Computer Science Distinguished Lecture
| Date/Time: | Thursday, 22 Mar 2012 at 3:40 pm |
|---|---|
| Location: | 207 Marston Hall |
| Cost: | Free |
| Phone: | 515-294-6516 |
| Channel: | College of Liberal Arts and Sciences |
| Categories: | Lectures |
| Actions: | Download iCal/vCal | Email Reminder |
The importance of spatial and spatio-temporal data mining is growing with the increasing incidence and importance of large datasets such as maps, virtual globes, repositories of remote-sensing images, the decennial census and collections of trajectories (e.g. gps-tracks). Applications include Environment and Climate (global change, land-use classification), Public Health (e.g. monitoring and predicting spread of disease), Public Safety (e.g. crime hot spots), Public Security (e.g. common operational picture), M(obile)-commerce (e.g. location-based services), etc.
Classical data mining techniques often perform poorly when applied to spatial and spatio-temporal data sets because of the many reasons. First, these dataset are embedded in continuous space, whereas classical datasets (e.g. transactions) are often discrete. Second, patterns are often local where as classical data mining techniques often focus on global patterns. Finally, one of the common assumptions in classical statistical analysis is that data samples are independently generated. When it comes to the analysis of spatial and spatio-temporal data, however, the assumption about the independence of samples is generally false because such data tends to be highly self correlated. For example, people with similar characteristics, occupation and background tend to cluster together in the same neighborhoods. In spatial statistics this tendency is called autocorrelation. Ignoring autocorrelation when analyzing data with spatial and spatio-temporal characteristics may produce hypotheses or models that are inaccurate or inconsistent with the data set.
Thus new methods are needed to analyze spatial and spatio-temporal data to interesting, useful and non-trivial patterns. This talk surveys some of the new methods including those for discovering interactions (e.g. co-locations , co-occurrences, tele-connections), detecting spatial outliers and location prediction along with emerging ideas on spatio-temporal pattern mining.
Bio:
Shashi Shekhar is a McKnight Distinguished University Professor at the University of Minnesota, Minneapolis, MN, USA. For contributions to spatial databases, spatial data mining, and geographic information systems(GIS), he received the IEEE Technical Achievement Award and was elected a Fellow of the IEEE as well as the American Assoc. for Advancement of Science. He has co-authored over 250 research papers in peer-reviewed forums, co-edited an Encyclopedia of GIS (Springer, 2008, isbn 978-0-387-30858-6), and co-authored a textbook on Spatial Databases (Prentice Hall, 2003, isbn 0-13-017480-7) which has been translated into multiple foreign languages. He is serving as a co-Editor-in-Chief of Geo-Informatica: An Intl. Journal on Advances in Computer Sc. for GIS (ISSN 1384-6175) , an editor for the SpringerBriefs GIS series, a co-chair of the Symposium on Spatial and Temporal Databases (2011), and a member of the steering committee for the IEEE Workshop on Spatial and Spatio-temporal Data Mining. He served on two committees of the National Research Council National Academy of Sciences, namely, the committee on mapping sciences (2004-2009) and the committee to review the basic and applied research at National Geospatial-Intelligence Agency (2005). He also served as a member of the Board of Directors of University Consortium on GIS (2003-2004), a member of the editorial boards of IEEE Transactions on Knowledge and Data Eng., a member of the steering committee of the ACM Intl. Conference on GIS, a member of the IEEE-CS Computer Science & Engineering Practice Board, a program co-chair of the ACM Intl. Workshop on Advances in GIS (1996), and a technical advisor to United Nations Development Program (UNDP), Environmental Systems Research Institute (ESRI), and other organizations. His research projects have been sponsored by the NSF, NASA, UDOD, USDOT, MN/DoT etc. He received a Ph.D. degree in Computer Science from the University of California (Berkeley, CA). More details are available from http://www.cs.umn.edu/~shekhar.
F. Wendell Miller
This lecture was made possible in part by the generosity of F. Wendell Miller, who left his entire estate jointly to Iowa State University and the University of Iowa. Mr. Miller, who died in 1995 at age 97, was born in Altoona, Illinois, grew up in Rockwell City, graduated from Grinnell College and Harvard Law School and practiced law in Des Moines and Chicago before returning to Rockwell City to manage his family's farm holdings and to practice law. His will helped to establish the F. Wendell Miller Trust, the annual earnings on which, in part, helped to support this activity.