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: --