DataPilot: Utilizing Quality and Usage Information for Subset Selection during Visual Data Preparation
Abstract
Selecting relevant data subsets from large, unfamiliar datasets can be difficult. We address this challenge by modeling and visualizing two kinds of auxiliary information: (1) quality - the validity and appropriateness of data required to perform certain analytical tasks; and (2) usage - the historical utilization characteristics of data across multiple users. Through a design study with 14 data workers, we integrate this information into a visual data preparation and analysis tool, DataPilot. DataPilot presents visual cues about "the good, the bad, and the ugly" aspects of data and provides graphical user interface controls as interaction affordances, guiding users to perform subset selection. Through a study with 36 participants, we investigate how DataPilot helps users navigate a large, unfamiliar tabular dataset, prepare a relevant subset, and build a visualization dashboard. We find that users selected smaller, effective subsets with higher quality and usage, and with greater success and confidence.
Cite
@article{arxiv.2303.01575,
title = {DataPilot: Utilizing Quality and Usage Information for Subset Selection during Visual Data Preparation},
author = {Arpit Narechania and Fan Du and Atanu R Sinha and Ryan A. Rossi and Jane Hoffswell and Shunan Guo and Eunyee Koh and Shamkant B. Navathe and Alex Endert},
journal= {arXiv preprint arXiv:2303.01575},
year = {2023}
}
Comments
18 pages, 5 figures, 1 table, ACM CHI 2023