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Data Variety Modeling: A Case of Contextual Diversity Identification from a Bottom-up Perspective

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Computer Science – CACIC 2021 (CACIC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1584))

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Abstract

Variety is a property related to data diversity in Big Data Systems (BDS) that comprises several cases, such as structural diversity (variety in data types), source diversity (variety in the way data are produced), etc. Recently, adding contextual information allows more complex analyses, which open the possibility of modeling variety thinking of reuse. In this article, we introduce a proposal for modeling variety by following a dual data processing perspective. We exemplify the case of one of these perspectives by identifying and modeling contextual variations in a particular domain problem.

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Notes

  1. 1.

    https://hadoop.apache.org/.

  2. 2.

    https://spark.apache.org/.

  3. 3.

    Common services are services that will be part of every product derived from the SPL.

  4. 4.

    https://www.json.org/json-es.html.

  5. 5.

    The \(<<\)require\(>>\) restriction implies that every new model is stored in the KB.

  6. 6.

    https://data.kingcounty.gov/Environment-Waste-Management/Water-Quality/vwmt-pvjw.

  7. 7.

    https://flume.apache.org/.

  8. 8.

    https://hbase.apache.org/.

  9. 9.

    https://cassandra.apache.org/.

  10. 10.

    To do so, we used the pivot operation of Pandas (https://pandas.pydata.org/docs/index.html).

  11. 11.

    In order to refine the correlation analysis, the transformed dataset was treated to mitigate influences of null values. Here, we tried several replacement alternatives (zero, mean value, etc.), to choose the better one.

  12. 12.

    We used Keras (https://keras.io/).

  13. 13.

    https://inta.gob.ar/altovalle.

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Authors and Affiliations

  1. GIISCO Research Group - Departamento de Ingeniería de Sistemas, Facultad de Informática, Universidad Nacional Del Comahue, Neuquen, Argentina

    Líam Osycka, Agustina Buccella & Alejandra Cechich

Authors
  1. Líam Osycka
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  2. Agustina Buccella
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  3. Alejandra Cechich
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Corresponding author

Correspondence to Líam Osycka .

Editor information

Editors and Affiliations

  1. National University of La Plata, La Plata, Argentina

    Patricia Pesado

  2. National University of Salta, Salta, Argentina

    Gustavo Gil

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Osycka, L., Buccella, A., Cechich, A. (2022). Data Variety Modeling: A Case of Contextual Diversity Identification from a Bottom-up Perspective. In: Pesado, P., Gil, G. (eds) Computer Science – CACIC 2021. CACIC 2021. Communications in Computer and Information Science, vol 1584. Springer, Cham. https://doi.org/10.1007/978-3-031-05903-2_9

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  • DOI: https://doi.org/10.1007/978-3-031-05903-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05902-5

  • Online ISBN: 978-3-031-05903-2

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