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Effectively and Efficiently Supporting Visual Big Data Analytics over Big Sequential Data: An Innovative Data Science Approach

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Computational Science and Its Applications – ICCSA 2022 (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13376))

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Abstract

In the current era of big data, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the willingness of many government, researchers, and organizations are led by the initiates of open data to share their data and make them publicly accessible. Healthcare, disease, and epidemiological data, such as privacy-preserving statistics on patients who suffered from epidemic diseases such as Coronavirus disease 2019 (COVID-19), are examples of open big data. Analyzing these open big data can be for social good. For instance, people get a better understanding of the disease by analyzing and mining the disease statistics, which may inspire them to take part in preventing, detecting, controlling and combating the disease. Having a pictorial representation further enhances the understanding of the data and corresponding results for analysis and mining because a picture is worth a thousand words. Hence, in this paper, we present a visual data science solution for the visualization and visual analytics of big sequential data. The visualization and visual analytics of sequences of real-life COVID-19 epidemiological data illustrate the ideas. Through our solution, we enable users to visualize the COVID-19 epidemiological data over time. It also allows people to visually analyze the data and discover relationships among popular features associated with the COVID-19 cases. The effectiveness of our visual data science solution in enhancing user experience in the visualization and visual analytics of big sequential data are demonstrated by evaluation of these real-life sequential COVID-19 epidemiological data.

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Author information

Authors and Affiliations

  1. iDEA Lab, University of Calabria, Rende, Italy

    Alfredo Cuzzocrea & Majid Abbasi Sisara

  2. LORIA, University of Lorraine, Nancy, France

    Alfredo Cuzzocrea

  3. Department of Engineering, University of Trieste, Trieste, Italy

    Majid Abbasi Sisara

  4. Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada

    Carson K. Leung & Yan Wen

  5. Department of Computer Science, University of Northern British Columbia, Prince George, BC, Canada

    Fan Jiang

Authors
  1. Alfredo Cuzzocrea
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  2. Majid Abbasi Sisara
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  3. Carson K. Leung
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  4. Yan Wen
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  5. Fan Jiang
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Corresponding author

Correspondence to Alfredo Cuzzocrea .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Universidad de Málaga, Malaga, Spain

    Eligius M. T. Hendrix

  4. Monash University, Clayton, VIC, Australia

    David Taniar

  5. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

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Cuzzocrea, A., Sisara, M.A., Leung, C.K., Wen, Y., Jiang, F. (2022). Effectively and Efficiently Supporting Visual Big Data Analytics over Big Sequential Data: An Innovative Data Science Approach. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13376. Springer, Cham. https://doi.org/10.1007/978-3-031-10450-3_9

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  • DOI: https://doi.org/10.1007/978-3-031-10450-3_9

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