Real-Time Big Data Stream Analytics
Senior Researcher
Huawei
COM2 Level 4
Executive Classroom, COM2-04-02
Organized by Telecom-ParisTech, NUS SoC, and IPAL
Abstract:
Big Data is a new term used to identify datasets that we cannot manage with 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, and velocity, of such data. In this talk, we will focus on advanced techniques in Big Data mining in real time using evolving data stream techniques: using a small amount of time and memory resources, an being able to adapt to changes. We will discuss some advanced state-of-the-art methodologies in stream mining based in the use of adaptive size sliding windows. Finally, we will present the MOA software framework with classification, regression, and frequent pattern methods, and the new Apache SAMOA distributed streaming software.
Biodata:
Dr. Albert Bifet is a Senior Researcher at Huawei. He is the author of a book on Adaptive Stream Mining and Pattern Learning and Mining from Evolving Data Streams. His main research interest is in Learning from Data Streams. He published more than 60 articles. He is serving as Industrial Track co-Chair of ECM-PKDD 2015. He is one of the leaders of MOA and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He has been Co-Chair of BigMine (2015, 2014, 2013, 2012), and ACM SAC Data Streams Track (2015, 2014, 2013, 2012).