CURRENT WORK

Benchmarking and Performance Evaluation

February 2010 - Present

TBD

PREVIOUS WORK

Data Collection and Analysis Tool (DCAT)

September 2008 - March 2010

In parallel computing, many tasks can be performed simultaneously. However, performance factors such as communication, synchronization, and computation can negatively impact the performance of parallel machines. Current methods for locating bottlenecks and points of failure on parallel machines can generate a tremendous amount of data. The objective of the Data Collection and Analysis Tool (DCAT) is to provide a foundation for the analysis of large data sets and a framework for network analysis.
[Thanks to Dr. Line C. Pouchard and Dr. Stephen Poole]

Oak Ridge Content Analysis Tool (ORCAT)

May 2008 - August 2008

ORCAT is the cousin of GeoCAT and the precursor of DCAT. ORCAT enables the collection, storage, analysis, and display of text and sensor data. Text data is gathered from news sources, blogs, and aggregated searches. Sensor data comes from a collection of MODIS files - containing pixel readings for vegetation indices, surface reflectance, leaf area index, temperature, and net photosynthesis. ORCAT utilizes the same Natural Language Processing technology as GeoCAT to annotate each text document or MODIS file with city names and coordinates. It takes the coordinate list one step further by plotting each point on a map.
[Thanks to Dr. Line C. Pouchard, Dr. Stephen Poole, Joseph Trien, and Stephenie Brown]

Geographic Coordinate Annotation Tool (GeoCAT)

May 2007 - August 2007

One of the most valuable sources of intelligence for military agencies today is open source intelligence (OSINT). OSINT embodies all publicly available information. It is becoming increasingly important to intelligence analysts as knowledge becomes globally available via the Internet. However, the problem with OSINT is that it is so time consuming and inefficient for an analyst to sort through all the available information on any given subject. The goal of this project was to use Semantic Web technologies and Natural Language Processing (NLP) to enhance the search capabilities of software tools used by intelligence analysts. GeoCAT utilizes NLP to find city names within documents and annotate each document with a list of the cities and coordinates found.
[Thanks to Dr. Line C. Pouchard and Joseph Trien]