Parallel Sessions - Approaches to Modeling 1
           
Event Type Start Time End Time Rm # Chair  

 

Education 8:30AM 10:00AM 21, 22, 23, 24, 25, 28
 
Title:

Approaches to Modeling
  Speakers/Presenter:

 

Education 8:30AM 10:00AM 21, 22, 23, 24, 25, 28
 
Title:

Cluster Computing: Parallel Computing in Education
  Speakers/Presenter:
Paul Gray, David Joiner

 

Education 8:30AM 10:00AM 21, 22, 23, 24, 25, 28
 
Title:

Data Mining and D2K
  Speakers/Presenter:
Michael Welge, Loretta Auvil, Peter Bajscy
             

 

     
  Session: Parallel Sessions - Approaches to Modeling 1
  Title: Approaches to Modeling
  Chair:
  Time: Sunday, November 16, 8:30AM - 10:00AM
  Rm #: 21, 22, 23, 24, 25, 28
  Speaker(s)/Author(s):  
 
   
  Description:
  Civic Center Rooms [to be assigned]

* Agent Modeling Michael Novak, Lisa Bievenue
* Systems Dynamics Modeling Shawn Sendlinger, Susan Ragan
* Algebraic Modeling Garrett Love, Dan Warner
* Visualization Steve Cunningham
* Modeling and visualization using Spreadsheets Edna Gentry
* Data Mining and D2K Michael Welge, Loretta Auvil, Peter Bajscy
* Cluster Computing: Parallel Computing in Education Paul Gray, David Joiner
  Link: --
   

 

     
  Session: Parallel Sessions - Approaches to Modeling 1
  Title: Cluster Computing: Parallel Computing in Education
  Chair:
  Time: Sunday, November 16, 8:30AM - 10:00AM
  Rm #: 21, 22, 23, 24, 25, 28
  Speaker(s)/Author(s):  
  Paul Gray, David Joiner
   
  Description:
  Training students for roles in the future of High Performance Computing can be a challenge. What should an HPC course entail, where can you leverage existing materials, and what aspects are most important to core HPC are all questions that arise. This presentation will discuss current standards and existing frameworks for working parallel computing into the undergraduate curriculum and will present various hands-on examples for integrating parallel computing topics into the classroom.
  Link: --
   

 

     
  Session: Parallel Sessions - Approaches to Modeling 1
  Title: Data Mining and D2K
  Chair:
  Time: Sunday, November 16, 8:30AM - 10:00AM
  Rm #: 21, 22, 23, 24, 25, 28
  Speaker(s)/Author(s):  
  Michael Welge, Loretta Auvil, Peter Bajscy
   
  Description:
  The field of Data Mining has developed in response to the need for machine-oriented, automated methods for analyzing large data sets. Data mining combines work from areas such as statistics, machine learning, pattern recognition, databases, and, more recently, high performance computing. The goal of data mining is to discover interesting and previously unknown information in data sets. Tools for data mining have the ability to parse enormous amounts of data and discover significant patterns and relationships that might otherwise have taken a human being thousands of hours to find. In order to facilitate our research activities, ALG has, over the last few years, developed the D2K application environment for data mining. D2K is a flexible data mining and machine learning system that integrates analytical data mining methods for prediction, discovery, and deviation detection, with information visualization tools.

This session will review data mining techniques for prediction and rule pattern problems. We will also briefly describe D2K and the need for frameworks for data analysis.
  Link: --