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Embedded Space Analytics

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0064
Agency Tracking Number: N16A-020-0228
Amount: $967,032.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N16A-T020
Solicitation Number: 2016.0
Timeline
Solicitation Year: 2016
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-11-20
Award End Date (Contract End Date): 2019-11-19
Small Business Information
320 Whittington PKWY
Louisville, KY 40222
United States
DUNS: 877380530
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Bin Xie
 (502) 371-0907
 Bin.Xie@InfoBeyondtech.com
Business Contact
 Bin Xie
Phone: (502) 371-0907
Email: Bin.Xie@InfoBeyondtech.com
Research Institution
 Louisville Research Foundation, Inc.
 Lauren Goralski
 
300 East Market Street, Suite 300
LOUISVILLE, KY 40202-0195
United States

 (502) 852-2597
 Nonprofit college or university
Abstract

Navy needs a real-time graph embedding tool for analyzing huge graphs (millions of nodes and billions of edges) from diverse sources. However, current approaches cannot provide dynamic and scalable graph analytics to signify the military value of tactical data. In this project, InfoBeyond advocates EStreaming (Embedding & Streaming) for scalable and efficient graph streaming. EStreaming promotes big data streaming technology where unsupervised and semi-supervised machine learning algorithms can be conducted over the streaming platform. It can split a huge graph into small subgraphs such that distributed graph embedding can be conducted in parallel among a set of processors. Meanwhile, the graph embedding can be effectively merged and visualized. Considering the diversity of Navy applications, EStreaming is an open platform that can implement various graph embedding algorithms. Compared to other algorithms, DLINE can be conducted from the internal relations and the similarity among the persons in the solider, enemy, and other social networks. EStreaming has a learning model to integrate supervised, unsupervised, and semi-supervised learning algorithms for analyzing the embedded data. It allows dynamically and continuously tracking nodes/behaviors/events/attacks and capture perishable opportunities for decision making in response to vulnerable graph events. These capabilities are not provided in the traditional approaches.

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

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