Taggle: Combining Overview and Details in Tabular Data Visualizations
This addresses the need for better tabular data exploration tools for domain experts like genomics researchers, though it appears incremental as it builds on existing spreadsheet-like and aggregation methods.
The paper tackles the problem of visualizing large tabular data by combining overview and detail views, presenting Taggle, a technique that visualizes each row individually with visual encodings and data-driven aggregation, demonstrated in a genomics case study for drug discovery.
Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle using a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.