- Chair of Data Systems Engineering (School of Computing Science)
Personal site: http://www.dcs.gla.ac.uk/ideas/
Peter Triantafillou has held the Data Systems Chair at the School of Computing Science at the University of Glasgow since January 2013 and has served as the Associate Director of the Urban Big Data Research Centre at Glasgow — a national infrastructure for urban data services and analytics. Before that Peter held professorial positions at Simon Fraser University Canada (1991-1995), the Technical University of Crete (1995-2002), the University of Patras (2002-2013) in Greece, and he was visiting professor at the Max-Planck Institute for Informatics in Germany (in 2004-2005 and in 2012-2013). Peter received his PhD in computer science from the University of Waterloo in 1991, being the Department of computer Science and the Faculty of Mathematics nominee for the Gold Medal for outstanding achievements at the Doctoral level. Peter has been conducting research for over 25 years, in large-scale data management and systems, including Distributed Databases, Distributed Filesystems, Multimedia Systems, Storage Servers, Peer-to-Peer Data Systems, Publish-Subscribe / Event-based Systems, Decentralized Search Engines and Information Retrieval, and Social Networks/Systems. Currently, his contributions are in Big Data Management and Large-Scale Infrastructures. Peter has published extensively in top journals and conferences, has served in the Technical Program Committees of more than 100 international conferences, and has been the PC Chair or Vice-chair in several prestigious conferences. Peter has received the best paper award at the ACM CIKM (Conference on Information and Knowledge Management) in 2006 and is a co-designer of several innovative systems (such as the MINERVA decentralized search engine and the eXO decentralized social networking system).
- Big data system architectures.
- Scalable, distributed storage systems, including file systems and modern NoSQL Database systems.
- Complex query processing and optimization (graph queries, ranking, temporal analytics).
- Indexing structures including statistical structures and methods.
- Appropriate notions for data and application consistently at scale.
- Machine learning techniques for big data systems.