Towards Scalable Visual Data Wrangling via Direct Manipulation
Data wrangling, the process of cleaning, transforming, and preparing data for analysis, is a well-known bottleneck in data science workflows. A wide range of data wrangling techniques have been proposed to mitigate this challenge. Of particular interest are visual data wrangling tools, in which users prepare data via graphical interactions (such as with visualizations) rather than requiring them to write scripts. We develop a visual data wrangling system, Buckaroo, that expands upon this paradigm by enabling the automatic discovery of interesting groups (e.g., Salary values for Country="Buthan") and identification of anomalies (e.g., missing values, outliers, and type mismatches) both within and across these groups. Crucially, this allows users to reason about how repairs applied to one group affect other groups in the dataset. A central challenge in visual data wrangling is scalability. Rendering entire datasets is often infeasible, yet showing only a small sample risks hiding rare but critical errors across groups. We address these challenges through carefully designed sampling strategies that prioritize errors, as well as novel aggregation techniques that support pan-and-zoom interactions over large datasets. Buckaroo maintains efficient indexing data structures and differential storage to localize anomaly detection and minimize recomputation. We demonstrate the applicability of our approach via an integration with the Hopara pan-and-zoom engine (enabling multi-layered navigation over large datasets without sacrificing interactivity). Finally, we explore our system's usability (via an expert review) and its scalability, finding that this design seems well matched with the challenges of this domain.