Temporal Data Analytics on COVID-19 Data with Ubiquitous Computing

Chen, Yubo; Leung, Carson K.; Shang, Siyuan; Wen, Qiang

doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00146

Cited by 25 publications

(3 citation statements)

References 56 publications

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“…The study show that Prophet algorithm is faster than other models for COVID-19 prediction. Temporal data science algorithm in [8] is presented to analyze big COVID-19 data using ubiquitous computing. Gupta et al [9] detects COVID-19 cases including confirmed, deaths and cured cases in India.…”

Section: Related Workmentioning

confidence: 99%

Predictive analytics on COVID-19 data using Hive based on Hadoop cluster

Khaleel,

Kareem,

Mahdi

2023

IJEECS

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COVID-19 pandemic has received a serious attention from academia, industry and governments to stop the huge number of deaths and economic disruptions around the world. Many techniques have been used to control the spread of the pandemic by understanding its characteristics and behavior. However, because of the large amounts and complex characteristics of COVID-19 data, the querying and analysis of such data using conventional tools have become a challenging task. As a result, powerful and distributed tools are highly required for querying and analyzing this data effectively. In this paper, distributed system using Hive based on Hadoop cluster is used to query and analyze COVID-19 data to obtain meaningful information. Hadoop is employed as a scalable and reliable framework to accommodate such large amounts of data. Hive is used as a data warehouse that run on Hadoop cluster to perform querying and predictive analytics on huge COVID-19 datasets. Several experiments are performed to evaluate the performance of proposed system. Experiments show that the proposed system outperforms relational database management system (RDBMS) in terms of query processing time. Experiments also show that the proposed system has a better efficiency in terms of data load, I/O operation, reading and writing data.

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Section: Related Workmentioning

confidence: 99%

Predictive analytics on COVID-19 data using Hive based on Hadoop cluster

Khaleel,

Kareem,

Mahdi

2023

IJEECS

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“…No common methodological or conceptual framework for the implementation of BDA in organizations has been proposed up to this point (and even less so for TBDA). This is most likely due to the fact that researchers have rather been focusing on specific BDA tasks such as, for example, supporting innovations or competitive advantage (Häikiö and Koivumäki, 2016;Lusch and Nambisan, 2015;Serrat, 2017), big data analytics in healthcare (Chen, Leung, Shang, and Wen, 2020;Dinov, 2016;Lin et al, 2014), and business transformation (Kayser et al, 2018;Wang, Conboy, and Cawley, 2018). Both Lin et al (2014) and Chen et al (2020) addressed the topic of temporality within their respective methodologies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is most likely due to the fact that researchers have rather been focusing on specific BDA tasks such as, for example, supporting innovations or competitive advantage (Häikiö and Koivumäki, 2016;Lusch and Nambisan, 2015;Serrat, 2017), big data analytics in healthcare (Chen, Leung, Shang, and Wen, 2020;Dinov, 2016;Lin et al, 2014), and business transformation (Kayser et al, 2018;Wang, Conboy, and Cawley, 2018). Both Lin et al (2014) and Chen et al (2020) addressed the topic of temporality within their respective methodologies. The former group suggests a new data architecture that is based on NoSQL, while the other group proposes a temporal algorithm for processing COVID-19 epidemiological data; however, neither group provides a framework for the deployment of big data.…”

Section: Literature Reviewmentioning

confidence: 99%

Reception of the New Framework for Implementing Temporal Big Data Analytics in Organizations

Mach‐Król¹

2022

Informatyka Ekonomiczna

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The main goal of this study was to present the reception of the new framework for implementing temporal big data analytics (TBDA) in organizations. This research also aimed at verifying the correctness and usefulness of the proposed framework by means of a focus group interview. The need for TBDA is described, and the proposed framework briefly outlined. Finally, the results of the focus group interview are presented. The proposed conceptual framework was positively verified. The most important findings of this study are: proving that effective implementation of big data analytics in companies requires consideration of time; demonstrating the usefulness of the leagile approach in the implementation of TBDA in companies; positive verification of the comprehensive conceptual framework for TBDA implementation in organizations.

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