DataPilot: Utilizing Quality and Usage Information for Subset Selection during Visual Data Preparation
ACM CHI · 2023
★ Patent
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.
Citation
@inproceedings{narechania2023datapilot,
author = {Narechania, Arpit and Du, Fan and Sinha, Atanu R and Rossi, Ryan and Hoffswell, Jane and Guo, Shunan and Koh, Eunyee and Navathe, Shamkant B and Endert, Alex},
title = {{DataPilot: Utilizing Quality and Usage Information for Subset Selection during Visual Data Preparation}},
year = {2023},
isbn = {9781450394215},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3544548.3581509},
doi = {10.1145/3544548.3581509},
booktitle = {Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems},
articleno = {809},
numpages = {18},
location = {Hamburg, Germany},
series = {CHI '23}
}