Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA
Arun Kejariwal

Arun Kejariwal
R&D Leader, Independent

@arun_kejariwal | Attendee Directory Profile

Until recently, Arun Kejariwal was a statistical learning principal at Machine Zone (MZ), where he led a team of top-tier researchers and worked on research and development of novel techniques for install and click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns. In addition, his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection. Previously, Arun worked at Twitter, where he developed and open sourced techniques for anomaly detection and breakout detection. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.

Sessions

11:50am12:30pm Thursday, March 16, 2017
Stream processing and analytics
Location: LL20 D Level: Intermediate
Secondary topics:  Media, Streaming
Arun Kejariwal (Independent), Karthik Ramasamy (Twitter)
Average rating: ***..
(3.00, 1 rating)
Anomaly detection plays a key role in the context of analysis of real-time streams. This is exemplified by, say, detection incidents in real life from tweet storms. Arun Kejariwal and Karthik Ramasamy walk you through how anomaly detection is supported in real-time data streams in Heron—the streaming system built in-house at Twitter (and open sourced) for real-time computation. Read more.