The wide availability of high‐performance computing systems, Grids and Clouds, allowed
scientists and engineers to implement more and more complex applications to access and …

Most data mining approaches assume that the data can be provided from a single source. If
data was produced from many physically distributed locations like Wal-Mart, these methods …

More than twelve years have elapsed since the first public release of WEKA. In that time, the
software has been rewritten entirely from scratch, evolved substantially and now …

This paper presents Xel, a cloud-agnostic data platform for the design-driven building of
high-availability data science services as a support tool for data-driven decision-making. We …

Sentiment analysis from data streams is aimed at detecting authors' attitude, emotions and
opinions from texts in real-time. To reduce the labeling effort needed in the data collection …

The paper presents a platform for distributed computing, developed using the latest software
technologies and computing paradigms to enable big data mining. The platform, called …

Effective Big Data Mining requires scalable and efficient solutions that are also accessible to
users of all levels of expertise. Despite this, many current efforts to provide effective …

Association rules mining (ARM) is a well-known combinatorial optimization problem aiming
at extracting relevant rules from given large-scale datasets. According to the state of the art …

The enormous growth of data in a variety of applications has increased the need for high
performance data mining based on distributed environments. However, standard data …

Computational workflows are a powerful paradigm to represent and manage complex
applications, particularly in large-scale distributed scientific data analysis. Workflows …