Hans -Peter Kriegel ( born October 1, 1948) is a German computer scientist and professor at the Ludwig -Maximilians- University of Munich, where he directs the teaching and research unit for database systems. He is currently ( 2013) also deputy head of the Institute of computer science at the University.
He received his doctorate in 1976 at the Technical University of Karlsruhe Fridericiana about " generating translations by grammar couples ". After his habilitation in computer science at the University of Dortmund in 1982 and professor at the Julius- Maximilians- University of Würzburg and the University of Bremen in 1991, he moved to the fledgling computer science at the Ludwig- Maximilians- University of Munich.
Among his best known works include the database index structures R *-tree, X - tree and IQ - tree, the cluster analysis algorithms DBSCAN and OPTICS and the outlier detection method Local Outlier Factor. Other research focuses on the similarity search on blurred and multimedia data, as well as cluster analysis and outlier detection especially on high-dimensional, correlated data and subspaces.
He is regarded as the most cited computer scientists at a German university, and ranks among the world top 15 most cited researchers in the fields of databases and data mining / knowledge discovery in databases.
ACM SIGMOD Best Paper Award, 1997 for the work "Fast Parallel Similarity Search in Multimedia Databases" (Stefan Berchtold, Christian Böhm, Bernhard Braun Müller, Daniel Keim and Hans- Peter Kriegel ).
DASFAA Best Paper Award, 2006 for the work " Probabilistic Similarity Join on Uncertain Data" (Hans -Peter Kriegel, Peter Kunath, Martin Pfeifle, Matthias Renz and ).
ACM Fellow in 2009 for his contributions in the areas of " knowledge discovery, data mining, similarity search, and management of spatial data access methods for high-dimensional data ," the Association for Computing Machinery was the first computer a Bavarian university.
IEEE ICDM Research Contributions Award in 2013 for his contributions to data mining, in particular with the algorithms DBSCAN, OPTICS and Local Outlier Factor.