In the last two decades, machine learning research and practice has focused on batch
learning, usually with small datasets. Nowadays there are applications in which the data are …

Big data streams are possibly one of the most essential underlying notions. However, data
streams are often challenging to handle owing to their rapid pace and limited information …

The movement of tourists has important economic and social implications for destination
management. However, tracking and analyzing such movement can be a challenge both …

We study the problem of efficiently mining statistically-significant sequential patterns from
large datasets, under different null models. We consider one null model presented in the …

In recent years, the field of pattern mining has shifted to user-centered methods. In such a
context, it is necessary to have a tight coupling between the system and the user where …

Previous studies have shown mining closed patterns provides more benefits than mining the
complete set of frequent patterns, since closed pattern mining leads to more compact results …

Incorporating cognition into the design and architecture of the Internet of Things (IoT) has
recently been the subject of much research, giving rise to a new subfield known as the …

Sequential pattern mining has been introduced by Agrawal and Srikant (in: Proceedings of
ICDE'95, pp 3–14, 1995) 2 decades ago, and its usefulness has been widely proved for …

Sequential pattern mining is a fundamental data mining task with application in several
domains. We study two variants of this task—the first is the extraction of frequent sequential …

We present ProSecCo, an algorithm for the progressive mining of frequent sequences from
large transactional datasets: It processes the dataset in blocks and it outputs, after having …