14 Mar Seminar ‘Real-Time Data Mining’ from João Gama, INESC TEC (University of Porto)
When and where:
March 29th at 3PM, room: ForumC, Campus La Plaine
Abstract
Nowadays, there are applications in which the data are modelled best not as persistent tables, but rather as transient data streams. In this keynote, we discuss the limitations of current machine learning and data mining algorithms. We discuss the fundamental issues in learning in dynamic environments like learning decision models that evolve over time, learning and forgetting, concept drift and change detection. Data streams are characterized by huge amounts of data that introduce new constraints in the design of learning algorithms: limited computational resources in terms of memory, processing time and CPU power. In this talk, we present some illustrative algorithms designed to taking these constrains into account. We identify the main issues and current challenges that emerge in learning from data streams, and present open research lines for further developments.
Bio
João Gama is an Associate Professor at the University of Porto, Portugal. He is a senior researcher and member of the board of directors of the LIAAD, a group belonging to INESC Porto. He is Director of the master Data Analytics. He serves as member of the Editorial Board of MLJ, DAMI, TKDE, NGC, KAIS, and IDA. He served as Chair of ECMLPKDD 2005 and 2015, DS09, ADMA09 and a series of Workshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD. His main research interest is in knowledge discovery from data streams and evolving data. He is the author of a recent book on Knowledge Discovery from Data Streams. He has extensive publications in the area of data stream learning.
For more informations check on the http://di.ulb.ac.be/seminaires/