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TACTIC-D II: Techniques to Adjust Computational Trends Involving Changing Data

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-19-C-0133
Agency Tracking Number: N17B-032-0016
Amount: $991,743.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N17B-T032
Solicitation Number: 17.B
Solicitation Year: 2017
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-03-06
Award End Date (Contract End Date): 2022-05-10
Small Business Information
12 Gill Street Suite 1400
Woburn, MA 01801-0000
United States
DUNS: 967259946
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Alan Carlin
 Principal Research Engineer
 (781) 496-2408
Business Contact
 Thomas McKenna
Phone: (781) 496-2443
Research Institution
 Tufts University
 Luke Baccari Luke Baccari
169 Holland St. Tufts Admin. Bldg.
Somerville, MA 02144-0000
United States

 (617) 627-3203
 Nonprofit College or University

The US Navy recognizes the potential for “big data” to facilitate force readiness. However, the analysis of such data presents numerous challenges, among them, (1) the evolving nature of Navy tactics, hindering apples-to-apples comparisons; (2) the presence of many interdependent platforms, making data complex; and (3) presenting analyses to humans, making understandability important. Aptima’s approach to the topic Techniques to Adjust Computational Trends Involving Changing Data (TACTIC-D) is a big data analytics solution that utilizes training and qualifications data to discover training opportunities that will enhance force readiness. Our solution leverages understanding of the relationships between performance measures and assessments to maintain relevance in a changing tactical environment. Probabilistic graphical models and automated machine learning are proposed as ways to represent these relationships, facilitating detection and understanding of changing tactical behaviors. Our solution can adapt to changing data, enabling associated analytics to scale from one to many platforms, thus offering a service-wide solution. Advanced data visualizations make the results of mathematical modeling comprehensible, facilitating identification of opportunities for training interventions and helping key decisionmakers understand the source of variability in their data.

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

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