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Technical Perspective: Visualization Search: From Sketching to Natural Language


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Visualization enables effective data exploration by harnessing the higher bandwidth interactivity of the human visual cortex. But the space of possible visualizations is immense, such that general abstractions for creating (that is, finding) the right visualization are elusive, despite recent progress in systems like vega2 and Draco.1

The following paper provides a general abstraction, along with advanced interfaces, focusing on visualization search. If you have ever created a long sequence of visualizations looking for interesting patterns, you have manually performed a visualization search task. The visualization search problem is to find subsets of the data that, when suitably rendered, generate a visualization like a provided pattern specification. This task is intuitively difficult, requiring at least a model of visualization similarity, a representation of a massive search space, a strategy for navigating the search space, and appropriate interfaces through which users can express specifications. The authors approach these challenges by designing a shape query algebra consisting of primitives and operators that can describe complex functions; that is, "trend lines." The algebra assembles complex patterns from simple segments, akin to the way complex continuous functions are approximated by assemblies of piecewise linear functions in engineering and science (for example, finite element models).


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