Metaphorical Visualization: Mapping Data to Familiar Concepts

Abstract

We present a new approach to visualizing data that is well-suited for personal and casual applications. The idea is to map the data to another dataset that is already familiar to the user, and then rely on their existing knowledge to illustrate relationships in the data. We construct the map by preserving pairwise distances or by maintaining relative values of specific data attributes. This metaphorical mapping is very flexible and allows us to adapt the visualization to its application and target audience. We present several examples where we map data to different domains and representations. This includes mapping data to cat images, encoding research interests with neural style transfer and representing movies as stars in the night sky. Overall, we find that although metaphors are not as accurate as the traditional techniques, they can help design engaging and personalized visualizations. CCS CONCEPTS• Human-centered computing → Visualization techniques; Visualization theory, concepts and paradigms; • Computing methodologies → Machine learning.

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

“…However, from another perspective, embeddings open up opportunities for creating personal visualization [HTAA * 15] that enables data analysis in a personal context. Metaphorical Visualization by Tkachev et al [TCS * 22] proposes an inspiring VA+embeddings approach that uses embeddings to create metaphors. They link one data entity to another via distance‐based mapping derived from ML embedding spaces along with other mapping algorithms.…”