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Summarizing and Detecting Structural Drifts from Multiple Data Streams

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Classification and Data Mining

Abstract

In recent years the analysis of data streams has received a lot of attention. This is motivated by the increase of the number of applications which generate huge amounts of high speed temporal data. Let us think to sensor networks, computer networks, manufactures. Data streams are usually highly evolving, thus mining changes in data is a challenging task. In this paper we will deal with the structural drift detection problem where the aim is to discover and to describe changes in proximity relations among multiple data streams. We will introduce a new strategy whose effectiveness is shown through an application on simulated data.

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Author information

Authors and Affiliations

  1. Second University of Naples, Naples, Italy

    Antonio Balzanella & Rosanna Verde

Authors
  1. Antonio Balzanella
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  2. Rosanna Verde
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Corresponding author

Correspondence to Antonio Balzanella .

Editor information

Editors and Affiliations

  1. Department of Statistics, Università degli Studi di Firenze, Viale G.B. Morgagni 59, Firenze, 50134, Italy

    Antonio Giusti

  2. Fakultät für Informatik, und Mathematik, Universität Passau, Innstr. 33, Passau, 94030, Germany

    Gunter Ritter

  3. Sapienza", Department of Statistics, University of Rome "La, Piazzale Aldo Moro 5, Rome, 00185, Italy

    Maurizio Vichi

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Balzanella, A., Verde, R. (2013). Summarizing and Detecting Structural Drifts from Multiple Data Streams. In: Giusti, A., Ritter, G., Vichi, M. (eds) Classification and Data Mining. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28894-4_13

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