Computational Intelligence in the Big Data Context
Details
Agenda:
6:00 pm: A talk by Dr. Isaac Triguero and Rebecca Tickle
7:00 pm: Q & A, discussion and chat
Abstract:
Computational Intelligence (CI) commonly refers to a variety of bio-inspired and/or human-like techniques that can be applied in optimisation, learning and modelling problems. Broadly speaking, CI comprises Artificial Neural Networks, Fuzzy Sets and Fuzzy Logic, and Evolutionary Computation. In the era of big data, CI in conjunction with data mining techniques are expected to help uncover useful knowledge from big data as they are very well suited for dealing with the intrinsic veracity and variety of big data. However, they are challenged by the volume and velocity of big data which typically limit their application in this context. In this talk, we will provide a gentle introduction to big data, defining what it is and how to deal with it. As a case study, we will present a dynamic fuzzy approach to detect hot spots in big data streams and its application to identify incidents in the roads caused by heavy goods vehicles.
Isaac Triguero Bio:
Isaac Triguero received his M.Sc. and Ph.D. degrees in Computer Science from the University of Granada, Granada, Spain, in 2009 and 2014, respectively. He is currently an Assistant Professor of Data Science since June 2016. His work is mostly concerned with the research of novel methodologies for big data analytics. Dr Triguero has published more than 65 international publications in the fields of Big Data, Machine Learning and Optimisation (H-index=22 and more than 1800 citations on Google Scholar). He is a Section Editor-in-Chief of the Machine Learning and Knowledge Extraction journal, and an associate editor of the Big Data and Cognitive Computing journal, and the IEEE Access journal. He has acted as Program Co-Chair of the IEEE Conference on Smart Data (2016), the IEEE Conference on Big Data Science and Engineering (2017), and the IEEE International Congress on Big Data (2018). Dr Triguero is currently leading a KTP project funded by Innovative UK and the energy provider E.ON that investigates Smart Metering data.
Rebecca Tickle Bio:
Rebecca Tickle received her BSc degree in Computer Science from the University of Nottingham in 2018. She is currently a Ph.D student with the Automated Scheduling, Optimisation and Planning (ASAP) research group at the University of Nottingham. Her research area is the application of computational intelligence to improve data stream mining techniques, including in the context of big data.
Contact Details:
Isaac Triguero
Assistant Professor in Data Science
School of Computer Science, University of Nottingham
E-mail: Isaac.Triguero@nottingham.ac.uk
Web-site: http://www.cs.nott.ac.uk/~pszit
Google Scholar: https://scholar.google.com/citations?user=KogUP4YAAAAJ&hl=en
Rebecca Tickle
PhD Student in Computer Science
School of Computer Science, University of Nottingham
E-mail: Rebecca.Tickle@nottingham.ac.uk
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We will be providing pizza and drinks which are kindly provided by our sponsor, RP Analytics.
Please don’t forget to RSVP so we have an idea about the numbers attending
See you all at University of Nottingham,
Data Science Nottingham team