When learning froIn very large databases, the reduction of complexity is of highest importance. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a most simple hypothesis language and so to be capable of very fast learning on real-world databases. The opposite extreme is to select a small data set and be capable of learning very expressive (first-order logic) hypotheses. A multistrategy approach allows to combine most of the advantages and exclude most of the disadvantages. More simple learning algorithms detect hierarchies that are used in order to structure the hypothesis space for a more complex learning algorithm. The better structured the hypothesis space is, the better can learning prune away uninteresting or losing hypotheses and the faster it becomes. We have combined inductive logic programming (ILP) directly with a relational database. The ILP algorithm is controlled in a model-driven way by the user and in a data-driven way by structures that are induced by three simple learning algorithms.