Spatio-Temporal Data Mining of Ancient Genome Databases
Peter Z. Revesz
Department of Computer Science and Engineering
University of Nebraska-Lincoln
Ancient human DNA can be used in analyzing and visualizing human population movements.
However, in many regions of the world the large time range and the extreme sparseness of ancient DNA
samples pose important challenges to data mining and analytics. We review several spatio-temporal data
mining and analytics methods that help to understand and model human population movements. We also look
at data mining methods that can incorporate data from archaeology and linguistics to enhance the models.
We use as an example of spatio-temporal data mining the problem of modeling the spread of the Indo-European
language family, for which we generate new animations using the MLPQ constraint database system.
Brief Biography of the Speaker:
Short biography: Peter Z. Revesz holds a Ph.D. degree in Computer Science from Brown University.
He was a postdoctoral fellow at the University of Toronto before joining the University of Nebraska-Lincoln,
where he is a professor in the Department of Computer Science and Engineering. Dr. Revesz is an expert in databases,
data mining, big data analytics and bioinformatics. He is the author of Introduction to Databases: From Biological
to Spatio-Temporal (Springer, 2010) and Introduction to Constraint Databases (Springer, 2002). Dr. Revesz held
visiting appointments at the IBM T. J. Watson Research Center, INRIA, the Max Planck Institute for Computer Science,
the University of Athens, the University of Hasselt, the U.S. Air Force Office of Scientific Research and the U.S.
Department of State. He is a recipient of an AAAS Science & Technology Policy Fellowship, a J. William Fulbright
Scholarship, an Alexander von Humboldt Research Fellowship, a Jefferson Science Fellowship, a National Science
Foundation CAREER award, and a “Faculty International Scholar of the Year” award by Phi Beta Delta, the Honor
Society for International Scholars.