  Gradual pattern mining allows for extraction of attribute correlations
through gradual rules such as: "the more X, the more Y". Such correlations are
useful in identifying and isolating relationships among the attributes that may
not be obvious through quick scans on a data set. For instance, a researcher
may apply gradual pattern mining to determine which attributes of a data set
exhibit unfamiliar correlations in order to isolate them for deeper exploration
or analysis. In this work, we propose an ant colony optimization technique
which uses a popular probabilistic approach that mimics the behavior biological
ants as they search for the shortest path to find food in order to solve
combinatorial problems. In our second contribution, we extend an existing
gradual pattern mining technique to allow for extraction of gradual patterns
together with an approximated temporal lag between the affected gradual item
sets. Such a pattern is referred to as a fuzzy-temporal gradual pattern and it
may take the form: "the more X, the more Y, almost 3 months later". In our
third contribution, we propose a data crossing model that allows for
integration of mostly gradual pattern mining algorithm implementations into a
Cloud platform. This contribution is motivated by the proliferation of IoT
applications in almost every area of our society and this comes with provision
of large-scale time-series data from different sources.
