Explaining Query Answers with Explanation-Ready Databases

With the increased generation and availability of big data in different domains, there is an imminent requirement for data analysis tools that are able to 'explain' the trends and anomalies obtained from this data to a range of users with different backgrounds. Wu-Madden (PVLDB 2013) and Roy-Suciu (SIGMOD 2014) recently proposed solutions that can explain interesting or unexpected answers to simple aggregate queries in terms of predicates on attributes. In this paper, we propose a generic framework that can support much richer, insightful explanations by preparing the database offline, so that top explanations can be found interactively at query time. The main idea in such explanation-ready databases is to pre-compute the effects of potential explanations (called interventions), and efficiently re-evaluate the original query taking into account these effects. We formalize this notion and define an explanation-query that can evaluate all possible explanations simultaneously without having to run an iterative process, develop algorithms and optimizations, and evaluate our approach with experiments on real data.