Anomaly and error detection in computerized materials control & accountability databases Page: 4 of 7
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ANOMALY AND ERROR DETECTION IN COMPUTERIZED MATERIALS
CONTROL & ACCOUNTABILITY DATABASES
Rena Whiteson, Barbara Hoffbauer, and Tresa F. Yarbro
Los Alamos National Laboratory
Los Alamos, New Mexico USA
AlliedSignal Federal Manufacturing & Technologies
Albuquerque, New Mexico USA
United States Department of Energy sites use computerized material control and accountability
(MC&A) systems to manage the large amounts of data necessary to control and account for their
nuclear materials. Theft or diversion of materials from these sites would likely result in anomalies in
the data, and erroneous information greatly reduces the value of the information to its users. There-
fore, it is essential that MC&A data be periodically assessed for anomalies or errors. At Los Alamos
National Laboratory, we have been developing expert systems to provide efficient, cost-effective,
automated error and anomaly detection. Automated anomaly detection can provide assurance of the
integrity of data, reduce inventory frequency, enhance assurance of physical inventory, detect errors
in databases, and gain a better perspective on overall facility operations. The Automated MC&A
Database Assessment Project is aimed at improving anomaly and error detection in MC&A databases
and increasing confidence in the data. We are working with data- from the Los Alamos Plutonium
Facility and the Material Accountability and Safeguards System, the Facility's near-real-time comput-
erized nuclear material accountability and safeguards system. This paper describes progress in cus-
tomizing the expert systems to the needs of the users of the data and reports on our results.
Department of Energy (DOE) sites presently use computerized material control and accountability
(MC&A) systems to account for and track the disposition of nuclear material within the weapons
complex. These MC&A systems are typically highly sophisticated and provide efficient, controlled
access to an enormous amount of information about nuclear material items such as the type of mate-
rial, the amount of material, etc. However, such systems must also provide the capability to evaluate
and validate the integrity of the information in the database. This would include the ability to perform
error checking and error handling as data is entered into the database as well as the means to detect
anomalies in the database that may be associated with the diversion of nuclear material.
The current MC&A system in use at Los Alamos National Laboratory (LANL) is the Material
Accountability and Safeguards System, or MASS. This system allows controlled access to data on
nuclear material and it contains a very large amount of information. The data tends to be very diverse
in nature and sometimes very complex. As a result, manual verification of the data integrity is diffi-
cult and tedious.
In the remainder of this paper, we will describe how we are working with the users of the MC&A
data to develop automated error and anomaly detection tools for database assessment. These tools are
being used today to validate the integrity of the data contained in LANL's MC&A system.
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Whiteson, R.; Hoffbauer, B. & Yarbro, T.F. Anomaly and error detection in computerized materials control & accountability databases, article, September 1, 1997; New Mexico. (digital.library.unt.edu/ark:/67531/metadc691139/m1/4/: accessed October 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.