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Distributed Scalable Association Rule Mining over Covid-19 Data

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Future Data and Security Engineering (FDSE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13076))

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Abstract

The worldwide Covid-19 widespread in 2020 has turned into a phenomenon that has shaken human life significantly. It is widely recognized that taking faster measurements is crucial for monitoring and preventing the further spread of COVID-19. The advent of distributive computing frameworks provides one efficient solution for the issue. One method uses non-clinical techniques, such as data mining tools and other artificial intelligence technologies. Spark is a widely used framework and accepted by the big data community. This research used a cross-country Covid-19 dataset to assess the performance of the Apriori and FP-growth through different components of Spark (different numbers of cores and transactions). This involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. This research aims to understand the difference between FP-growth and Apriori and find the ideal parameters of Spark that can improve the performance by adding nodes.

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Notes

  1. 1.

    https://github.com/beoutbreakprepared/nCoV2019.

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Acknowledgements

This work has been conducted in the project “ICT programme” which was supported by the European Union through the European Social Fund.

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Authors and Affiliations

  1. Information Systems Group, Tallinn University of Technology, Akadeemia tee 15a, 12618, Tallinn, Estonia

    Mahtab Shahin & Dirk Draheim

  2. Software Science Department, Tallinn University of Technology, Akadeemia tee 15a, 12618, Tallinn, Estonia

    Wissem Inoubli & Sadok Ben Yahia

  3. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Syed Attique Shah

Authors
  1. Mahtab Shahin
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  2. Wissem Inoubli
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  3. Syed Attique Shah
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  4. Sadok Ben Yahia
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  5. Dirk Draheim
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Corresponding author

Correspondence to Mahtab Shahin .

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Editors and Affiliations

  1. HCMC University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

  4. Hosei University, Koganei-shi, Tokyo, Japan

    Makoto Takizawa

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Shahin, M., Inoubli, W., Shah, S.A., Yahia, S.B., Draheim, D. (2021). Distributed Scalable Association Rule Mining over Covid-19 Data. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2021. Lecture Notes in Computer Science(), vol 13076. Springer, Cham. https://doi.org/10.1007/978-3-030-91387-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-91387-8_3

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