mQAPViz: A divide-and-conquer multi-objective optimization algorithm to compute large data visualizations

TL;DR

mQAPViz is a scalable divide-and-conquer algorithm that transforms large datasets into visualizations by framing the task as a multi-objective quadratic assignment problem, employing machine learning sampling and data structures.

Contribution

It introduces a novel multi-objective optimization approach for large-scale data visualization using divide-and-conquer strategies and advanced sampling techniques.

Findings

Handles datasets with millions of objects efficiently

Produces competitive visualizations compared to existing methods

Scales well with large, real-world datasets

Abstract

Algorithms for data visualizations are essential tools for transforming data into useful narratives. Unfortunately, very few visualization algorithms can handle the large datasets of many real-world scenarios. In this study, we address the visualization of these datasets as a Multi-Objective Optimization Problem. We propose mQAPViz, a divide-and-conquer multi-objective optimization algorithm to compute large-scale data visualizations. Our method employs the Multi-Objective Quadratic Assignment Problem (mQAP) as the mathematical foundation to solve the visualization task at hand. The algorithm applies advanced sampling techniques originating from the field of machine learning and efficient data structures to scale to millions of data objects. The algorithm allocates objects onto a 2D grid layout. Experimental results on real-world and large datasets demonstrate that mQAPViz is a competitive alternative to existing techniques.