Dr. Miriam Capretz
Title: Big Data Opportunities, Challenges and Applications
Dr. Miriam Capretz is a Professor of Software Engineering in the Department of Electrical and Computer Engineering and the Associate Dean, Research in the Faculty of Engineering at Western University. She was an Associate Vice-Provost (Acting), Graduate and Postdoctoral Studies from July 2015 to June 2016. Before joining Western University, she was with the University of Aizu, Japan. She received her B.Sc. and M.E.Sc. degrees from UNICAMP, Brazil and her Ph.D. from the University of Durham, UK.
Dr. M. Capretz has been successfully working with software development, research and teaching in software engineering for more than 35 years. She has been involved with the organization of workshops and symposia and has been serving on numerous program committees in international conferences. She has published close to 200 peer-reviewed journal articles, book chapters and conference proceedings. Dr. M. Capretz has made several invited presentations nationally and internationally. Her current research interests include Big Data, machine learning, service oriented architecture, blockchain, cloud computing, privacy and security. Her current research activities include software projects with industry partners for smart buildings, big data analytics for energy management, smart asset management and smart grids.
Nowadays, the amount of data is exploding at an unprecedented rate due to advances in Web technologies, social media, and mobile and sensing devices. Such Big Data possess tremendous potential in terms of business value in several areas such as healthcare, energy management, online advertising, transportation, and financial services. Because of their potential, Big Data have been referred to as a revolution that will transform how we act, work, and live. The main purpose of this revolution is to make use of large amounts of data to enable knowledge discovery and better decision making transforming economies in local, national and international level. The potential of Big Data is highlighted by their definition; however, traditional approaches are struggling when faced with these massive data. This presentation aims to expose the Big Data opportunities, technologies to overcome challenges to deal with them along with some real-world applications.
Title: Data-Intensive Scalable Computing
Mario A.R. Dantas is a Professor in the Department of Computer Science (DCC) at the Exact Sciences Instituite (ICE), Federal University of Juiz de Fora (UFJF) and in the Graduate Program in Computer Science (PPGCC), at the Technology Centre (CTC), Federal University of Santa Catarina (UFSC), with a PhD in Computer Science from the University of Southampton (UK), Visiting Professor at the University of Western Ontario (Canada) and Senior Visiting Researcher in Riken (Japan). He is the author of hundred scientific articles, dozens of chapters in books and three books. He has advised numerous undergraduate, specialization, master and doctorate research works. He has acted as a consultant on various projects with industry in the areas of IoT, networking, distributed systems, and high-performance environments.
The talk will present the Data-Intensive Scalable Computing (DISC), a data-centric approach containing a wide diversity of applications usually employed in the web and business environments. DISC has a strong orientation to data management and analysis with objectives including scalability, fault-tolerance, availability and cost-performance. The paradigm considers programming models utilizing high-level operators, as well as Java/Python/Scala languages under shared-nothing clusters of commodity hardware. HPC experts who have been using this approach within the industry sector will be sharing some experiences of great interest.