Intuitive Information Fusion and Visualization

Award Information
Agency:
Department of Defense
Amount:
$149,780.00
Program:
SBIR
Contract:
W911QX-13-C-0107
Solitcitation Year:
2012
Solicitation Number:
2012.3
Branch:
Army
Award Year:
2013
Phase:
Phase I
Agency Tracking Number:
O123-LD4-2030
Solicitation Topic Code:
OSD12-LD4
Small Business Information
Soar Technology, Inc.
3600 Green Court, Suite 600, Ann Arbor, MI, -
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
009485124
Principal Investigator
 Jack Zaientz
 Lead Scientist
 (734) 887-7621
 jzaientz@soartech.com
Business Contact
 Andrew Dallas
Title: Vice President
Phone: (734) 887-7603
Email: contracts@soartech.com
Research Institution
N/A
Abstract
Traditional Information Fusions (IF) systems need to be expanded to encompass a decision support system (DSS) role that enables analysts and commander to manage the volume and heterogeneity of sensor data, including the textual"soft"data descriptive of the social cultural landscape characteristics of contemporary missions. To achieve these improvements, SoarTech, supported by NCSU, will develop Sensemaking Patterns for Analysis and Decision-making (SPADE) a hybrid human computer interaction IF/DSS systems that draws its requirements from abductive hypothesis generation, a feature of sophisticated human decision models including sense-making and recognition primed decision-making. SPADE will apply hierarchical task network and hypothesis graphs to support temporal, spatial, semantic hypothesis definitions. SPADE uses user populated hypothesis patterns to auto-generate and adapt queries for existing, vetted data processing algorithms such as sentiment analysis and text indexing. This will enable analysts and decision makers to easily encode analytic and mission-planning hypothesis, including decision and time constraints, and automatically gain data management and integration, emergent concept detection, and hypothesis testing across traditional INTs and social data. SPADE will better support the coupling of data and decision needs, better mitigate analysis biases, and better support analysis and decision-making under uncertainty and time constraints.

* information listed above is at the time of submission.

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