Large Scale Machine Learning with the SimSQL System

In this talk, I'll describe the SimSQL system, which is a platform for
writing and executing machine learning codes. Since SimSQL is at its
heart a relational database system, it designed to support data
independence. That is, the same declarative statistical inference
codes can be used regardless of data set size, compute hardware, and
physical data storage and distribution across machines. One concern
is that a platform supporting data independence in this way will not
perform well. But we've done extensive experimentation, and have
found that SimSQL performs as well as other competitive platforms that
support writing and executing machine learning codes for large data
sets.
Chris Jermaine is an associate professor of computer science at
Rice University. He is the recipient of an Alfred P. Sloan Foundation
Research Fellowship, a National Science Foundation CAREER award, and
an ACM SIGMOD Best Paper Award. In his spare time, Chris enjoys
outdoor activities such as hiking, climbing, and whitewater boating.
In one particular exploit, Chris and his wife floated a whitewater
raft (home-made from scratch using a sewing machine, glue, and
plastic) over 100 miles down the Nizina River (and beyond) in Alaska.