Big data has become an important issue for a large number of research areas such as data
mining, machine learning, computational intelligence, information fusion, the semantic Web …

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 …

A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …

Recently, data management and processing for wireless sensor networks (WSNs) has
become a topic of active research in several fields of computer science, such as the …

Plan recognition, activity recognition, and intent recognition together combine and unify
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …

In data mining, Frequent Itemsets Mining is a technique used in several domains with
notable results. However, the large volume of data in modern datasets increases the …

Clustering streaming data requires algorithms that are capable of updating clustering results
for the incoming data. As data is constantly arriving, time for processing is limited. Clustering …

Stream processing is a novel distributed computing paradigm that supports the gathering,
processing, and analysis of high-volume, heterogeneous, continuous data streams, to …

As data volumes continue to rise, manual inspection is becoming increasingly untenable. In
response, we present MacroBase, a data analytics engine that prioritizes end-user attention …

Abstract Concept drift constitutes a challenging problem for the machine learning and data
mining community that frequently appears in real world stream classification problems. It is …