Efficient mining of temporally annotated sequences

Sequential patterns mining received much attention in recent years, thanks to its various potential application domains. A large part of them represent data as collections of time-stamped itemsets, e.g., customers' purchases, logged web accesses, etc. Most approaches to sequence mining focus on sequentiality of data, using time-stamps only to order items and, in some cases, to constrain the temporal gap between items. In this paper, we propose an e±cient algorithm for computing (temporally-)annotated sequential patterns, i.e., sequential patterns where each transition is annotated with a typical transition time derived from the source data. The algorithm adopts a prefix-projection approach to mine candidate sequences, and it is tightly integrated with a annotation mining process that associates sequences with temporal annotations. The pruning capabilities of the two steps sum together, yielding significant improvements in performances, as demonstrated by a set of experiments performed on synthetic datasets.