Keynote II

Data mining or Knowledge Discovery in Databases is becoming a very active field. Many different organisations perceive the tremendous potential that lies within the massive amounts of data available deep down in their databases. That precious golden knowledge is waiting to be pumped out in the form of models, predictors, summaries, profiles, association rules and more. This would be a happy ending if we stopped here. However, the problem is that the data production volume is increasing exponentially, challenging the most advanced big data architectures and storage capacities. To this point, many researchers and practitioners are proposing different approaches. Instead of, or apart from storing all data produced, there should be imaginative ways of extracting useful knowledge right where/when such data is being collected. Instead of fishing in enormous ponds, it should be feasible to catch the fish as they arrive. However, most of the foundations of Data mining sit over the assumption of a large, complete enough dataset to substantiate accurate models, assuming stability on the "concepts" to be modelled. Data Stream Mining instead assumes a more realistic approach, where that "concepts" may change ("drift") with time. Therefore, a brand new race of learners, able to follow their "concept drift", is needed. Intelligent Transportation Systems (ITS) is a field of Engineering focused on the application of Computer Science, Communications and others to make transportation more efficient, safe and environmentally friendly. Most of these systems are intrinsically dynamic and therefore susceptible of real-time managing, modelling and optimisation. In this talk, I will discuss how many applications are a perfect target for Data Stream Mining in ITS. I will also explore present and future applications, paying careful attention to the Threats and Opportunities of this new way of Knowledge Discovery in another dramatically evolving field like ITS. About the Speaker Dr. Javier Sanchez-Medina is currently a Professor in the Computer Science Department at the University of Las Palmas de Gran Canaria (ULPGC), Spain. Dr. Sanchez-Medina earned his Engineering Master's Degree at the Telecommunications Faculty in 2002, and his PhD at the Computer Science Department in 2008. His research interests mainly include the application of Evolutionary Computation, Data mining and Parallel Computing to Intelligent Transportation Systems. He has significant experience in the development of traffic models and simulation platforms. Dr. Sanchez-Medina has been volunteering for several years at many international conferences related to Intelligent Transportation, Computer Science, Evolutionary Computation, etc. He is also very active as a volunteer in the IEEE ITS Society. Since 2010, he has served the IEEE ITS Society as an organizer of the TBMO 2010 Workshop at ITSC2010, co-organizer of the "Travel Behavior Research: Bounded Rationality and Behavioral Javier Sanchez-Medina et al. / Procedia Computer Science 109C (2017) 8–9 9 Available online at www.sciencedirect.com