01 Mar 2019

Selective Wander Join: Fast Progressive Visualizations for Data Joins


Marianne Procopio, Carlos Scheidegger, Eugene Wu, Remco Chang. Selective Wander Join: Fast Progressive Visualizations for Data Joins. Informatics, 2019.

  title={Selective Wander Join: Fast Progressive Visualizations for Data Joins},
  author={Procopio, Marianne and Scheidegger, Carlos and Wu, Eugene and Chang, Remco},
  organization={Multidisciplinary Digital Publishing Institute}


Progressive visualization offers a great deal of promise for big data visualization; however, current progressive visualization systems do not allow for continuous interaction. What if users want to see more confident results on a subset of the visualization? This can happen when users are in exploratory analysis mode but want to ask some directed questions of the data as well. In a progressive visualization system, the online aggregation algorithm determines the database sampling rate and resulting convergence rate, not the user. In this paper, we extend a recent method in online aggregation, called Wander Join, that is optimized for queries that join tables, one of the most computationally expensive operations. This extension leverages importance sampling to enable user-driven sampling when data joins are in the query. We applied user interaction techniques that allow the user to view and adjust the convergence rate, providing more transparency and control over the online aggregation process. By leveraging importance sampling, our extension of Wander Join also allows for stratified sampling of groups when there is data distribution skew. We also improve the convergence rate of filtering queries, but with additional overhead costs not needed in the original Wander Join algorithm.