In inductive databases, there is no conceptual difference between data and the models describing the data: both can be stored and queried using some query language. The approach that adheres most strictly to this philosophy is probably the one proposed by Calders et al. (2006): in this approach, models are stored in relational tables and queried using standard SQL. The approach has been described in detail for association rule discovery. In this work, we study how decision tree induction can be integrated in this approach. We propose a representation format for decision trees similar to the format proposed earlier for association rules, and queryable using standard SQL; and we present a prototype system in which part of the needed functionality is implemented. In particular, we have developed an exhaustive tree learning algorithm able to answer a wide range of constrained queries.