Michael Hohenstein

"Leveraging Approximate Query Processing to Realize Progressive Visual Analytics"

Progressive Visual Analytics is a relatively new paradigm in the realm of visualization. It's main objective is to develop algorithms and infrastructure to support analysts in exploratory ad hoc data analysis. This means each query should return an (approximate) result in an upper time bound, so that the data exploration can be considered a real-time process. Additionally, the analyst should be able to steer the query by tuning parameters of the computation. The keystone of the progressive paradigm is to instantly return an approximate result which is (progressively) updated in the background. Ideally some notion of (partial) re-use of earlier results that intersect with a live query should be in place to further reduce the time to return of later queries. PVA is closely related to approximate query processing and streaming applications with a focus on real-time interactivity, which is usually reached by (partial) re-use of prior results, data- or process chunking, sampling and using fast algorithms that replace exact computations with approximate results. We want to observe the paradigm of progressive data science through the lens of database systems, trying to improve and tailor existing approaches to create an efficient infrastructure for PVA systems.

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