STREAM: Streamlined TRaining Extraction and Architecture Model

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911QX-12-C-0058
Agency Tracking Number: A121-027-0534
Amount: $99,074.00
Phase: Phase I
Program: SBIR
Awards Year: 2012
Solicitation Year: 2012
Solicitation Topic Code: A12-027
Solicitation Number: 2012.1
Small Business Information
12 Gill Street, Suite 1400, Wo, MA, -
DUNS: 967259946
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Charlotte Shabarekh
 Modeling and Simulation Scientist
 (781) 496-2465
 cshabarekh@aptima.com
Business Contact
 Thomas McKenna
Title: Chief Financial Officer
Phone: (781) 496-2443
Email: mckenna@aptima.com
Research Institution
N/A
Abstract
Considerable effort is required to establish and maintain a centralized organization of training capabilities to facilitate the identification, development and delivery of training. Many tools for structuring information, and extracting and analyzing knowledge from source documents exist in the commercial, academic, and government communities. However, these techniques and technologies have rarely been applied in the training domain since training is often derived from SME driven information and representations. Aptima, Inc. proposes to develop the Streamlined TRaining Extraction and Architecture Model (STREAM) which focuses on supporting training developers through the creation of a robust, mutable, structured data representation that organizes the wide range of training information. STREAM leverages enhanced machine learning technologies and advanced visualization techniques. The resulting data representation will support navigation of the complex relationships which will facilitate improvements in training development with less reliance on the limited availability of SME expertise. A database structured with this representation would provide a centralized authoritative repository that would capture and organize multiple training solutions.

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

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