Scotland C. Leman, Leanna L. House, Dipayan Maiti, Alex Endert, Chris North

Abstract

Typical data visualizations result from linear pipelines that start by characterizing data using a model or algorithm to reduce the dimension and summarize structure, and end by displaying the data in a reduced dimensional form. Sensemaking may take place at the end of the pipeline when users have an opportunity to observe, digest, and internalize any information displayed. However, some visualizations mask meaningful data structures when model or algorithm constraints (e.g., parameter specifications) contradict information in the data. Yet, due to the linearity of the pipeline, users do not have a natural means to adjust the displays. In this paper, we present a framework for creating dynamic data displays that rely on both mechanistic data summaries and expert judgement. The key is that we develop both the theory and methods of a new human-data interaction to which we refer as “ Visual to Parametric Interaction” (V2PI). With V2PI, the pipeline becomes bi-directional in that users are embedded in the pipeline; users learn from visualizations and the visualizations adjust to expert judgement. We demonstrate the utility of V2PI and a bi-directional pipeline with two examples.

People

leman-updated

Scotland C. Leman


house-updated

Leanna L. House


COC Faculty/Staff portraits at Klaus.

Alex Endert


Christopher L North, Associate Professor, Computer Science

Chris North


Publication Details

Date of publication:
March 20, 2013
Journal:
PLOS