OLAP over Big COVID-19 Data: A Real-Life Case Study

Cuzzocrea, A.; Leung, C. K.; Soufargi, S.; Gallo, C.; Shang, S.; Chen, Y.

Cuzzocrea, A.; Leung, C. K.; Soufargi, S.; Gallo, C.; Shang, S.; Chen, Y..

20th IEEE International Conference on Dependable, Autonomic and Secure Computing, 20th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing, 2022 IEEE International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022 ; 2022.

Article in English | Scopus | ID: covidwho-2191707

ABSTRACT

ABSTRACT

This paper focuses the attention on a real-life case study represented by the design, the development and the practice of OLAP tools over big COVID-19 data in Canada. The OLAP tools developed in this context are further enriched by machine learning procedures that magnify the mining effect. The contribution presented in this paper also embeds an implicit methodology for OLAP over big COVID-19 data. Experimental analysis on the target case study is also provided. © 2022 IEEE.

Keywords

big COVID-19 data; big data analytics; intelligent big data systems; machine learning; OLAP; real-life case studies on big COVID-19 data; Advanced Analytics; Big data; Data Analytics; Big data analytic; Case-studies; Data systems; Intelligen

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Case report Language: English Journal: PiCom Year: 2022 Document Type: Article

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