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Data Compression for Bandwidth Constrained Environments

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00024-12-P-4056
Agency Tracking Number: N121-052-0557
Amount: $80,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N121-052
Solicitation Number: 2012.1
Timeline
Solicitation Year: 2012
Award Year: 2012
Award Start Date (Proposal Award Date): 2012-05-29
Award End Date (Contract End Date): N/A
Small Business Information
12140 Woodcrest Executive Drive Suite 250
St. Louis, MO -
United States
DUNS: 867726424
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Kerry Neeley
 Principal SW Engineer
 (314) 590-0259
 neeleym@ociweb.com
Business Contact
 Malcolm Spence
Title: Director of Business Deve
Phone: (314) 590-0206
Email: spence_m@ociweb.com
Research Institution
 Stub
Abstract

We propose to extend existing free and open source software to construct a framework solution which implements a multi stage process to support the SBIR requirements. The baseline approach includes an encoding stage followed by a compression stage with optional encryption and transmission stages. Our approach referred to as"Framework for Rule-based Encoding and Stream COmpression (FRESCO)"leverages proven techniques developed for the finance industry to support high volume market data in a low latency manner. FRESCO's first stage analyzes source data and encodes it for the purpose of reducing redundant content and preparing it for optimal compression. Rules written in XML govern encoding behavior and are configuration managed to support data life-cycle. Rules are loaded and interpreted at runtime removing the need to modify encoding software. Automated tools analyze representative source data to assist in development of encoding rules. The second stage is responsible for receiving content from the encoding stage and selecting an appropriate best-of-breed compression algorithm optimized for the encoded data. FRESCO will accommodate pluggable compression components to achieve greater product flexibility. Preliminary testing with candidate data shows we are able to exceed the goal of 1100:1 compression.

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

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