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Monitoring Incremental Histogram Distribution for Change Detection in Data Streams

  • Conference paper
Knowledge Discovery from Sensor Data (Sensor-KDD 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5840))

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Abstract

Histograms are a common technique for density estimation and they have been widely used as a tool in exploratory data analysis. Learning histograms from static and stationary data is a well known topic. Nevertheless, very few works discuss this problem when we have a continuous flow of data generated from dynamic environments.

The scope of this paper is to detect changes from high-speed time-changing data streams. To address this problem, we construct histograms able to process examples once at the rate they arrive. The main goal of this work is continuously maintain a histogram consistent with the current status of the nature. We study strategies to detect changes in the distribution generating examples, and adapt the histogram to the most recent data by forgetting outdated data. We use the Partition Incremental Discretization algorithm that was designed to learn histograms from high-speed data streams.

We present a method to detect whenever a change in the distribution generating examples occurs. The base idea consists of monitoring distributions from two different time windows: the reference window, reflecting the distribution observed in the past; and the current window which receives the most recent data. The current window is cumulative and can have a fixed or an adaptive step depending on the distance between distributions. We compared both distributions using Kullback-Leibler divergence, defining a threshold for change detection decision based on the asymmetry of this measure.

We evaluated our algorithm with controlled artificial data sets and compare the proposed approach with nonparametric tests. We also present results with real word data sets from industrial and medical domains. Those results suggest that an adaptive window’s step exhibit high probability in change detection and faster detection rates, with few false positives alarms.

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Author information

Authors and Affiliations

  1. LIAAD, INESC Porto, L.A. Rua de Ceuta, 118, 6, 4050-190, Porto, Portugal

    Raquel Sebastião, João Gama & Pedro Pereira Rodrigues

  2. Faculty of Science, University of Porto,  

    Raquel Sebastião & Pedro Pereira Rodrigues

  3. Faculty of Economics, University of Porto,  

    João Gama

  4. Faculty of Medicine, University of Porto,  

    Pedro Pereira Rodrigues & João Bernardes

  5. INEB, Porto

    João Bernardes

Authors
  1. Raquel Sebastião
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  2. João Gama
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  3. Pedro Pereira Rodrigues
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  4. João Bernardes
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Editor information

Editors and Affiliations

  1. Centre for Distributed Systems and Software Engineering, Monash University, 900 Dandenong Road, Caulfield East, 3145, Melbourne, VIC, Australia

    Mohamed Medhat Gaber

  2. Oak Ridge National Laboratory, Computational Sciences and Engineering Division, 37831, Oak Ridge, TN, USA

    Ranga Raju Vatsavai , Olufemi A. Omitaomu  & Auroop R. Ganguly ,  & 

  3. Faculty of Economics, LIAAD-INESC Porto, University of Porto, L.A. Rua de Ceuta, 118, 6, 4050-190, Porto, Portugal

    João Gama

  4. Computer Science and Engineering Department, University of Notre Dame, 353 Fitzpatrick Hall, 46556, Notre Dame, IN, USA

    Nitesh V. Chawla

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Sebastião, R., Gama, J., Rodrigues, P.P., Bernardes, J. (2010). Monitoring Incremental Histogram Distribution for Change Detection in Data Streams. In: Gaber, M.M., Vatsavai, R.R., Omitaomu, O.A., Gama, J., Chawla, N.V., Ganguly, A.R. (eds) Knowledge Discovery from Sensor Data. Sensor-KDD 2008. Lecture Notes in Computer Science, vol 5840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12519-5_2

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  • DOI: https://doi.org/10.1007/978-3-642-12519-5_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12518-8

  • Online ISBN: 978-3-642-12519-5

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