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     NATIONAL SCIENCE FOUNDATION SYMPOSIUM ON
         NEXT GENERATION OF DATA MINING AND
     CYBER-ENABLED DISCOVERY FOR INNOVATION

        Inner Harbor, Baltimore, USA
             10-12 October 2007
      http://www.cs.umbc.edu/~hillol/NGDM07/

  ***REGISTRATION WILL BE CLOSED on OCT 5, 2007***

Explore and experience the next generation of data mining.
Join NGDM'07 in exploring the emerging technologies and
applications of data mining in:

1) E-Science and Engineering
2) Ubiquitous, Distributed, and High Performance Environments
3) Security, Surveillance, and Privacy
4) Social Science, Finance, Medicine, and Digital Humanities
5) The Web and its Semantics

Contribute to the NSF-report on the needs for next generation
of research and development in this area.

GENERAL CHAIR

Hillol Kargupta
Department of Computer Science and Electrical Engineering,
University of Maryland, Baltimore County
& Agnik
Web: http://www.cs.umbc.edu/~hillol
   http://www.agnik.com
E-mail: ngdm07 at agnik dot com

STEERING COMMITTEE

(1) Rakesh Agrawal, Microsoft Research
(2) Christos Faloutsos, Carnegie Mellon University
(3) Jiawei Han, University of Illinois at Urbana-Champaign
(4) Hillol Kargupta, University of Maryland, Baltimore County
(5) Vipin Kumar, University of Minnesota
(6) Rajeev Motwani, Stanford University
(7) Philip Yu, IBM T. J. Watson Research Center

SCOPE

The dramatic increase in the availability of data from various
sources is creating many fundamental challenges in computing,
storage, communication, and human computer interaction issues for
data mining. Scientists, engineers, and businesses are facing
with emerging problems that involve complex networked
observations, massive simulation-data sets, and ubiquitous
sensory data streams. These heterogeneous data sources should be
linked and analyzed for discovering the next frontiers of
science, arts, and technology. We also need to look beyond the
current cyber-infrastructure and explore how the next generation
of networked data mining applications will support such
large-scale, ubiquitous, multi-source, and data intensive
domains.

This National Science Foundation symposium on Next Generation Data
Mining and Cyber Enabled Discovery for Innovation (NGDM?07) will
bring together data mining researchers, scientists, and engineers
from a diverse background along with domain experts for various
emerging problems that are relevant to Cyber Enabled Discovery
for Innovation (CDI). The objective is to enhance the
understanding of the research problems and facilitate creating an
environment for better understanding the challenges in front of
the data mining and the CDI community.

NGDM'07 will focus on the following areas:

(1) Data Mining in e-Science and Engineering
(2) Media, Pervasive Computing, and Ubiquitous Data Mining
(3) Data mining in Security Surveillance, and Privacy Protection
(4) Social Science, Finance, Digital Humanities, and Data Mining
(5) The Web, Semantics, and Data Mining

For more details please visit the symposium website.

SPEAKERS

(1) Alessandro Acquisti, Carnegie Mellon University
(2) Michael Berry, University of Tennessee
(3) Kirk Borne, George Mason University
(4) Gerbrand Ceder and Chris Fischer, MIT
(5) Chris Clifton, Purdue University
(6) David Covell, National Cancer Institute
(7) Saso Dzeroski, Jozef Stefan Institute
(8) Christos Faloutsos, Carnegie Mellon University
(9) Ian Foster, Argonne National Laboratory
(10) James Gentle, George Mason University
(11) David Goldberg, Univ. of Illinois at Urbana Champaign
(12) Robert Grossman, University of Illinois at Chicago
(13) Larry Hall, University of South Florida
(14) Jiawei Han, University of Illinois at Urbana-Champaign
(15) James Hendler, Rensselaer Polytechnic Institute
(16) Haym Hirsh, National Science Foundation and Rutgers
(17) Vasant Honavar, Iowa State University
(18) V. Hristidis, Florida International University
(19) Anupam Joshi, University of Maryland, Baltimore County
(20) Hillol Kargupta, University of Maryland, Baltimore County and Agnik
(21) Matt Krischenbaum, Univ. of Maryland, College Park
(22) Andrew Kusiak, University of Iowa
(23) Vipin Kumar, University of Minnesota
(24) Huan Liu, Arizona State University
(25) Michael May, Fraunhofer Institute
(26) Olfa Nasraoui, University of Louisville
(27) Srinivasan Parthasarathy, Ohio State University
(28) D. Quick, Southern Methodist University
(29) Raghu Ramakrishnan, Yahoo! Research
(30) Steven Salzberg, University of Maryland, College Park
(31) Lisa Singh, Georgetown University
(32) Marc Snir, University of Illinois at Urbana-Champaign
(33) Jaideep Srivastava, University of Minnesota
(34) Domenico Talia, University of Calabria
(35) Bhavani Thuraisingham, University of Texas at Dallas
(36) Wei Wang, University of North Carolina Chapel-Hill
(37) Daniel J. Weitzner, MIT
(38) Xindong Wu, University of Vermont
(39) Philip Yu, IBM T. J. Watson Research Center

PANELISTS:

(1) Alok Choudhary, Northwestern University
(2) Tim Finin, UMBC
(3) Gary Strong, Johns Hopkins University
(4) Joseph Kielman, Department of Homeland Security
(5) Diane Lambert, Google Research
(6) Pat Langley, Stanford University
(7) H. K. Ramapriyan, NASA
(8) Ted Senator, SAIC
(9) Rene Vidal, Johns Hopkins University
(10) Grace Yang, National Science Foundation
(11) Yelena Yesha, UMBC
(12) Maria Zemankova, National Science Foundation

POSTERS:

1. Jeffrey Campbell, et al. Data Mining for Ecological Field Research: Lessons Learned from Amphibian and Reptile Activity Analysis.

2. N. Balac et al. Distributed Data Mining System with Gateway for Virtual Observatories.

3. Madhu Ahluwalia et al. Preserving Privacy in Supply Chain Management: A Challenge for Next Generation Data Mining.

4. H. Jasso et al. Spatio-temporal Characteristics of 911 Emergency Call Hotspots.

5. S. Tsumoto, et al. Data Mining for Risk Management in Hospital Information Systems.

6. L. Raschid, et al. A Framework for Discovering Associations from the Annotated Biological Web.

7. J. Tang, et al. Mining Conditions in Rapid Intensifications of Tropical Cyclones.

8. Mario Boley. Intelligent Pattern Mining via Quick Parameter Evaluation.

9. Li Yang. Multiresolution Data Aggregation and Analytical Exploration of Large Data Sets.

10. Joao Gama. Issues and Challenges in Learning from Data Streams.

11. Lee Giles. ChemXSeer: An eChemistry Web Search Engine and Repository

12. Yasui et al. Message Feature Map toward Effective Facilitation on On-line Discussions.

13. David Lo et al. Software Specification Discovery: A New Data Mining Approach.

14. Li Xiong. Mining Distributed Private Databases using Random Response Protocols.

15. S. Sahay, et al. Semantic Annotation and Inference for Medical Knowledge Discovery.

16. Mark Meiss. A Framework for Analysis of Anonymized Network Flow Data

17. Hady Lauw and Ee-Peng Lim. A Multitude of Opinions: Mining Online Rating Data.

18. Rodney Martin, NASA Ames Research Center, Investigation of Optimal Alarm System Performance for Anomaly Detection.

19. Kamalika Das, Kun Liu, and Hillol Kargupta. A Game Theoretic Perspective Toward Practical Privacy Preserving Data Mining.

20. Murat Kantarcioglu, Bowei Xi, and Chris Clifton, A Game Theoretical Framework for Adversarial Learning.