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Attensity tailors new suite

This article appears in the issue November/December 2004 [Volume 13, Issue 10]

Attensity has released its new Application Suite, which has been designed to fuse unstructured content with existing structured data.

The new Attensity Application Suite includes comprehensive Knowledge Libraries, which provide a knowledgebase to categorize and normalize data for vertical markets. Attensity says its new offering enables customers to quickly assemble structured information in the way their businesses are already organized, expediting implementation and expanding the product's overall usability. Currently, the Application Suite gives customers the option to select Attensity Knowledge Libraries in the areas of automotive, durable goods, technology, hospitality, government intelligence, law enforcement and defense. The full line of business applications currently includes Attensity Customer Care, Attensity Risk Management, Attensity Warranty, Attensity Government Intelligence, Attensity Government Law Enforcement and Attensity Government Logistics.

The offering also includes Attensity Discover (formerly known as Attensity PowerDrill), which is said to help organizations uncover important patterns and relationships in text-based information and then view them in context with structured data. These capabilities, says Attensity, permits users to quickly discover important and previously undetectable patterns and trends, such as quality problems, customer preferences or, in the case of government agencies, terrorist threats.

Powering the Application Suite is Attensity Server, a solution for extracting relational facts, entities and events that describe the "who, what, when, where and why" contained in text-based information. Attensity Server rapidly extracts information in free-form text and organizes it into structured output for analytic applications and data warehouses. Further, says the company, it generates analysis-ready datasets from free-form text sources up to 20,000 times faster than prior processes, with up to 95% accuracy.


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