QPJVis Demo: Quality-Boost Progressive Join Query Processing System

Abstract

Progressive query processing enables data scientists to efficiently analyze and explore large datasets. Data scientists can start further analyses earlier if the progressive result can represent the complete results well. Most progressive processing frameworks carefully control which parts of the input to process in order to improve the quality of progressive results. The input control strategies work well when the data are processed uniformly. However, the progressive results will be biased towards the join keys if the processed data are not uniform. A recently proposed input&output framework named QPJ corrects the bias by temporarily hiding some results. The framework dynamically estimates the distribution of the complete result and outputs progressive results with a similar distribution to the estimated complete result. This demo presents QPJVis, which is a progressive query processing system designed to inherently process the progressive queries using the QPJ framework. Additionally, we also implement an input control framework, Prism, in QPJVis so that users can compare the difference between the input&output framework and a purely input framework.