Publications

Aas, Christopher A.; Bray, Olin H.; Lenhart, James E.; Witcher, Christina J.

Sandia National Laboratories was tasked with developing the Defense Nuclear Material Stewardship Integrated Inventory Information Management System (IIIMS) with the sponsorship of NA-125.3 and the concurrence of DOE/NNSA field and area offices. The purpose of IIIMS was to modernize nuclear materials management information systems at the enterprise level. Projects over the course of several years attempted to spearhead this modernization. The scope of IIIMS was broken into broad enterprise-oriented materials management and materials forecasting. The IIIMS prototype was developed to allow multiple participating user groups to explore nuclear material requirements and needs in detail. The purpose of material forecasting was to determine nuclear material availability over a 10 to 15 year period in light of the dynamic nature of nuclear materials management. Formal DOE Directives (requirements) were needed to direct IIIMS efforts but were never issued and the project has been halted. When restarted, duplicating or re-engineering the activities from 1999 to 2003 is unnecessary, and in fact future initiatives can build on previous work. IIIMS requirements should be structured to provide high confidence that discrepancies are detected, and classified information is not divulged. Enterprise-wide materials management systems maintained by the military can be used as overall models to base IIIMS implementation concepts upon.

Conrad, Gregory N.; Britanik, John M.; Deland, Sharon M.; Witcher, Christina J.; Deland, Sharon M.

This report describes the results of a Laboratory-Directed Research and Development project on techniques for pattern discovery in discrete event time series data. In this project, we explored two different aspects of the pattern matching/discovery problem. The first aspect studied was the use of Dynamic Time Warping for pattern matching in continuous data. In essence, DTW is a technique for aligning time series along the time axis to optimize the similarity measure. The second aspect studied was techniques for discovering patterns in discrete event data. We developed a pattern discovery tool based on adaptations of the A-priori and GSP (Generalized Sequential Pattern mining) algorithms. We then used the tool on three different application areas--unattended monitoring system data from a storage magazine, computer network intrusion detection, and analysis of robot training data.