The current ability to produce massive amounts of data and the impossibility in storing it motivated the development of data stream mining strategies. Despite the proposal of many techniques, this research area still lacks ...

This paper presents the work in progress of an on-demand software deployment system based on application virtualization concepts which eliminates the need of software installation and configuration on each computer. Some ...

This paper presents the work in progress of an on-demand software deployment system based on application virtualization concepts which eliminates the need of software installation and configuration on each computer. Some ...

Given a data stream with many attributes and high frequency of events, how to cluster similar events? Can it be done in real time? For example, how to cluster decades of frequent measurements of tens of climatic attributes ...

A chegada das plataformas de streaming revolucionou drasticamente os mercados fonográfico e de audiovisual na última década. As plataformas se popularizaram rapidamente e alteraram o status quo de um mercado outrora dominado ...

Data stream mining is an emergent research area that investigates knowledge extraction from large amounts of continuously generated data, produced by non-stationary distribution. Novelty detection, the ability to identify ...

Knowledge Discovery in Databases (KDD) techniques present limitations when the volume of data to process is very large. Any KDD algorithm needs to do several iterations on the complete set of data in order to carry out its ...