These include building efficient databases and indexes for sequence information, extracting the frequently occurring patterns, comparing sequences for similarity, and recovering missing sequence members. In general, sequence mining problems can be classified as string mining which is typically based on string processing algorithms and itemset mining which is typically based on association rule learning. Local process models [2] extend sequential pattern mining to more complex patterns that can include (exclusive) choices, loops, and concurrency constructs in addition to the sequential ordering construct.