  Classic algorithms for sequential pattern discovery, return all frequent
sequences present in a database, but, in general, only a few ones are
interesting for the user. Languages based on regular expressions (RE) have been
proposed to restrict frequent sequences to the ones that satisfy user-specified
constraints. Although the support of a sequence is computed as the number of
data-sequences satisfying a pattern with respect to the total number of
data-sequences in the database, once regular expressions come into play, new
approaches to the concept of support are needed. For example, users may be
interested in computing the support of the RE as a whole, in addition to the
one of a particular pattern. Also, when the items are frequently updated, the
traditional way of counting support in sequential pattern mining may lead to
incorrect (or, at least incomplete), conclusions. The problem gets more
involved if we are interested in categorical sequential patterns. In light of
the above, in this paper we propose to revise the classic notion of support in
sequential pattern mining, introducing the concept of temporal support of
regular expressions, intuitively defined as the number of sequences satisfying
a target pattern, out of the total number of sequences that could have possibly
matched such pattern, where the pattern is defined as a RE over complex items
(i.e., not only item identifiers, but also attributes and functions).
