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Thinking Outside the Box: A New Approach to Clustering Data in Large Scale Cloud Environments
Head of Research – Cyera
Thinking Outside the Box: A New Approach to Clustering Data in Large Scale Cloud Environments
Head of Research – Cyera
Shiran is the head of research at Cyera, a data security posture management platform that helps companies quickly and automatically discover and assess sensitive data across various cloud environments. Prior to this role, Shiran served as a security research manager at Imperva and held various leadership positions in the cyber divisions of Unit 8200. She holds a BSc in Economics and Middle Eastern Studies, which she obtained as part of the Havazalot excellence program, and an MSc in Information Systems with a focus on Data Science from Tel Aviv University.
Shiran is the head of research at Cyera, a data security posture management platform that helps companies quickly and automatically discover and assess sensitive data across various cloud environments. Prior to this role, Shiran served as a security research manager at Imperva and held various leadership positions in the cyber divisions of Unit 8200. She holds a BSc in Economics and Middle Eastern Studies, which she obtained as part of the Havazalot excellence program, and an MSc in Information Systems with a focus on Data Science from Tel Aviv University.
Accurate and efficient data classification is essential in large scale computing environments, but can be difficult to achieve due to the abundance of data. Traditional methods can be inefficient in terms of time complexity and impractical in real-time scenarios. However, a unique approach to clustering was discovered by looking beyond the data and analyzing the behavior of metadata features.
The method involves embedding the path of data objects in a unique way and reduces the amount of data that needs to be analyzed, enabling accurate classification with minimal sampling. This approach has the potential to significantly improve data classification in fields such as data management, information technology, and cybersecurity. The use of metadata and embedding offers a creative solution to the data classification problem in large scale computing environments. Come and discover how this innovative method can benefit your work.
Accurate and efficient data classification is essential in large scale computing environments, but can be difficult to achieve due to the abundance of data. Traditional methods can be inefficient in terms of time complexity and impractical in real-time scenarios. However, a unique approach to clustering was discovered by looking beyond the data and analyzing the behavior of metadata features.
The method involves embedding the path of data objects in a unique way and reduces the amount of data that needs to be analyzed, enabling accurate classification with minimal sampling. This approach has the potential to significantly improve data classification in fields such as data management, information technology, and cybersecurity. The use of metadata and embedding offers a creative solution to the data classification problem in large scale computing environments. Come and discover how this innovative method can benefit your work.
| 8:45 | Reception |
|---|---|
| 9:30 | Opening words by WiDS TLV ambassador Nitzan Gado and by Lily Ben Ami, CEO of the Michal Sela Forum |
| 9:50 | Prof. Bracha Shapira – Data Challenges in Recommender Systems Research: Insights from Bundle Recommendation |
| 10:20 | Juan Liu – Accounting Automation: Making Accounting Easier So That People Can Forget About It |
| 10:50 | Break |
| 11:00 | Lightning talks |
| 12:20 | Lunch & poster session |
|---|---|
| 13:20 | Roundtable session & poster session |
| 14:05 | Roundtable closure |
| 14:20 | Break |
| 14:30 | Merav Mofaz – “Every Breath You Take and Every Move You Make…I'll Be Watching You:” The Sensitive Side of Smartwatches |
| 14:50 | Reut Yaniv – Ad Serving in the Online Geo Space Along Routes |
| 15:10 | Rachel Wities - It’s Not Just the Doctor’s Handwriting: Challenges and Opportunities in Healthcare NLP |
| 15:30 | Closing remarks |
| 15:40 | End |
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