Professor Riedewald’s research interests lie in databases and data mining with an emphasis on designing scalable techniques for data-driven science. Most sciences are already producing an abundance of data. Analyzing this rapidly growing wealth of information has become a major challenge. This provides an exciting opportunity for developing novel approaches that will have an impact both in computer science as well as in the domain sciences.
Professor Riedewald is developing techniques for distributed data analysis, for mining observational data, for making realistic scientific simulations feasible through data-mining, and for real-time processing of massive data streams. He has a track record of successful collaborations with scientists from different areas, including ornithology, physics, mechanical and aerospace engineering, and astronomy. His work has been published in premier peer-reviewed data management research venues like the Association for Computing Machinery’s International Conference on Management of Data (SIGMOD), the International Conference on Very Large Data Bases (VLDB), IEEE’s International Conference on Data Engineering (ICDE), and Transactions on Knowledge and Data Engineering (TKDE).