Presented By O'Reilly and Cloudera
Make Data Work
December 1–3, 2015 • Singapore
Albert Bifet

Albert Bifet
Professor and Head, Data, Intelligence and Graphs (DIG) Group, Télécom ParisTech

Website | @abifet

Albert Bifet is a professor at LTCI and head of the Data, Intelligence, and Graphs (DIG) Group at Télécom ParisTech, and a scientific collaborator at École Polytechnique. A big data scientist with 10+ years of international experience in research, Albert has led new open source software projects for business analytics, data mining, and machine learning at Huawei, Yahoo, the University of Waikato, and UPC. At Yahoo Labs, he cofounded Apache SAMOA (Scalable Advanced Massive Online Analysis), a distributed streaming machine learning framework that contains a programing abstraction for distributed streaming ML algorithms. At the WEKA Machine Learning Group, he co-led MOA (Massive Online Analysis), the most popular open source framework for data stream mining, with more than 20,000 downloads each year. Albert is the author of Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams and the editor of the Big Data Mining special issue of SIGKDD Explorations in 2012. He was cochair of the industrial track at ECML PKDD 2015, BigMine (2014, 2013, 2012), and the data streams track at ACM SAC (2015, 2014, 2013, 2012). He holds a PhD from BarcelonaTech.

Sessions

11:00am–11:40am Thursday, 12/03/2015
IoT and Real-time
Location: 324 Level: Intermediate
Tags: telecom
Albert Bifet (Télécom ParisTech), Silviu Maniu (Huawei)
Average rating: ****.
(4.17, 6 ratings)
Real-time analytics are becoming increasingly important to telecommunication operators due to the large amount of data that flows through their networks. Drawing from our experience at Huawei, we present StreamDM, a new open source data mining and machine learning library on top of Spark Streaming. We will present its implemented advanced methods, and demonstrate its ease of use and extensibility. Read more.