Big Data Visualization and Visual Analytics of COVID-19 Data

Leung, Carson K.; Chen, Yubo; Hoi, Calvin S. H.; Shang, Siyuan; Wen, Yan; Cuzzocrea, Alfredo

doi:10.1109/iv51561.2020.00073

Cited by 57 publications

(14 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At this stage, improved visualisation of infection rates analysis is required to rapidly detect patients with the disease [27]. Big-data visualisations and visual analytics tools were introduced in COVID data analytics to assist users in obtaining a better understanding of information concerning confirmed cases [28]. Additionally, some research has been conducted from a visualisation point of view in order to analyse air pollution and COVID data.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impacts of Human Activities on Air Quality during the COVID-19 Pandemic through Case Analysis

Chi

Hua

et al. 2022

Atmosphere

View full text Add to dashboard Cite

COVID-19 is the latest of many pandemic affecting the world in the past few decades, and it has had a significant impact on the global environment. Some research has analysed the effects of the pandemic on air quality; however, very few studies have employed relationship analytics. In order to analyse the potential relationship between pandemic-related information and air quality data from a more holistic and detailed point of view, we propose a methodology based on pure data analysis. Three types of data were collected, namely air quality index, pandemic-related events, and number of COVID cases. Data were collected from five cities—Wuhan, New York, Seoul, Melbourne, and Singapore—to further analyse the response of air quality index to COVID events, thus revealing how human activity influences air quality from a pandemic perspective. The results show that a potential connection does exist in most cases and provide more evidence showing that air pollution declined during the pandemic. However, the strength of this relationship may also be related to other factors, such as geography, politics, population density, and measures imposed by local authorities, etc. This study provides another perspective to assist stakeholders in improving environmental decision making.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessing the Impacts of Human Activities on Air Quality during the COVID-19 Pandemic through Case Analysis

Chi

Hua

et al. 2022

Atmosphere

View full text Add to dashboard Cite

show abstract

“…As ongoing and future work, we transfer knowledge learned from the current work to machine learning and analytics of big data in many other real-life applications and services. Moreover, we explore the incorporation of our machine learning tool with a COVID-19 visualizer [52] such that the machine learning serves as a back-end engine for big data analytics and the visualizer serves as a front-end interface for information visualization and visual analytics of big COVID-19 epidemological data.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning and OLAP on Big COVID-19 Data

Leung

Chen

Hoi

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

Self Cite

View full text Add to dashboard Cite

In the current technological era, huge amounts of big data are generated and collected from a wide variety of rich data sources. These big data can be of different levels of veracity in the sense that some of them are precise while some others are imprecise and uncertain. Embedded in these big data are useful information and valuable knowledge to be discovered. An example of these big data is healthcare and epidemiological data such as data related to patients who suffered from epidemic diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data-via data science techniques such as machine learning, data mining, and online analytical processing (OLAP)-helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a machine learning and big data analytic tool for processing and analyzing COVID-19 epidemiological data. Specifically, the tool makes good use of taxonomy and OLAP to generalize some specific attributes into some generalized attributes for effective big data analytics. Instead of ignoring unknown or unstated values of some attributes, the tool provides users with flexibility of including or excluding these values, depending on their preference and applications. Moreover, the tool discovers frequent patterns and their related patterns, which help reveal some useful knowledge such as absolute and relative frequency of the patterns. Furthermore, the tool learns from the patterns discovered from historical data and predicts useful information such as clinical outcomes for future data. As such, the tool helps users to get a better understanding of information about the confirmed cases of COVID-19. Although this tool is designed for machine learning and analytics of big epidemiological data, it would be applicable to machine learning and analytics of big data in many other real-life applications and services.

show abstract

“…As datasets are increasing in complexity and size, it is crucial for these tools to scale efficiently without domain-specific technical micromanagement [7]. Furthermore, charts and graphs can be exploited to interpret complex data sets, with data visualization being the star of COVID19 pandemic [8]. All these works are offering different capabilities, with most of them being siloed and not concurrently interlinked in real word cases.…”

Section: Related Workmentioning

confidence: 99%

An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics

Kiourtis

Karamolegkos

Karabetian

et al. 2022

Studies in Health Technology and Informatics

View full text Add to dashboard Cite

Big Data has proved to be vast and complex, without being efficiently manageable through traditional architectures, whereas data analysis is considered crucial for both technical and non-technical stakeholders. Current analytics platforms are siloed for specific domains, whereas the requirements to enhance their use and lower their technicalities are continuously increasing. This paper describes a domain-agnostic single access autoscaling Big Data analytics platform, namely Diastema, as a collection of efficient and scalable components, offering user-friendly analytics through graph data modelling, supporting technical and non-technical stakeholders. Diastema’s applicability is evaluated in healthcare through a predicting classifier for a COVID19 dataset, considering real-world constraints.

show abstract

Big Data Visualization and Visual Analytics of COVID-19 Data

Cited by 57 publications

References 33 publications

Assessing the Impacts of Human Activities on Air Quality during the COVID-19 Pandemic through Case Analysis

Assessing the Impacts of Human Activities on Air Quality during the COVID-19 Pandemic through Case Analysis

Machine Learning and OLAP on Big COVID-19 Data

An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics

Contact Info

Product

Resources

About