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Context-Aware Data Mining: Embedding External Data Sources in a Machine Learning Process

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Hybrid Artificial Intelligent Systems (HAIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10334))

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Abstract

The article presents a data mining system capable of predicting the soil moisture using local data, provided by weather stations in real time, as well as context-related, publicly available data from web portals. We have proven that the quality and quantity of context data is very important for improving the accuracy of the predictions, comparing with classical scenario, in which only the local data is used.

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Acknowledgment

This paper was performed under the frame of the Partnership in priority domains - PNII, developed with the support of MEN-UEFISCDI, project no. PN-II-PT-PCCA-2013-4-0015: Expert System for Risk Monitoring in Agriculture and Adaptation of Conservative Agricultural Technologies to Climate Change.

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Authors and Affiliations

  1. Holisun srl, Baia Mare, Romania

    Oliviu Matei & Andrei Bozga

  2. University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania

    Teodor Rusu

  3. Technical University of Cluj-Napoca, North University Centre of Baia Mare, Cluj-Napoca, Romania

    Oliviu Matei, Petrica Pop-Sitar & Carmen Anton

Authors
  1. Oliviu Matei
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  2. Teodor Rusu
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  3. Andrei Bozga
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  4. Petrica Pop-Sitar
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  5. Carmen Anton
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Corresponding author

Correspondence to Oliviu Matei .

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Editors and Affiliations

  1. University of La Rioja , Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  2. University of La Rioja , Logroño, La Rioja, Spain

    Rubén Urraca

  3. University of A Coruña , Ferrol, La Coruña, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Matei, O., Rusu, T., Bozga, A., Pop-Sitar, P., Anton, C. (2017). Context-Aware Data Mining: Embedding External Data Sources in a Machine Learning Process. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_35

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  • DOI: https://doi.org/10.1007/978-3-319-59650-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59649-5

  • Online ISBN: 978-3-319-59650-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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