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.
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