TY - GEN
T1 - CovidLens
T2 - 23rd IEEE International Conference on Mobile Data Management, MDM 2022
AU - Sharaf, Mohamed A.
AU - Zhang, Xiaozhong
AU - Chrysanthis, Panos K.
AU - Alsaedi, Wadima
AU - Alkalbani, Maitha
AU - Helal, Heba
AU - Aldhaheri, Alyazia
N1 - Funding Information:
Acknowledgment. This work is partially supported by UAE University grant G00003352 and US NSF grant SES-2017614.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Since the onset of the Covid-19 pandemic, an over-whelming amount of related data has been released. In an attempt to gain insights from that data, multiple public data visualization dashboards have been deployed. Differently from such dashboards, which mainly support basic data filtering and visualization of separate datasets, in this work, we propose CovidLens, which: 1) integrates various Covid-19 indicators and is centred around the Google Community Mobility Report dataset, 2) supports similarity search for finding similar and correlated patterns and trends across the integrated datasets, and 3) automatically recommends insightful visualizations that unlocks valuable insights into the pandemic effects. To that end, we will be presenting the employed dataset, together with the design, implementation, and multiple usage scenarios of our proposed CovidLens.
AB - Since the onset of the Covid-19 pandemic, an over-whelming amount of related data has been released. In an attempt to gain insights from that data, multiple public data visualization dashboards have been deployed. Differently from such dashboards, which mainly support basic data filtering and visualization of separate datasets, in this work, we propose CovidLens, which: 1) integrates various Covid-19 indicators and is centred around the Google Community Mobility Report dataset, 2) supports similarity search for finding similar and correlated patterns and trends across the integrated datasets, and 3) automatically recommends insightful visualizations that unlocks valuable insights into the pandemic effects. To that end, we will be presenting the employed dataset, together with the design, implementation, and multiple usage scenarios of our proposed CovidLens.
KW - data exploration
KW - data visualization
KW - similarity search
KW - visual dashboard
KW - visualization recommendation
UR - http://www.scopus.com/inward/record.url?scp=85137572932&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137572932&partnerID=8YFLogxK
U2 - 10.1109/MDM55031.2022.00066
DO - 10.1109/MDM55031.2022.00066
M3 - Conference contribution
AN - SCOPUS:85137572932
T3 - Proceedings - IEEE International Conference on Mobile Data Management
SP - 302
EP - 305
BT - Proceedings - 2022 23rd IEEE International Conference on Mobile Data Management, MDM 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 June 2022 through 9 June 2022
ER -