Por João Gama (INESC-Porto e Faculdade de Economia, Universidade do Porto).
Nowadays, there are applications where data is best modelled not as persistent tables, but rather as transient data streams. In this talk we discuss the limitations of current machine learning and data mining algorithms and 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.
