BIG DATA are everywhere. They are high-veracity, high-velocity, highvalue,
and/or high-variety data with volumes beyond the ability of
commonly-used software to manage, query, and process within a tolerable
elapsed time. Big data analytics incorporates various techniques from a broad
range of fields, which include cloud computing, data mining, machine learning,
mathematics, and statistics. Data mining aims to extract implicit, previously
unknown, and potentially useful information from data. On the other
hand, nowadays uncertain big data management represents an active and wellrecognized
research area where a relevant number of proposals converge. This
due to several reasons, but mostly dictated by emerging big data trends as well
as the Cloud-computing-paradigms’ explosion. Within this so-wide research
context, a leading role is played by the issue of extracting-useful-knowledge-
from big data, being the uncertain big data setting a critical case to be considered.
In our research, we specially focus on two well-known distinct first-class
Data Mining problems over uncertain big data, namely: frequent itemset min-
ing from uncertain big data, and constrained mining from uncertain big data.
We recognize that these sub-problems converge into a general problem that
we name as “complex mining from uncertain big data”, for which a plethora
of real-life applications and systems can be found. Inspired by these relevant
research challenges, in this chapter we provide the following contributions:
(i ) a comprehensive overview of state-of-the-art literature in the context of
complex mining from uncertain big data; (ii ) an algorithm for supporting
tree-based mining of uncertain big data in distributed environments; (iii ) a
MapReduce-based algorithm for supporting constrained mining over uncertain
big (transactional) data in Cloud environments.
