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From "Stavros Kontopoulos (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (FLINK-2147) Approximate calculation of frequencies in data streams
Date Tue, 04 Apr 2017 14:01:41 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2147?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15955164#comment-15955164
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Stavros Kontopoulos edited comment on FLINK-2147 at 4/4/17 2:01 PM:
--------------------------------------------------------------------

You just pick one of the sketches merge it with another one kill the task (3 down to 2 case).
For 1 to N. Just split the stream and create N count-min sketches. Wouldn't that work?


was (Author: skonto):
You just pick one of the sketches merge it with another one kill the task (3 down to 2 case).
For 1 to N. Just split the stream and create N count-min sketched. Wouldn't that work?

> Approximate calculation of frequencies in data streams
> ------------------------------------------------------
>
>                 Key: FLINK-2147
>                 URL: https://issues.apache.org/jira/browse/FLINK-2147
>             Project: Flink
>          Issue Type: New Feature
>          Components: DataStream API
>            Reporter: Gabor Gevay
>              Labels: approximate, statistics
>
> Count-Min sketch is a hashing-based algorithm for approximately keeping track of the
frequencies of elements in a data stream. It is described by Cormode et al. in the following
paper:
> http://dimacs.rutgers.edu/~graham/pubs/papers/cmsoft.pdf
> Note that this algorithm can be conveniently implemented in a distributed way, as described
in section 3.2 of the paper.
> The paper
> http://www.vldb.org/conf/2002/S10P03.pdf
> also describes algorithms for approximately keeping track of frequencies, but here the
user can specify a threshold below which she is not interested in the frequency of an element.
The error-bounds are also different than the Count-min sketch algorithm.



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