The COVID-19 pandemic has been regarded as the most significant global disaster since World
Wars I and II. It has caused social and economic disruptions all over the world, as well as
millions of deaths. Quarantines and lockdowns have been frequently occurring around the
globe. Thus, the COVID-19 pandemic has brought new unknown challenges to everyone.
The global science and medical communities have advanced with timely research and
analysis, contributing to our understanding and response to the virus. But much valuable
information is still hidden in vast quantities of relevant data, such as statistics, text,
speech, etc.
Social media outlets have become an increasingly crucial source of potentially rich
information. Such data may provide useful insights regarding the public’s reactions to
social dilemmas. Since COVID-19 was never seen before, massive amounts of misinformation could
have been rapidly distributed.
This workshop aims to support research to observe impacts of COVID-19, to explore challenges
and opportunities for improving safety and health to fight COVID-19, and to encourage
collaborations between researchers in Natural Language Processing (NLP), machine learning,
deep learning, information visualization, geospatial computing, cloud computing,
healthcare,
and finance communities to share their recent findings in this active and
late-breaking
research.
Topics
We invite submissions related to any aspect of Big Data analytics in theory, methodologies, and applications for COVID-19 topics. List of potential COVID-19 related topics includes, but are not limited to:
- COVID-19 contact tracing and hotspot detection
- COVID-19 tracking, data collection, and data management
- COVID-19 data mining, query processing, and information retrieval
- COVID-19 simulation and modeling
- COVID-19 prediction of positive cases, deaths, etc.
- COVID-19 spatial analysis, mapping, visual analytics
- COVID-19 socioeconomic impact
- COVID-19 and social media (e.g., sentiment analysis, misinformation detection, hate and toxic speech detection, etc.)
- Effects of social distance, mask wearing, or vaccines
- COVID-19 impact on education and distance learning
- COVID-19 disaster response and emergency management
Important Dates
- First Deadline:
- Oct. 4, 2021: Submission of full papers (7-10 pages) and short papers (5-6 pages)
- Nov 1, 2021: Notification of paper acceptance
- Nov 19, 2021: Accepted Papers & Program Schedule
- Second Deadline (General Workshop Deadline of IEEE Big Data 2021):
- Oct. 27, 2021: Submission of full papers (7-10 pages) and short papers (5-6 pages)
- Nov 10, 2021: Notification of paper acceptance
- Nov 19, 2021: Accepted Papers & Program Schedule
- IEEE Big Data 2021 Important Deadlines:
- Nov 21, 2021: Camera-ready of accepted papers
- Nov 24, 2021: Presentation video record uploading
- Dec. 15-18, 2021: IEEE Big Data Conference & Workshops (Virtually)
Submission
Please submit to IEEE Big Data 2021 paper submission site.
Program Committee
Program Chair and Co-chair
- Lindi Liao, George Mason University, USA
- Harry J. Foxwell, George Mason University, USA
Program Committee members
- Zhiqian Chen, Mississippi State University, USA
- Kaiqun Fu, South Dakota State University, USA
- Ge Jin, Purdue University, USA
- Jundong Li, University of Virginia, USA
- Michael D. Porter, University of Virginia, USA
- Ioulia Rytikova, George Mason University, USA
- Chen Shen, National Institute of Standards and Technology (NIST), USA
- David Wong, George Mason University, USA
- Chaowei Yang, George Mason University, USA
- Eddy Zhang, George Mason University, USA