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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 taxonomy, based on the type of the events. Additionally events themselves are also described with a certain number of taxonomical concepts. The authors 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 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.
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