Mining Big Data in Real Time

Big Data is a new term used to identify datasets that we can not managewith current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, andvelocity, of such data. In this talk, we will focus on advanced techniquesin Big Data mining in real time using evolving data stream techniques:

1. using a small amount of time and memory resources, and
2. being able to adapt to changes.

We will present the MOA software framework with classification, regression, and frequent pattern methods, the upcoming SAMOA distributed streaming software, and finally we will discuss someadvanced state-of-the-art methodologies in stream mining based in the use of adaptive size sliding windows.

Albert Bifet

Researcher in Big Data stream mining at Yahoo LabsBarcelona. He is the author of a book on Adaptive Stream Mining and Pattern Learning and Mining from Evolving Data Streams. He is one of the project leaders of MOA software environment for implementing algorithms and running experiments for online learning from evolving data streams at theWEKA Machine Learning group at University of Waikato, New Zealand.