Frank Dehne received a MCS (Dipl. Inform.) from RWTH Aachen University, Germany and a PhD from the University of Wuerzburg, Germany. He is currently Chancellor's Professor of Computer Science at Carleton University in Ottawa, Canada. His research program is focused on improving the performance of big data analytics systems, in particular for business intelligence and computational biochemistry, through efficient parallel computing methods for multi-core processors, GPUs, processor clusters and clouds. He is serving or has served on the Editorial Boards of IEEE Transaction on Computers, Information Processing Letters, Journal of Bioinformatics Research and
Applications, and Int. Journal of Data Warehousing and Mining. He is a member and former vice-chair of the IEEE Technical Committee on Parallel Processing, and member of the ACM Symposium on Parallel Algorithms & Architectures Steering Committee. Since 2010, he is a Fellow of the IBM Centre For Advanced Studies Canada (Business Intelligence and Business Analytics section).
In contrast to queries for on-line transaction processing (OLTP) systems that typically access only a small portion of a database, on-line analytical processing (OLAP) queries may need to aggregate large portions of a database which often leads to performance issues. We have built multi-core and cloud based real-time OLAP systems utilizing a new distributed index structure for OLAP, termed distributed PDCR tree. Our system supports multiple dimension hierarchies and efficient query processing on elaborate dimension hierarchies which are central to OLAP systems. It is particularly efficient for complex OLAP queries that need to aggregate large portions of a data warehouse. Our project is partially funded by IBM and our system has won an "IBM Innovation Impact of the Year" award.