Anomaly Detection in Telecommunications Using Complex Streaming Data
Mats Uddenfeldt
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Mats Uddenfeldt
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Ted Dunning is Chief Application Architect at MapR Technologies and very active in the Apache Software Foundation where he's currently serving as vice president of incubation.
In a recent whiteboard session Ted explained in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors.
Watch the full video by clicking here or below:
Additionally you can get the full video transcription here.
Additional resources
- Read “Why Cloud and Analytics are Essential to the Success of 5G and IoT” on the Ericsson Cloud Blog by Tugduall Grall
- Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman
- Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage”
- Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning
Technical Product Management UCC, Ericsson
8 年A development could be to dynamically adapt/orchestrate the networks, especially in use of network slices/verticals, to high RAN (Radio Access Network)-resource usage anomalies like in incidents to give Public Safety/First responders higher QoS, or safeguarding QoS, and secure accessibility to the RAN and critical core services. This could be a small but essential cornerstone for Ericsson in bringing LTE to a Mission Critical level.
Technical Product Management UCC, Ericsson
8 年Cool! Now we are seeing the benefits of the co-operation between Ericsson and MapR Technologies! Should be useful in automated Network Orchestration!
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8 年Repeating the same thing time and again with making much sense. Can be done in a 5 min vedio>
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8 年Koleen BP The R is from Reduce. Map Reduce.
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8 年MapR. Does this mean that the platform uses or made from R language? Am interested here.