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Sampling Technique for Complex Data

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Sampling Techniques for Supervised or Unsupervised Tasks

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

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Abstract

In the context of Big Data, complex data from heterogeneous and distributed sources is potentially unlimited in number. The analysis of these data is now at the center of the concerns of actors in most activity domains whose predictions and perspectives are decisive. In many key domains (security, air traffic control, road traffic, industry, medicine, telemonitoring, etc.), there is a need to process large complex data streams to provide near-immediate results. To fill this void, several works have applied on-the-fly processing on mass data. Sampling is widely used in large data situations to reduce the volume to be processed. However, with the emergence of complex data, traditional sampling techniques have shown some limitations. To overcome this problem, there is an increased need for tools and techniques from statistics, mathematics, machine learning and deep learning. In this chapter, we present a state-of-the-art, not exhaustive, of the sampling of complex data. We also present an overview on integrating data from heterogeneous sources. In fact, data integration is an implicit problem in the sampling data problem from heterogeneous sources. The underlying idea is to combine these data in order to reduce both the heterogeneity of these data and the uncertainty about the resulting data.

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Authors and Affiliations

  1. Labo. Image et Reconnaissance de Formes – Systèmes Intelligents et Communicants (IRF-SIC), Ibn Zohr University, Agadir, Morocco

    A. Idarrou & H. Douzi

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  1. A. Idarrou
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  2. H. Douzi
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Correspondence to A. Idarrou .

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Editors and Affiliations

  1. PRISME Laboratory, University of Orléans, Orléans, France

    Frédéric Ros

  2. UMR ITAP, Irstea, Montpellier, France

    Serge Guillaume

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Idarrou, A., Douzi, H. (2020). Sampling Technique for Complex Data. In: Ros, F., Guillaume, S. (eds) Sampling Techniques for Supervised or Unsupervised Tasks. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-29349-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-29349-9_6

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