  When comparing inductive logic programming (ILP) and attribute-value learning
techniques, there is a trade-off between expressive power and efficiency.
Inductive logic programming techniques are typically more expressive but also
less efficient. Therefore, the data sets handled by current inductive logic
programming systems are small according to general standards within the data
mining community. The main source of inefficiency lies in the assumption that
several examples may be related to each other, so they cannot be handled
independently.
  Within the learning from interpretations framework for inductive logic
programming this assumption is unnecessary, which allows to scale up existing
ILP algorithms. In this paper we explain this learning setting in the context
of relational databases. We relate the setting to propositional data mining and
to the classical ILP setting, and show that learning from interpretations
corresponds to learning from multiple relations and thus extends the
expressiveness of propositional learning, while maintaining its efficiency to a
large extent (which is not the case in the classical ILP setting).
  As a case study, we present two alternative implementations of the ILP system
Tilde (Top-down Induction of Logical DEcision trees): Tilde-classic, which
loads all data in main memory, and Tilde-LDS, which loads the examples one by
one. We experimentally compare the implementations, showing Tilde-LDS can
handle large data sets (in the order of 100,000 examples or 100 MB) and indeed
scales up linearly in the number of examples.
