Nowadays, there are applications in which the data are modeled 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 take these constraints into account. We identify the main issues and current challenges that emerge in learning from data streams. We will present an illustrative algorithm to continuously maintain a clustering structure from evolving time-series. We finalize with a brief presentation of open research lines for further developments
João Gama is a Full Professor at the School of Economics, University of Porto, Portugal. He is EurIA Fellow, senior researcher, and member of the board of directors of the LIAAD, a group belonging to INESC Porto.
He is an Editor of several Machine Learning and Data Mining journals. He served as Program Chair of ECMLPKDD 2005, DS09, ADMA09, EPIA 2017, DSAA 2017, served as Conference Chair of IDA 2011, ECMLPKDD 2015, and a series of Workshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD. His main research interests are in knowledge discovery from data streams and evolving data. He published more than 300 reviewed papers in journals and major conferences. He has an extensive list of publications in the area of data stream learning