Multi-Source Pandemic Data Visualization and Synchronization for Information Extraction

被引:0
|
作者
Zhang, Qi [1 ]
Brokaw, James [1 ]
机构
[1] Illinois State Univ, Sch IT, Normal, IL USA
关键词
Synchronization; Data-Driven Documents (D3); dynamic visualization; data analysis; information extraction; feature enhancement;
D O I
10.1109/CCWC57344.2023.10099327
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Visualization is integral to investigating information hidden in data and providing users with intuitive feedback for decision-making. No matter the field a data set describes, inspecting the data visually will yield fruitful insights into the trends and statistics. Over the past calendar year, COVID-19 vaccines have become increasingly available for much of the population. However, the CDC (Centers for Disease Control and Prevention) fails to consider multiple sets of pandemic data in a side-by-side view and synchronize multiple key factors in one web page, limiting medical professionals and individuals to seeing, comparing, and interacting with complete data visualization. To analyze the coronavirus and vaccination data collected from multiple sources, effectively displaying them is critically important for interpreting the pandemic transmission pattern and vaccine efficiency. This paper presents new algorithms for innovative data visualizations that provide users with intuitive feedback and enable them to see a complete story of where the data is concerned. The information derived from our developed web-based data visualization will aid healthcare professionals and everyday citizens in moving forward as the pandemic progresses.
引用
收藏
页码:140 / 146
页数:7
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