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

Abstract

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.

Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM

“…For example, an 'update rate' of 3Hz would be su cient for most scienti c applications, allowing full display of around 11 million 16-dimensional points, or 6 million 32-dimensional points. The data display capacity may be further improved using similar tradeo s (e.g., dynamic sub-sampling of the data) up until the throughput of the graphical rendering pipeline becomes a bottleneck [26].…”

The industry engagement ladder

“…This may be to provide industrial domain expert validation for a particular project. As an example, we cite joint publications between us and QPC Ltd., a call centre infrastructure provider (Rees et al, 2018(Rees et al, , 2019(Rees et al, , 2020Roberts et al, 2016Roberts et al, , 2018aRoberts et al, , b, 2019.…”

“…Roberts et al (2016),Rees et al (2018),Roberts et al (2018b),Roberts et al (2018a),Rees et al (2019),Roberts et al (2019),Rees et al (2020) together researchers at university (and surroundings) with a common interest in visual computing. This event encourages informal discussion, open exchange and personal interaction in order to foster both research directions in visual computing and a sense of local community.…”