Development of a real-time data quality monitoring system using embedded intelligence

Author

Bethem, Thomas ; Evans, Michael ; Vafaie, Haleh ; Shaughnessy, Mark

Author_Institution

NOAA Ocean Service, Silver Spring, MD, USA

Volume

3

fYear

2002

fDate

29-31 Oct. 2002

Firstpage

1820

Abstract

Rule-based reasoning and case-based reasoning have emerged as two important and complimentary reasoning methodologies in the field of artificial intelligence (AI). This paper describes the development of a real-time data quality monitoring system (CORMS AI) using case-based and rule-based reasoning. CORMS AI was developed to augment an existing decision support system (CORMS Classic) for monitoring the quality of environmental data and information and their respective computer based systems for use in NOAA Ocean Service´s oceanographic operational products.

Keywords

case-based reasoning; data acquisition; data analysis; decision support systems; monitoring; oceanographic equipment; oceanographic techniques; real-time systems; CORMS AI; CORMS Classic; NOAA Ocean Service; artificial intelligence; case-based reasoning; computer based systems; data quality monitoring system; decision support system; embedded intelligence; oceanography; real-time system; reasoning methodology; rule-based reasoning; Application software; Artificial intelligence; Computer architecture; Computerized monitoring; Intelligent systems; Navigation; Oceans; Quality control; Real time systems; Temperature sensors;

fLanguage

English

Publisher

ieee

Conference_Titel

OCEANS '02 MTS/IEEE

Print_ISBN

0-7803-7534-3

Type

conf

DOI

10.1109/OCEANS.2002.1191909

Filename

1191909