Spark Correlation Coefficient
<p>I have an specific application in which I am trying to verify the strong positive relationship between many of the time series data that I am reading. I Should elaborate more:</p>

<ul>
<li><p>I have a lot of actors which are distributed, and they generate some
large number of time series streams of data each. The number of
actors *  time-series-streams is quite large so using them in order
for my specific regression analysis is very costly. So I chose
sampling and I am getting robust estimations.</p></li>
<li><p>The problem is; I need to validate this idea, and in order to<br>
validate it, I want to do "Correlation Coefficient" between a random 
sample of these time series and create the Gaussian Distribution for 
the outcomes of it, and assign the mean and stdev of correlations to 
the actors. To show which actors are producing more related time<br>
series in the application domain.</p></li>
</ul>

<p>Questions: </p>

<ol>
<li>Am I choosing the right way to verify that the correlation exists,
so we should expect sampling to reduce the amount of readings of the
actual data?</li>
<li>or are there any other ways of doing collective correlation
analysis?</li>
</ol>