  Frequent episode discovery is a popular framework for pattern discovery in
event streams. An episode is a partially ordered set of nodes with each node
associated with an event type. Efficient (and separate) algorithms exist for
episode discovery when the associated partial order is total (serial episode)
and trivial (parallel episode). In this paper, we propose efficient algorithms
for discovering frequent episodes with general partial orders. These algorithms
can be easily specialized to discover serial or parallel episodes. Also, the
algorithms are flexible enough to be specialized for mining in the space of
certain interesting subclasses of partial orders. We point out that there is an
inherent combinatorial explosion in frequent partial order mining and most
importantly, frequency alone is not a sufficient measure of interestingness. We
propose a new interestingness measure for general partial order episodes and a
discovery method based on this measure, for filtering out uninteresting partial
orders. Simulations demonstrate the effectiveness of our algorithms.
