Visualization and Visual Knowledge Discovery from Big Uncertain Data

In the current uncertain world, data are kept growing bigger. Big data refer to the data flow of huge volume, high velocity, wide variety, and different levels of veracity (e.g., precise data, imprecise/uncertain data). Embedded in these big data are implicit, previously unknown, but valuable information and knowledge. With huge volumes of information and knowledge that can be discovered by techniques like data mining, a challenge is to validate and visualize the data mining results. To validate data for better data aggregation in estimation and prediction and for establishing trustworthy artificial intelligence, the synergy of visualization models and data mining strategies are needed. Hence, in this paper, we present a solution for visualization and visual knowledge discovery from big uncertain data. Our solution aims to discover knowledge in the form of frequently co-occurring patterns from big uncertain data and visualize the discovered knowledge. In particular, the solution shows the upper and lower bounds on frequency of these patterns. Evaluation with real-life Coronavirus disease 2019 (COVID-19) data demonstrates the effectiveness and practicality of our solution in visualization and visual knowledge discovery from big health informatics data collected from the current uncertain world. © 2022 IEEE.

Analyzing COVID-19 Epidemiological Data

Technological advancements have made it easy and quick to generate and collect huge volumes of varieties of data from wide ranges of rich data sources. These big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by big data science and analysis for social good. In this paper, we propose a solution to analyze coronavirus disease 2019 (COVID-19) epidemiological data. In particular, the solution focuses on analyzing valuable information and useful knowledge (e.g., distribution, frequency, patterns) of health-related states and characteristics in populations. Discovered information and knowledge helps users (e.g., researcher, civilian) to understand the disease better, and thus take an active role in fighting, controlling, and/or combating the disease. Evaluation of our solution on real-life data demonstrates its practicality in analyzing COVID-19 epidemiological data and revealing demographic relationships among COVID-19 cases. © 2021 IEEE.

Spatial Data Science of COVID-19 Data

Huge amounts of big data can be generated and collected from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge. An example is healthcare and epidemiological data such as data related to patients who suffered from viral diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data via data science 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 spatial data science system for analyzing big COVID-19 epidemiological data, with focus on the spatial data analytics among different geographic locations. The system helps users to get a better understanding of information about the confirmed cases of COVID-19. Evaluation results show the benefits of our system in spatial data analytics of big COVID-19 data. © 2020 IEEE.