  The query log of a DBMS is a powerful resource. It enables many practical
applications, including query optimization and user experience enhancement. And
yet, mining SQL queries is a difficult task. The fundamental problem is that
queries are symbolic objects, not vectors of numbers. Therefore, many popular
statistical concepts, such as means, regression, or decision trees do not
apply. Most authors limit themselves to ad hoc algorithms or approaches based
on neighborhoods, such as k Nearest Neighbors. Our project is to challenge this
limitation. We introduce methods to manipulate SQL queries as if they were
vectors, thereby unlocking the whole statistical toolbox. We present three
families of methods: feature maps, kernel methods, and Bayesian models. The
first technique directly encodes queries into vectors. The second one
transforms the queries implicitly. The last one exploits probabilistic
graphical models as an alternative to vector spaces. We present the benefits
and drawbacks of each solution, highlight how they relate to each other, and
make the case for future investigation.
