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Multiscale Fast and Distributed Data and Statistics Summarization

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-17-P-0039
Agency Tracking Number: A17A-010-0121
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A17A-T010
Solicitation Number: 2017.0
Timeline
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-05-24
Award End Date (Contract End Date): 2017-11-30
Small Business Information
15400 Calhoun Drive
Rockville, MD 20855
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Chris Kurcz
 Senior Research Scientist
 (301) 294-4260
 ckurcz@i-a-i.com
Business Contact
 Mr. Mark James
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
 Johns Hopkins University
 Ms. Joan F Warfield
 
600 N Wyman Park Building, Office of the Dean 3400 N. Charles Street, 600N Wyman Park Bldg.
Baltimore, MD 21218
United States

 (410) 516-6126
 Nonprofit College or University
Abstract

This work seeks to demonstrate the wide spread applicability of Geometric Multi-Resolution Analysis (GMRA) to uncover low dimensional structure in large volume and high dimensional data sets. The objective is to show that the GMRA approach applies naturally to a variety of data types and to demonstrate its practical implementation in a scalable data intensive computing environment. The GMRA approach has many beneficial characteristics including linear scaling with the number of data points, multi-resolution representation of the data, robust to noise, provable error estimates, amenable to fast algorithms, and suitability for visualization and subsequent analysis. The coarse scale representation is a form summarization where separated partitions approximate the data. In addition, the representation at fine scales encode details and provide means to zoom into particular structures of the data. We will demonstrate that GMRA provides a natural structure to perform a variety of data analysis tasks including clustering, anomaly detection, change detection and visualization.

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

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