Capturing User Intent when Brushing in Scatterplots

Intent-Inference screenshot

Abstract

Being able to capture or predict a user's intent behind a brush in a visualization tool has important implications in two scenarios. First, predicting intents can be used to auto-complete a partial selection in a mixed-initiative approach, with potential benefits to selection speed, correctness, and confidence. Second, capturing the intent of a selection can be used to improve recall, reproducibility, and even re-use. Augmenting provenance logs with semi-automatically captured intents makes it possible to save the reasoning behind selections. In this paper, we introduce a method to infer intent for selections and brushes in scatterplots. We first introduce a taxonomy of types of patterns that users might specify, which we elicited in a formative study conducted with professional data analysts and scientists. Based on this, we identify algorithms that can classify these patterns, and introduce various approaches to score the match of each pattern to an analyst's selection of items. We introduce a system that implements these methods for scatterplots and ranks alternative patterns against each other. Analysts then can use these predictions to auto-complete partial selections, and to conveniently capture their intent and provide annotations, thus making a concise representation of that intent available to be stored as provenance data. We evaluate our approach using interviews with domain experts and in a quantitative crowd-sourced study, in which we show that using auto-complete leads to improved selection accuracy for most types of patterns.

Citation

BibTeX

@article{2020_preprint_intent,
  title = {Capturing User Intent when Brushing in Scatterplots},
  author = {Kiran Gadhave and Jochen Görtler and Zach Cutler and Carolina Nobre and Oliver Deussen and Miriah Meyer and Jeff Phillips and Alexander Lex},
  booktitle = {Preprint},
  doi = {10.31219/osf.io/mq2rk},
  year = {2020}
}

Acknowledgements

We thank the domain experts we interviewed for their time and their willingness to provide datasets, and Lane Harrison and members of the Visualization Design Lab for feedback. We gratefully acknowledge funding by the National Science Foundation (IIS 1751238) and by the Deutsche Forschungsgemeinschaft (251654672-TRR 161).