DETECTING ERRORS AND ANOMALIES IN COMPUTERIZED MATERIALS CONTROL & ACCOUNTABILITY DATABASES*

The Automated MC&A Database Assessment project is aimed at improving anomaly and error detection in materials control and accountability (MC&A) databases and increasing confidence in the data that they contain. Anomalous data resulting in poor categorization of nuclear material inventories greatly reduces the value of the database information to users. Therefore it is essential that MC&A data be assessed periodically for anomalies or errors. Anomaly detection can identify errors in databases and thus provide assurance of the integrity of data. An expert system has been developed at Los Alamos National Laboratory that examines these large databases for anomalous or erroneous data. For several years, MC&A subject matter experts at Los Alamos have been using this automated system to examine the large amounts of accountability data that the Los Alamos Plutonium Facility generates. These data are collected and managed by the Material Accountability and Safeguards System, a near-real-time computerized nuclear material accountability and safeguards system. This year we have expanded our user base, customizing the anomaly detector for the varying requirements of different groups of users. This paper describes our progress in customizing the expert systems to the needs of the users of the data and reports on our results.