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Classic algorithms for sequential pattern discovery, return all frequentsequences present in a database, but, in general, only a few ones areinteresting for the user. Languages based on regular expressions (RE) have beenproposed to restrict frequent sequences to the ones that satisfy user-specifiedconstraints. Although the support of a sequence is computed as the number ofdata-sequences satisfying a pattern with respect to the total number ofdata-sequences in the database, once regular expressions come into play, newapproaches to the concept of support are needed. For example, users may beinterested in computing the support of the RE as a whole, in addition to theone of a particular pattern. Also, when the items are frequently updated, thetraditional way of counting support in sequential pattern mining may lead toincorrect (or, at least incomplete), conclusions. The problem gets moreinvolved if we are interested in categorical sequential patterns. In light ofthe above, in this paper we propose to revise the classic notion of support insequential pattern mining, introducing the concept of temporal support ofregular expressions, intuitively defined as the number of sequences satisfyinga target pattern, out of the total number of sequences that could have possiblymatched such pattern, where the pattern is defined as a RE over complex items(i.e., not only item identifiers, but also attributes and functions).

Temporal Support of Regular Expressions in Sequential Pattern Mining