上海科技大学知识管理系统

Modern data analytics tools often provide visualizations as an accessible data window to users in exploratory data analysis (EDA). Still, many analysts feel lost in this process due to issues such as the high complexity of data. Auto-insight recommendations offer a promising alternative by suggesting possible interpretations of the data to users during EDA but might impose undesirable effects on users. In this study, we systematically explore the "double-edged sword"effect of auto-insight recommendations on EDA in terms of exploration assistance, message reliability, and interference. Particularly, we design and develop two versions of a Tableau-like visualization system termed TurboVis: One supports auto-insight recommendations while the other does not. We first demonstrate how typical visualization specification tools can be augmented by incorporating auto-insight recommendations and then conduct a within-subjects user study with 18 participants during which they experience both versions in EDA tasks. We find that although auto-insight recommendations encourage more visualization inspections, they also introduce biases to data exploration. The perceived level of message reliability and interference of auto-insight recommendations depend on data familiarity and task structures. Our work elicits design implications for embedding auto-insight recommendations into the EDA process. © 2021 Copyright for this paper by its authors.