Centering Resonance Analysis: A superior data mining algorithm for textual data streams

Centering Resonance Analysis: A superior data mining algorithm for textual data streams

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9550-05-C-0023
Agency Tracking Number: F033-0063
Amount: $749,277.00
Phase: Phase II
Program: STTR
Awards Year: 2005
Solicitation Year: 2003
Solicitation Topic Code: AF03T011
Solicitation Number: N/A
Small Business Information
5412 W. Harrison Ct., Chandler, AZ, 85226
DUNS: 129119561
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dan Ballard
 PI
 (480) 615-0159
 ballard-d@cox.net
Business Contact
 Kevin Dooley
Title: COO
Phone: (480) 236-9588
Email: dooley@crawdadtech.com
Research Institution
 ARIZONA STATE UNIV.
 Meghan McKendry
 ORSPA, PO Box 873503
Tempe, AZ, 85287
 (480) 965-1436
 Nonprofit college or university
Abstract
An increasing amount of information comes in the form of streaming text: news media, email, and even human conversation. Creating insight from streaming text has been challenging though, because current knowledge discovery systems depend on expert analysts to move from simple data to actionable knowledge. In order for such systems to be used outside a small cadre of experts, they must incorporate deep analytics, which are information rich and diagnostic in nature, and when combined with domain knowledge, point to action. In Phase I, Crawdad Technologies demonstrated the superior text mining performance and scalability of its Centering Resonance Analysis (CRA). A topic tracking system based on CRA had up to five times better precision than ones based on existing technologies. In Phase II, Crawdad will develop deep analytics that capture (a) the temporal, dynamic nature of streaming text, and (b) the social dynamic that underlies many streaming text applications. We will create Dynamic CRA (DCRA) and Multi-Agent DCRA to model streaming text in applications such as communication monitoring, news streams, team and group conversations, and customer service interactions. The use of DCRA and Multi-Agent DCRA will be tested in an actual news streaming environment.

* Information listed above is at the time of submission. *

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