Parameter Space Visualization for Large-scale Datasets Using Parallel Coordinate Plots

Abstract

Visualization is an important task in data analytics, as it allows researchers to view patterns within the data instead of reading through extensive raw data. Allowing the ability to interact with the visualizations is an essential aspect since it provide the ability to intuitively explore data to find meaning and patterns more efficiently. Interactivity, however, becomes progressively more difficult as the size of the dataset increases. This project begins by leveraging existing web-based data visualization technologies and extends their functionality through the use of parallel processing. This methodology utilizes state-ofthe-art techniques, such as Node.js, to split the visualization rendering and user interactivity controls between a client-server infrastructure without having to rebuild the visualization technologies. The approach minimizes data transfer by performing the rendering step on the server while allowing for the use of HPC systems to render the visualizations more quickly. In order to improve the scaling of the system with larger datasets, parallel processing and visualization optimization techniques are used. This work will use parameter space data generated from mindmodeling.org to showcase our methodology for handling large-scale datasets while retaining interactivity and user friendliness.