Reconciling Data Analytics, Automation, Privacy, and Security (RDAAPS)

May 17th, 2021

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 May 17th, 2021 - May 19th, 2021
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Reconciling Data Analytics, Automation, Privacy, and Security (RDAAPS)

Event submitted on Friday, June 12th 2020, approved by Content Team

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May 17th, 2021 - May 19th, 2021
Canada » 
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* Due to COVID, it is most likely that this event will be online - but please make sure! The situation is constantly changing...

 Our Review

This Canadian Cybersecurity Conference, taking place in Canada, Ontario in 2021 has a quad-themed-focus on reconciling data analytics, automation (AI), privacy, and security. If any of these niches are within your work remit then we’d absolutely encourage you to attend this event. Its good to see events taking place in 2021 being posted in 2020 – and we hope that the COVID situation is well and truly behind us! We wish the organizers the best of luck with this event.

- Review written by Henry Dalziel on Friday, June 12th 2020.
- If you would like to edit or ammend facts in my review please either send us a message or connect with me via LinkedIn.

Conference Event Summary

The following description was either submitted by the Conference Organizer on Friday, June 12th 2020, or created by us.

The Reconciling Data Analytics, Automation, Privacy, and Security (RDAAPS) conference serves as a premier forum for advancing the state of the art in the intersection of the areas of Big Data Analytics for Decision Making, Accountable Data Analytics, Strings in Data Analytics, Security in Data Analysis, Domain knowledge modeling and generation, Automation for data analytics, security, and privacy in manufacturing, and Challenges of automation of data analytics processes.

RDAAPS brings together international experts from academia, industry, and government to present and discuss novel research in these areas.

The goal of RDAAPS is to promote academic and industrial collaborations, providing a platform for integrating the needs of the industry with the advancements in research and inspiring original research to solve enterprise data challenges.

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