# Piranha: Intelligent Agent Text Analysis

Thomas E. Potok, Ph.D.

Applied Software Engineering Research Group

Oak Ridge National Laboratory

Oak Ridge, TN 37831-6085

potokte@ornl.gov

1-865-574-0834

Piranha is a knowledge discovery engine designed to make sense of the
vast amounts of dynamic information and to discover the "gold nuggets"
that may often overlook. In a matter of minutes, Piranha can ingest tens
of thousands of electronic documents, analyze them, and provide relevant
information to make sense of them. Used by the intelligence community,
the military, industry, and government, Piranha has successfully
demonstrated the power of combining leading edge research and solid
engineering against real world problems.

![](media/image1.png){width="4.75in" height="3.9166666666666665in"}

We have pioneered an intelligent agent approach to clustering large
volumes of textual information using our patented "Term
Frequency-Inverse Corpus Frequency" (TFICF) algorithm\[9, 10\]. This
algorithm allows for documents to be processed in parallel, as opposed
to traditional approaches which require serial processing. This ability
to process documents in parallel enables us to assign a document to an
intelligent agent who can then work with other agent on a single
computer, or over a large set of computers. These intelligent agents
work together to compare, filter, categorize, cluster, and visualize
large sets of electronic documents. This method works two orders of
magnitude faster than traditional approaches, and through extensive
experimentation, has been shown to be at least as accurate as
traditional methods \[9\].

![](media/image2.png){width="4.375in" height="3.6in"}

The Piranha tool has been built with significant investment using
pioneering research and solid engineering to help people make sense of
the data that they currently have, and often overlook. Piranha can take
thousands of electronic documents and in a matter of minutes help an
analyst make sense of them. We have a number of ongoing projects with
the intelligence community, the U.S. military, and the U.S. Department
of Homeland Security in which Piranha is being used to analyze very
large sets of

streaming documents.

\[1\] X. Cui and T. E. Potok, \"Document Clustering Analysis Based on
Hybrid PSO+K-means

Algorithm,\" *Journal of Computer Sciences,* vol. Special Issue, pp.
27-33, 2005.

\[2\] X. Cui and T. E. Potok, "A distributed agent implementation of
multiple species Flocking model for document partitioning clustering,"
in *Lecture Notes in Computer Science*. vol. 4149 NAI Edinburgh, United
Kingdom: Springer Verlag, Heidelberg, D-69121, Germany, 2006, pp.
124-137.

\[3\] X. Cui, T. E. Potok, and P. Palathingal, \"Document clustering
using particle swarm optimization,\" in *Proceedings of 2005 IEEE Swarm
Intelligence Symposium* Pasadena, CA, USA: IEEE, 2005, pp. 185-91.

\[4\] M. T. Elmore, T. E. Potok, and F. T. Sheldon, \"Dynamic data
fusion using an ontology-based

software agent system,\" in *Proceedings of 7th World Multiconference on
Systemics, Cybernetics*

*and Informatics (SCI 2003)* vol. Vol.9 Orlando, FL, USA: IIIS, 2003,
pp. 5-10.

\[5\] P. Palathingal, T. E. Potok, and R. M. Patton, \"Agent based
approach for searching, mining and managing enormous amounts of spatial
image data,\" in *Proceedings of the Eighteenth*

*International Florida Artificial Intelligence Research Society
Conference, FLAIRS 2005 - Recent*

*Advances in Artifical Intelligence* Clearwater Beach, FL, United
States: American Association for

Artificial Intelligence, Menlo Park, CA 94025-3496, United States, 2005,
pp. 351-356.

\[6\] R. M. Patton and T. E. Potok, \"Characterizing large text corpora
using a maximum variation

sampling genetic algorithm,\" in *Proceedings of the 8th annual
conference on Genetic and*

*evolutionary computation* Seattle, Washington, USA: ACM Press, 2006.

\[7\] T. E. Potok, M. T. Elmore, J. W. Reed, and N. F. Samatova, \"An
ontology-based HTML to XML conversion using intelligent agents,\" in
*Proceedings of the 35th Annual Hawaii International Conference on
System Sciences* Big Island, HI, USA: IEEE Comput. Soc, 2002, pp.
1220-9.

\[8\] P. Yan, Y. Jiao, A. R. Hurson, and T. E. Potok, \"Semantic-based
information retrieval of

biomedical data,\" in *Proceedings of the 2006 ACM symposium on Applied
computing* Dijon,

France: ACM Press, 2006.

\[9\] J. Reed, Y. Jiao, T. E. Potok, B. Klump, M. Elmore, and A. R.
Hurson, \"TF-ICF: A New Term Weighting Scheme for Clustering Dynamic
Data Streams,\" in *Proceedings of 5th International Conference on
Machine Learning and Applications (ICMLA\'06)*. vol. 0 ORLANDO, FL,
2006,

pp. 258-263.

\[10\] J. W. Reed, T. E. Potok, and R. M. Patton, \"A multi-agent system
for distributed cluster analysis,\" in *Proceedings of Third
International Workshop on Software Engineering for Large-Scale
Multi-Agent Systems (SELMAS\'04)\" W16L Workshop - 26th International
Conference on Software Engineering* Edinburgh, Scotland, UK: IEE, 2004,
pp. 152-5.