Visual exploratory analysis of large-scale databases often relies on precomputed query results in order to guarantee interactivity with the visualization system. This is especially true when the query requires joining tables across the database since join is one of the most computationally expensive operations and in the worst case requires joining every row of one table to every row of the other table. Advances in approximate query processing enable quick look summary statistics, but with limitations to the types and complexities of the queries. A recent advancement in approximate query processing is a technique called Wander Join, that performs random walks across these joins, resulting in faster convergence on aggregation values. However, this online aggregation technique is not tailored for visualization tasks that often involve filtering on one or more conditions or viewing aggregation values across multiple groups such as in bar charts. In both cases, the convergence rate is slowed since more samples are needed in order to find records that pass the filters, resulting in the user waiting longer for a confident result.
To address this issue, we propose a generalization of the Wander Join algorithm that improves the convergence rate for visualization queries involving filtering and viewing aggregation. We implemented this improved version of Wander Join that we call Load-n-Go and compared it to the original, specifically in the context of visual analysis tasks. Our evaluation finds that our algorithm outperforms Wander Join by reducing the sample complexity. Load-n-Go requires up to 50% fewer samples for group by queries, and up to 85% fewer samples for filtering queries. Such reduced sampling complexity can represent up to 2x and 6x speedups respectively for visual exploratory systems using the Load-n-Go algorithm.