Steering-by-example for Progressive Visual Analytics

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 meantto focus computational resources on data subspaces of interest, so as to ensure their computation is completed before allothers. Yet, current approaches to select a region of the view space and then to prioritize its corresponding data subspaceeither require a 1-to-1 mapping between view and data space, or they need to establish and maintain computationally costlyindex structures to trace complex mappings between view and data space. We present steering-by-example, a novel interactivesteering approach for progressive visual analytics, which allows prioritizing data subspaces for the progression by generatinga relaxed query from a set of selected data items. Our approach works independently of the particular visualization techniqueand without additional index structures. First benchmark results show that steering-by-example considerably improvesPrecision and Recall for prioritizing unprocessed data for a selected view region, clearly outperforming random uniformsampling.