=Paper=
{{Paper
|id=Vol-2368/invited1
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2368/invited1.pdf
|volume=Vol-2368
|dblpUrl=https://dblp.org/rec/conf/datalog/Aref19
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2368/invited1.pdf</pdf>
<pre>
              Relational Artificial Intelligence

                                  Molham Aref

                               RelationalAI, USA
                             molham.aref@gmail.com

    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. We cast the
machine learning problem as a database problem by decomposing the learning
task into a batch of aggregates over the feature extraction query and by com-
puting this batch over the input database. 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), com-
binatorial (hypertree width), or statistical (sampling). This translates to several
orders-of-magnitude speed-up over state-of-the-art systems. This work is based
on collaboration with Hung Q. Ngo (RelationalAI), Mahmoud Abo-Khamis (Re-
lationalAI), Ryan Curtin (RelationalAI), Dan Olteanu (Oxford), Maximilian
Schleich (Oxford), Ben Moseley (CMU), and XuanLong Nguyen (Michigan) and
other members of the RelationalAI team and faculty network.

</pre>