  Relationship-aware sequential pattern mining is the problem of mining
frequent patterns in sequences in which the events of a sequence are mutually
related by one or more concepts from some respective hierarchical taxonomies,
based on the type of the events. Additionally events themselves are also
described with a certain number of taxonomical concepts. We present RaSP an
algorithm that is able to mine relationship-aware patterns over such sequences;
RaSP follows a two stage approach. In the first stage it mines for frequent
type patterns and {\em all} their occurrences within the different sequences.
In the second stage it performs hierarchical mining where for each frequent
type pattern and its occurrences it mines for more specific frequent patterns
in the lower levels of the taxonomies. We test RaSP on a real world medical
application, that provided the inspiration for its development, in which we
mine for frequent patterns of medical behavior in the antibiotic treatment of
microbes and show that it has a very good computational performance given the
complexity of the relationship-aware sequential pattern mining problem.
