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Visualizing sets to reveal relationships between constituent elements is a complex representational problem. Recent research presents several automated placement and grouping techniques to highlight connections between set elements. However, these techniques do not scale well for sets with cardinality greater than one hundred elements. We present OnSet, an interactive, scalable visualization technique for representing large-scale binary set data.

This study describes a data-driven visualization approach to the systemic study of innovations in global supply chain networks. We demonstrate its applicability with illustrative examples of real-world supply chain in the electronics industry. Our visualization approach enhances the hypothesis-generating process as it can reveal important clusters, patterns, trends, and outliers in the networks.

This is a collaboration with our industry partner The Home Depot seeking to optimize the current curbside process from the associate perspective.