CORMS II: A Prototype for Using Rule Based and Case- Based Reasoning for Monitoring Real Time Oceanographic Data

Dr. Haleh Vafaie (under contract by CO-OPS)
National Ocean Service / Center for Operational Oceanographic Products and Services (CO-OPS)

The Continuous Operational Real-Time Monitoring System (CORMS) is a manned quality control support system implemented in April, 1998 which provides 24 hour a day, 7 day a week quality control monitoring of water level, current, and other marine environmental information. It is the focal point of operations within the Center for Operational Oceanographic Products and Services (CO-OPS). In addition to data monitoring, CORMS provides real-time monitoring of all main computer-based system components associated with the real-time systems and the processes that run on them. It also provides the watch standing personnel with the ability to communicate when necessary, around the clock, with operational standby technical personnel. The two primary program areas monitored by CORMS are the Physical Oceanographic Real-Time System (PORTS) and the National Water Level Observation Network (NWLON). The primary input to CORMS is from real-time water level, current, and other marine environmental sensors, which are deployed nationwide in many U.S. ports and waterways as a part of PORTS and NWLON. The primary purpose of CORMS is to ensure the availability and accuracy of real-time data provided by the CO-OPS that is used for navigational safety and the protection of life and property.

Given an expected increase in the number and complexity of data systems and subsystems to monitor, CO-OPS has contracted with LOGICON FDC to design and implment a prototype CORMS II system that will utilize a rule and case-based COTS engine to assist watchtanders in making more consistent and accurate decisions identifying sensor and other types of systems failures.

This presentation will address how software of this special nature can assist in providing guidance and decision making information to the user within the context of real time quality control of data.

BIO - Dr. Haleh Vafaie Dr. Haleh Vafaie is a senior scientist and manager of the Business Intelligence practice area at Logicon Federal Data. Her research interests include machine learning, intelligent information extraction, knowledge acquisition, decision support systems, data mining, and data modeling. Dr. Vafaie has over 14 years of specialized experience in the area of data mining and machine learning and is the author or co-author of over 25 publications in these areas. She has reviewed papers for many conferences and journals, organized invited sessions, and is a member of the organizing committee for the International Conference on Tools with Artificial Intelligence. Dr. Vafaie received her Ph.D. in Information Technology and Engineering from George Mason University in 1997.

Conference Center - Paper Wednesday - 9:00 - 9:20 A.M.