceleration for processing and filtering. We illustrate the use of the Open Computing Language (OpenCL) to utilize a commodity graphics card for the fast filtering of fields with multiple attributes. We demonstrate the use of the application with millions of call events from a month’s worth of real-world data and report domain expert feedback from our industry partner. View Full-Text
Keywords: information visualization; call-center data; big-data information visualization; call-center data; big-data
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MDPI and ACS Style

Rees, D.; Roberts, R.C.; Laramee, R.S.; Brookes, P.; D’Cruze, T.; Smith, G.A. Feature-Rich, GPU-Assisted Scatterplots for Millions of Call Events. Computers 2019, 8, 12.

AMA Style

Rees D, Roberts RC, Laramee RS, Brookes P, D’Cruze T, Smith GA. Feature-Rich, GPU-Assisted Scatterplots for Millions of Call Events. Computers. 2019; 8(1):12.

Chicago/Turabian Style

Rees, Dylan, Richard C. Roberts, Roberts S. Laramee, Paul Brookes, Tony D’Cruze, and Gary A. Smith 2019. "Feature-Rich, GPU-Assisted Scatterplots for Millions of Call Events" Computers 8, no. 1: 12.

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