Abstract:

In this talk, I will make the case for a first-principles approach to machine learning over relational databases that exploits recent development in database systems and theory. The input to learning classification and regression models is defined by feature extraction queries over relational databases. The mainstream approach to learning over relational data is to materialize the results of the feature extraction query, export it out of the database, and then learn over it using statistical software packages. These three steps are expensive and unnecessary. Instead, one can cast the machine learning problem as a database problem, keeping the feature extraction query unmaterialized and using a new generation of meta-algorithms to push the learning through the query. The performance of this approach benefits tremendously from structural properties of the relational data and of the feature extraction query; such properties may be algebraic (semi-ring), combinatorial (hypertree width), or statistical (sampling). Performance is further improved by leveraging recent advances in compiler technology that eliminate the cost of abstraction and allows us to specialize the computation for specific workloads and datasets. This translates to several orders-of-magnitude speed-up over state-of-the-art systems.

This work is done by my colleagues at RelationalAI and by members of our faculty research network, including Dan Olteanu (Oxford), Maximilian Schleich (Oxford), Ben Moseley (CMU), and XuanLong Nguyen (Michigan).

Biography:

Molham Aref is the Chief Executive Officer of RelationalAI. He has more than 28 years of experience in leading organisations that develop and implement high value machine learning and artificial intelligence solutions across various industries. Prior to RelationalAI he was CEO of LogicBlox and Predictix (now Infor), Optimi (now Ericsson), and co-founder of Brickstream (now FLIR). Molham held senior leadership positions at HNC Software (now FICO) and Retek (now Oracle).