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Data Reduction for Big Data

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Big Data Preprocessing

Abstract

Data reduction in data mining selects/generates the most representative instances in the input data in order to reduce the original complex instance space and better define the decision boundaries between classes. Theoretically, reduction techniques should enable the application of learning algorithms on large-scale problems. Nevertheless, standard algorithms suffer from the increment on size and complexity of today’s problems. The objective of this chapter is to provide several ideas, algorithms, and techniques to deal with the data reduction problem on Big Data. We begin by analyzing the first ideas on scalable data reduction in single-machine environments. Then we present a distributed data reduction method that solves many of the scalability problems derived from the sequential approaches. Next we provide a case of use of data reduction algorithms in Big Data. Lastly, we study a recent development on data reduction for high-speed streaming systems.

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Authors and Affiliations

  1. Department of Computer Science and AI, University of Granada, Granada, Spain

    Julián Luengo, Diego García-Gil, Salvador García & Francisco Herrera

  2. DOCOMO Digital España, Madrid, Madrid, Spain

    Sergio Ramírez-Gallego

Authors
  1. Julián Luengo
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  2. Diego García-Gil
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  3. Sergio Ramírez-Gallego
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  4. Salvador García
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  5. Francisco Herrera
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Luengo, J., García-Gil, D., Ramírez-Gallego, S., García, S., Herrera, F. (2020). Data Reduction for Big Data. In: Big Data Preprocessing. Springer, Cham. https://doi.org/10.1007/978-3-030-39105-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-39105-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39104-1

  • Online ISBN: 978-3-030-39105-8

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