Demonstration of Santoku: Optimizing Machine Learning over Normalized Data

Advanced analytics is a booming area in the data management industry and a hot research topic. Almost all toolkits that implement machine learning (ML) algorithms assume that the input is a single table, but most relational datasets are not stored as single tables due to normalization. Thus, analysts often join tables to obtain a denormalized table. Also, analysts typically ignore any functional dependencies among features because ML toolkits do not support them. In both cases, time is wasted in learning over data with redundancy. We demonstrate Santoku, a toolkit to help analysts improve the performance of ML over normalized data. Santoku applies the idea of factorized learning and automatically decides whether to denormalize or push ML computations through joins. Santoku also exploits database dependencies to provide automatic insights that could help analysts with exploratory feature selection. It is usable as a library in R, which is a popular environment for advanced analytics. We demonstrate the benefits of Santoku in improving ML performance and helping analysts with feature selection.