Steering-by-example for Progressive Visual Analytics

Abstract

Progressive visual analytics allows users to interact with early, partial results of long-running computations on large datasets. In this context, computational steering is often brought up as a means to prioritize the progressive computation. This is meant to focus computational resources on data subspaces of interest so as to ensure their computation is completed before all others. Yet, current approaches to select a region of the view space and then to prioritize its corresponding data subspace either require a one-to-one mapping between view and data space, or they need to establish and maintain computationally costly index structures to trace complex mappings between view and data space. We present steering-by-example, a novel interactive steering approach for progressive visual analytics, which allows prioritizing data subspaces for the progression by generating a relaxed query from a set of selected data items. Our approach works independently of the particular visualization technique and without additional index structures. First benchmark results show that steering-by-example considerably improves Precision and Recall for prioritizing unprocessed data for a selected view region, clearly outperforming random uniform sampling.