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From Kostas Tzoumas <ktzou...@apache.org>
Subject Fwd: External Talk: Apache Flink - Speakers: Kostas Tzoumas (CEO dataArtisans), Stephan Ewen (CTO dataArtisans)
Date Tue, 07 Apr 2015 10:42:26 GMT
Hi everyone,

I'm forwarding a private conversation to the list with Mats' approval.

The problem is how to compute correlation between time series in Flink. We
have two time series, U and V, and need to compute 1000 correlation
measures between the series, each measure shifts one series by one more
item: corr(U[0:N], V[n:N+n]) for n=0 to n=1000.

Any ideas on how one can do that without a Cartesian product?

Best,
Kostas

---------- Forwarded message ----------
From: Mats Zachrison <mats.zachrison@ericsson.com>
Date: Tue, Mar 31, 2015 at 9:21 AM
Subject:
To: Kostas Tzoumas <kostas@data-artisans.com>, Stefan Avesand <
stefan.avesand@ericsson.com>
Cc: "stephan@data-artisans.com" <stephan@data-artisans.com>


As Stefan said, what I’m trying to achieve is basically a nice way to do a
correlation between two large time series. Since I’m looking for an optimal
delay between the two series, I’d like to delay one of the series x
observations when doing the correlation, and step x from 1 to 1000.



Some pseudo code:



  For (x = 1 to 1000)

      Shift Series A ‘x-1’ steps

      Correlation[x] = Correlate(Series A and Series B)

  End For



In R, using cor() and apply(), this could look like:



  shift <- as.array(c(1:1000))

  corrAB <- apply(shift, 1, function(x) cor(data[x:nrow(data), ]$ColumnA,
data[1:(nrow(data) - (x - 1)), ]$ColumnB))





Since this basically is 1000 independent correlation calculations, it is
fairly easy to parallelize. Here is an R example using foreach() and
package doParallel:



  cl <- makeCluster(3)

  registerDoParallel(cl)

  corrAB <- foreach(step = c(1:1000)) %dopar% {

        corrAB <- cor(data[step:nrow(data), ]$ColumnA, data[1:(nrow(data) -
(step - 1)), ]$ColumnB)

  }

  stopCluster(cl)



So I guess the question is – how to do this in a Flink environment? Do we
have to define how to parallelize the algorithm, or can the cluster take
care of that for us?



And of course this is most interesting on a generic level – given the
environment of a multi-core or –processor setup running Flink, how hard is
it to take advantage of all the clock cycles? Do we have to split the
algorithm, and data, and distribute the processing, or can the system do
much of that for us?

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