HypDB: a demonstration of detecting, explaining and resolving bias in OLAP queries - Salimi, Babak; Cole, Corey; Li, Peter; Gehrke, Johannes; Suciu, Dan (Proceedings of the VLDB Endowment, 2018)
On line analytical processing (OLAP) is an essential element of decision-support systems. However, OLAP queries can be biased and lead to perplexing and incorrect insights. In this demo, we present HypDB, the first system to detect, explain and resolve bias in OLAP queries. Our demonstration, shows several examples of OLAP queries from real world datasets that are biased and could lead to statistical anomalies such as Simpson's paradox. Then, we demonstrate step-by-step how HypDB: (1) detects whether an OLAP query is biased, (2) explains the root causes of the bias and reveals illuminating insights about the domain and the data collection process and (3) eliminates the bias via query rewriting and generates decision-support insights.