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Keynote Lecture

 

Data Mining in the XXI Century

João Gama
LIAAD - INESC TEC, University of Porto
Portugal
 

Brief Bio
João Gama is an Associate Professor at the University of Porto, Portugal. He is also a senior researcher and member of the board of directors of the Laboratory of Artificial Intelligence and Decision Support (LIAAD), a group belonging to INESC Porto. João Gama serves as the member of the Editorial Board of Machine Learning Journal, Data Mining and Knowledge Discovery, Intelligent Data Analysis and New Generation Computing. He served as Co-chair of ECML 2005, DS09, ADMA09 and a series of Workshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD. He was also the chair for the conference of Intelligent Data Analysis 2011. His main research interest is in knowledge discovery from data streams and evolving data. He is the author of more than 200 papers reviewed by peers and author of a recent book on Knowledge Discovery from Data Streams. He has extensive publications in the area of data stream learning.


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.



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