Ok, thanks for the clarification.
On Tue, Apr 7, 2020, 7:00 PM Nienhuis, Ryan wrote:
> Vigo,
>
>
>
> I mean that the algorithm is a standalone piece of code. There are no
> examples that I am aware of for running it using Flink.
>
>
>
> Ryan
>
>
>
> *From:* Salvador Vigo
> *Sent:* Saturday, Ap
Vigo,
I mean that the algorithm is a standalone piece of code. There are no examples
that I am aware of for running it using Flink.
Ryan
From: Salvador Vigo
Sent: Saturday, April 4, 2020 12:26 AM
To: Marta Paes Moreira
Cc: Nienhuis, Ryan ; user
Subject: RE: [EXTERNAL] Anomaly detection Apach
Thanks for answer.
@Marta, First answer videos [1], [2]. It was interesting to see this two
different approaches, although I was looking for some more specific
implementation. Link number [3], I didn't know the existence of Kinesis, so
maybe could be good for benchmarking and comparing my results
Forgot to mention that you might also want to have a look into Flink CEP
[1], Flink's library for Complex Event Processing.
It allows you to define and detect event patterns over streams, which can
come in pretty handy for anomaly detection.
[1] https://ci.apache.org/projects/flink/flink-docs-sta
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.
h
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
