RQL: A Query Language for Rule Discovery in Databases

Promoting declarative approaches in data mining is a long standing theme, the main idea being to simplify as much as possible the way data analysts interact with their data. This paper goes into this direction by proposing a well-founded logical query language, SafeRL, allowing the expression of a wide variety of rules to be discovered against a database. By rules, we mean statements of the form “if …then …”, as defined in logics for “implications” between boolean variables. As a consequence, SafeRL extends and generalizes functional dependencies to new and unexpected rules. We provide a query rewriting technique and a constructive proof of the main query equivalence theorem, leading to an efficient query processing technique. From SafeRL, we have devised RQL, a user-friendly SQL-like query language. We have shown how a tight integration can be performed on top of any relational database management system. Every RQL query turns out to be seen as a query processing problem, instead of a particular rule mining problem. This approach has been implemented and experimented on sensor network data. A web prototype has been released and is freely available (http://rql.insa-lyon.fr). Data analysts can upload a sample of their data, write their own RQL queries and get answers to know whether or not a rule holds (if not, a counterexample from the database is displayed) and much more.