1. FREERIDE: A Framework for Rapid Implementation of Datamining Engines
Gagan Agrawal
Department of Computer and Information Sciences
Ohio State University

2. Motivation Large scale data is being created everywhere
Converting data to useful knowledge is challenging
Algorithms used depend upon application and datasets available
Need to keep trying different algorithms and parameters on available datasets
Time required for implementing different algorithms and running them with different parameters on large datasets slows down the mining process

3. FREERIDE offers:
The ability to rapidly prototype a high-performance mining implementation
Distributed memory parallelization
Shared memory parallelization
Ability to process large and disk-resident datasets
Minimal modifications to a sequential implementation for the above three

4. FREERIDE Experience
Used for association mining, clustering, decision tree construction (prediction models), nearest neighbor searches
Excellent speedups in both shared memory and distributed memory environments
Excellent scaleups to large datasets

5. Using FREERIDE Divide computation into local and global reductions
Create an index for data
Could be done easily for a number of mining algorithms
User manual and distribution being prepared

6. Proposed Plan for PET Year 2
Understand the data mining needs of DOD Users
Selection 1-2 demonstation problems
Obtain relevant datasets
Demonstrate the use of FREERIDE framework
Develop presentation interface suitable for user needs
Train DOD users in using the framework

7. PET Year 1 – ET-005
Apply data intensive computing tools to Liquid molding problem
Post-processing of data generated from simulation of molding phenomenon
Analyze the gradient of output to different parameters – avoid simulating with all possible combination of parameters
Use ADR – also see if mining the simulation datasets is useful (not part of the deliverables)