Real-time data mining of non-stationary data streams from sensor networks

Authors:
Lior Cohen;Gil Avrahami-Bakish;Mark Last;Abraham Kandel;Oscar Kipersztok
Affiliations:
Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel;Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel;Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel;Department of Computer Science and Engineering, University of South-Florida, Tampa, FL 33620, United States;Boeing Phantom Works, Mathematics and Computing Technology, P.O. Box 3707 MC 7L-44, Seattle, WA 98124-2207, United States
Venue:
Information Fusion
Year:
2008

Citing 8
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Abstract

In real-world sensor networks, the monitored processes generating time-stamped data may change drastically over time. An online data-mining algorithm called OLIN (on-line information network) adapts itself automatically to the rate of concept drift in a non-stationary data stream by repeatedly constructing a classification model from every sliding window of training examples. In this paper, we introduce a new real-time data-mining algorithm called IOLIN (incremental on-line information network), which saves a significant amount of computational effort by updating an existing model as long as no major concept drift is detected. The proposed algorithm builds upon the oblivious decision-tree classification model called ''information network'' (IN) and it implements three different types of model updating operations. In the experiments with multi-year streams of traffic sensors data, no statistically significant difference between the accuracy of the incremental algorithm (IOLIN) vs. the regenerative one (OLIN) has been observed.