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 The challenge most people face is to find a way for computers to sift through a large amount of data, and provide them with accurate and relevant information. This requires software that can quickly filter, relate, and show documents and relationships to them.
We have pioneered a software agent approach to text analysis that uses a large number of agents distributed over very large computer
clusters. This method works much faster than traditional approaches and provides the capability to cluster massive amounts of textual information in relatively short amounts of time, due to the scalability of the agent architecture.
Product Summary
There is massive amount of data available that cannot be manually analyzed. Computers can provide some help in this problem, but the shear volumes of data make the most promising approaches impractical. We have developed intelligence agent-based technology that allows for advanced textual analysis to be done on very large and dynamic data with unprecedented accuracy.
This capability can allow you to discover new opportunities or areas of concern in data that you already have. This capability has been vetted in the scientific community, as well as a number of real world applications.
Highlights of Piranha’s Capabilities
Finding Similar Documents: You can select a document of interest, and then quickly find other documents that are a close match to it. For example, you may have an e-mail message of interest. Clustering allows you to find similar e-mail on other computers to be quickly found, thus potentially establishing a link.
Document Sampling: A set of documents will usually contain common themes or topic. Representative documents from these themes can be quickly found, and presented to analyst. For example, hard drive may have thousands of documents representing many different topics, i.e., form finances to methods to favorite restaurants. Ten or twenty representative documents from these themes can be found, and used by an analyst to quickly determine what they mean.
Classifying Documents: A set of representative documents can be used by an analyst to define a topic of interest, and then related documents can be added to that set. This allows an analyst to pick specific documents that are of interest, perhaps nuclear materials, then allow the agents to automatically put related document into the same “potential customer” folder.
Brief Tutorial of Piranha
Click here to view how to get started with Piranha.
Click on the link above to try Piranha. Note that this version only allows 128 documents. To obtain a version which allows more documents, please contact us.
If you can not run JNLP applications, you can download the Piranha JAR file and run it using the following command:
java -Xmx256m -jar piranha_fat.jar
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