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From "Nienhuis, Ryan" <nienh...@amazon.com>
Subject RE: Anomaly detection Apache Flink
Date Fri, 03 Apr 2020 16:08:10 GMT
I would also have a look at the random cut forest algorithm. This is the base algorithm that
is used for anomaly detection in several AWS services (Quicksight, Kinesis Data Analytics,
etc.). It doesn’t help with getting it working with Flink, but may be a good place to start
for an algorithm.



From: Marta Paes Moreira <marta@ververica.com>
Sent: Friday, April 3, 2020 5:25 AM
To: Salvador Vigo <salvadoruve@gmail.com>
Cc: user <user@flink.apache.org>
Subject: RE: [EXTERNAL] Anomaly detection Apache Flink

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Hi, Salvador.

You can find some more examples of real-time anomaly detection with Flink in these presentations
from Microsoft [1] and Salesforce [2] at Flink Forward. This blogpost [3] also describes how
to build that kind of application using Kinesis Data Analytics (based on Flink).

Let me know if these resources help!

[1] https://www.youtube.com/watch?v=NhOZ9Q9_wwI
[2] https://www.youtube.com/watch?v=D4kk1JM8Kcg
[3] https://towardsdatascience.com/real-time-anomaly-detection-with-aws-c237db9eaa3f

On Fri, Apr 3, 2020 at 11:37 AM Salvador Vigo <salvadoruve@gmail.com<mailto:salvadoruve@gmail.com>>
Hi there,
I am working in an approach to make some experiments related with anomaly detection in real
time with Apache Flink. I would like to know if there are already some open issues in the
The only example I found was the one of Scott Kidder<https://mux.com/team/scott-kidder>
and the Mux platform, 2017. If any one is already working in this topic or know some related
work or publication I will be grateful.
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