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RiskWatch: Linking Environmental Stressors to Security Outcomes

Award Information
Agency: Department of Defense
Branch: Army
Contract: W912HZ-17-P-0101
Agency Tracking Number: A17A-021-0144
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A17A-T021
Solicitation Number: 2017.0
Timeline
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-07-07
Award End Date (Contract End Date): 2018-02-28
Small Business Information
2361 Rosecrans Avenue, Suite 348, El Segundo, CA, 90245
DUNS: 053003017
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Steven Minton
 President
 (310) 383-9234
 sminton@inferlink.com
Business Contact
 Steven Minton
Phone: (310) 383-9234
Email: sminton@inferlink.com
Research Institution
 The University of California at Santa Cruz
 Jennifer Chu
 1156 High Street
Santa Cruz, CA, 95064
 (831) 502-7081
 Nonprofit college or university
Abstract
Many environmental factors can potentially affect a communitys security. In this project, we propose to develop an open source system, called RiskWatch, for harvesting data from multiple sources and linking that data to security outcomes. Relevant data can potentially be found in a wide variety of sources, including structured, numeric data sets and images published by governments and NGOs, in natural language in reports and news articles, or even in informal text aggregated from social media. The difficulty of reliably forecasting security outcomes is amplified by the fact that there are so many types of relevant data, and so many ways to express that data. RiskWatch will include capabilities for extracting and interpreting data about environmental stressors published in a wide variety of sources. To forecast security outcomes, we will employ Probabilistic Soft Logic (PSL), a recently developed machine learning approach that has been employed successfully for many applications, including forecasting civil unrest from open source indicators. In Phase I, we will develop an end-to-end design of the RiskWatch architecture and show that our approach is feasible, using sample data sources.

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

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