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

Lior Cohen, Gil Avrahami-Bakish, Mark Last, Abraham Kandel, Oscar Kipersztok

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

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.

Original languageEnglish
Pages (from-to)344-353
Number of pages10
JournalInformation Fusion
Volume9
Issue number3
DOIs
StatePublished - 1 Jul 2008

Keywords

  • Concept drift
  • Incremental learning
  • Information networks
  • Online learning
  • Real-time data mining
  • Traffic sensor networks

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture

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