Probabilistic models for anomaly detection in remote sensor data streams

Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007

Volumn , Issue , 2007, Pages 75-82

  Dereszynski, Ethan W ^a   Dietterich, Thomas G ^a  

^a 1148 Kelley Engineering Center Corvallis   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AIR TEMPERATURE; ANOMALY DETECTION; APPLICATION ENVIRONMENT; AUTOMATED DATA; DATA CLEANING; DATA STREAM; DOMAIN EXPERTS; DYNAMIC BAYESIAN NETWORK MODELS; ECOLOGICAL DATA; EXTREME CONDITIONS; FAULT MODEL; H.J. ANDREWS EXPERIMENTAL FOREST; HISTORICAL DATA; PRECISION AND RECALL; PROBABILISTIC MODELS; REMOTE SENSORS; SENSOR DATA; SENSOR FAILURE; TEMPERATURE VARIATION; TEMPORAL RESOLUTION; TIME RESOLUTION;

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; REMOTE SENSING;

DATA COMMUNICATION SYSTEMS;

EID: 79551654496     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Aradhye, H.B. (1997). Sensor Fault Detection, Isolation, and Accommodation Using Neural Networks, Fuzzy Logic, and Bayesian Belief Networks. M.S. Diss, University of New Mexico, Albuquerque, NM.
2. Dean, T., Kanazawa, K. (1988). Probabilistic temporal reasoning. AAAI88, 524-528.
3. Dechter, R. (1996). Bucket Elimination: A Unifying Framework for Probabilistic Inference. UAI96, 211-219.
4. Gray, J., Szalya, A.S. (2002) The World-Wide Telescope, an Archetype for Online Science. MSR Technical Report MSR-TR-2002-75.
5. Hill D.J., Minsker, B.S. (2006). Automated Fault Detection for In-Situ Environmental Sensors. Proceedings of the 7th International Conference on Hydroinformatics.
6. Ibarguengoytia, P.H., Sucar, L.E., Vadera, S. (1996). A Probabilistic Model for Sensor Validation UAI96, 332-333.
7. Lauritzen, S.L. (1992). Propogation of Probabilities, Means, and Variance in Mixed Graphical Association Models. JASA, 87(420):1098-1108.
8. McKee, W. 2005. Meteorological data from benchmark stations at the Andrews Experimental Forest: Long-Term Ecological Research. Corvallis, OR: Forest Science Data Bank: MS001. (26 May 2007)
9. Mehranbod, N., Soroush, M., Piovos, M., Ogunnaike, B. A. (2003) Probabilistic Model for Sensor Fault Detection and Identification. AIChe Journal, 49(7):1787-1802. (Pubitemid 36871178)
10. Nicholson, A.E., Brady, J.M. (1992). Sensor Validation Using Dynamic Belief Networks. UAI92, 207-214.
11. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, Ca.
12. Rabiner, L.R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2):257-286.