N. Hosokawa, et al: A Scalable “Exploranation” Technique for Hierarchically Indexed Table Data - VINCI 2020

Natsuki Hosokawa, Kohei Arimoto and Ken Wakita: “A Scalable ‘Exploranation’ Technique for Hierarchically Indexed Table Data,” The 13th International symposium on Visual Information communication and interaction (VINCI 2020)

*Abstract: Data analytics tools that combine automated text generation and visualization techniques suffer from scalability problems. The amount of the generated text explodes with the increase of items and attributes. This study addresses this problem for table data, whose attributes and data items are hierarchically organized. In our approach, the user’s point of view is modeled by the dual focalization axis, which consists of the attribute-based and the data-item-based focal points. The user can refine the two-dimensional focal points to obtain the chart and the text that explain the data facts found in a more focused portion of the dataset. The proposal’s efficacy was assessed through a quantitative and qualitative evaluation using a prototype visual analytics tool that employs the idea.