STAR [electronic resource] : Software Toolkit for Analysis Research
- Washington, D.C. : United States. Dept. of Energy, 1993. and Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Physical Description:
- 5 pages : digital, PDF file
- Additional Creators:
- Los Alamos National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Analyzing vast quantities of data from diverse information sources is an increasingly important element for nonproliferation and arms control analysis. Much of the work in this area has used human analysts to assimilate, integrate, and interpret complex information gathered from various sources. With the advent of fast computers, we now have the capability to automate this process thereby shifting this burden away from humans. In addition, there now exist huge data storage capabilities which have made it possible to formulate large integrated databases comprising many thereabouts of information spanning a variety of subjects. We are currently designing a Software Toolkit for Analysis Research (STAR) to address these issues. The goal of STAR is to Produce a research tool that facilitates the development and interchange of algorithms for locating phenomena of interest to nonproliferation and arms control experts. One major component deals with the preparation of information. The ability to manage and effectively transform raw data into a meaningful form is a prerequisite for analysis by any methodology. The relevant information to be analyzed can be either unstructured text structured data, signals, or images. Text can be numerical and/or character, stored in raw data files, databases, streams of bytes, or compressed into bits in formats ranging from fixed, to character-delimited, to a count followed by content The data can be analyzed in real-time or batch mode. Once the data are preprocessed, different analysis techniques can be applied. Some are built using expert knowledge. Others are trained using data collected over a period of time. Currently, we are considering three classes of analyzers for use in our software toolkit: (1) traditional machine learning techniques, (2) the purely statistical system, and (3) expert systems.
- Published through SciTech Connect., 08/01/1993., "la-ur--93-2609", " conf-930749--57", "DE93018539", 34. annual meeting of the Institute of Nuclear Materials Management,Scottsdale, AZ (United States),18-21 Jul 1993., and Thomas, T.R.; Whiteson, R.; Doak, J.E.; Prommel, J.M.; Hoffbauer, B.L.; Helman, P.
- Funding Information:
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