Avoiding drill-down fallacies with VisPilot: assisted exploration of data subsets

Abstract

As datasets continue to grow in size and complexity, exploring multi-dimensional datasets remain challenging for analysts. A common operation during this exploration is drill-down-understanding the behavior of data subsets by progressively adding filters. While widely used, in the absence of careful attention towards confounding factors, drill-downs could lead to inductive fallacies. Specifically, an analyst may end up being "deceived" into thinking that a deviation in trend is attributable to a local change, when in fact it is a more general phenomenon; we term this the drill-down fallacy. One way to avoid falling prey to drill-down fallacies is to exhaustively explore all potential drill-down paths, which quickly becomes infeasible on complex datasets with many attributes. We present VisPilot, an accelerated visual data exploration tool that guides analysts through the key insights in a dataset, while avoiding drill-down fallacies. Our user study results show that VisPilot helps analysts discover interesting visualizations, understand attribute importance, and predict unseen visualizations better than other multidimensional data analysis baselines.