Software Framework for Intergrative Archival and Maintenance of Large Scale Data

Period of Performance: 01/01/2006 - 12/31/2006

$100K

Phase 1 STTR

Recipient Firm

CyberConnect EZ, LLC
37 Max Felix Dr.
Storrs, CT 06268
Principal Investigator
Firm POC

Research Institution

University of Connecticut
Office for Sponsored Programs 438 Whitney Rd. Ext., U-1133
Storrs, CT 06269
Institution POC

Abstract

The preservation of experimental data gathered by the nuclear physics community is being compromised by the dramatic increase in the rate of data generation. Physicists need to be able to easily archive the data, along with its experimental context and their current analysis procedures, in order to prepare for future breakthroughs in analysis technology and/or potential changes of the group performing the analysis. This project will develop a convenient, ¿visual¿-data-flow archival framework that nuclear physicists can use to annotate the context of the data and encapsulate numerous related items into a cohesive XML (extensible markup language) document. (XML-based archival is the best currently-available strategy, offering higher survivability over the data storage and management technology evolution.) Phase I will add archiving features to an existing visual-data-flow management framework. Then, the framework will be deployed to test the feasibility of using it for archiving on-going collaborative research projects involving nuclear physicists from multiple organizations. Finally, the framework's capability to support remote and distributed operation over the Internet will be evaluated. Commercial Applications And Other Benefits as described by the Applicant: The software framework should be useful not only in physics but also in many other data and computationally intensive areas, such as computational biology, chemistry, engineering, finance, seismic processing, climate modeling, astronomy, and simulation. In addition, the intelligence and homeland security branches of the federal government also should benefit, as they need to archive large amount of surveillance data and preserve their analysis for a prolonged period of time.