A unified view of Automata-based algorithms for Frequent Episode
  Discovery
  Frequent Episode Discovery framework is a popular framework in Temporal Data
Mining with many applications. Over the years many different notions of
frequencies of episodes have been proposed along with different algorithms for
episode discovery. In this paper we present a unified view of all such
frequency counting algorithms. We present a generic algorithm such that all
current algorithms are special cases of it. This unified view allows one to
gain insights into different frequencies and we present quantitative
relationships among different frequencies. Our unified view also helps in
obtaining correctness proofs for various algorithms as we show here. We also
point out how this unified view helps us to consider generalization of the
algorithm so that they can discover episodes with general partial orders.
