mQAPViz: A divide-and-conquer multi-objective optimization algorithm to compute large data visualizations
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Modern digital products and services are instrumental in understanding users activities and behaviors. In doing so, we have to extract relevant relationships and patterns from extensive data collections efficiently. Data visualization algorithms are essential tools in transforming data into narratives. Unfortunately, very few visualization algorithms can handle a significant amount of data. In this study, we address the visualization of large-scale 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 machine learning sampling techniques and efficient data structures to scale to millions of data objects. The divide-and-conquer strategy can efficiently handle millions of objects which the algorithm allocates onto a layout that allows the visualization of a whole dataset. Experimental results on real-world and large datasets demonstrate that mQAPViz is a competitive alternative to compute large-scale visualizations that we can employ to inform the development and improvement of digital applications.
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