Now let's compare the compressed data we just looked
at to sample data. Sample data is
coming from the same tags. It's just we're slicing through
the data at evenly spaced samples, determined by
a "Time" interval. So, let me show you how would make
that type of retrieval. You would typically use the same,
you know, the same tag name, start time and end time
but, now, in addition, you're going to provide
an interval of "Time". In this case, for example, 10 minutes.
Here's "Sample Data" on the ribbon. I'll go ahead and click,
and make sure I'm, I'm selected where I want my output to go first
I'll click on this. Let me go ahead
and put this over here. For the tag name we'll choose
that. Start time and end time, nothing unusual here.
This time a new field, "Time Interval." This indicates
how we're kinda going to slice through the time doing interpolations
on the way. As I said before, we'll discuss "Filtering" later.
I'll go ahead and show the timestamps, and here's the end result.
As you can see, evenly-spaced 10-minute intervals.
Now since I had reversed the order of my start time and end time,
just like in the previous example this is going backward in time.
But, of course, I can fix that. Let me switch this around to
Yesterday + 7 hours to Today +
7 hours. And now we're going forward in time
again. Let me try to describe some
times when this would be useful. For example,
if I'm looking at the compressed data
instead of this sample data for a given tag,
like let's go with BAlevel.1.
Yeah.
In that 24-hour period let's go with Yesterday
to Today. In that 24-hour period
I've only got
a few "Values" that have come in. And if I'd like to
compare that to these 'Values" here, not much of a comparison
I can make because we only have 3 "Values,"
3 compressed "Values." But if I were
to, on the other hand, let's just take that.
I'll copy that over here, add
this 10-minute interval, now I can
make that comparison. Let's use Sample Data,
there's our tag name, start time, end time,
and the interval of 10 minutes,
and I'll show the timestamps. And now we have
"Values" that are interpolated for, you know, each
and every "Value." For the one tag we have
a "Value" at the same time for the other tags.
In addition, I could even
offset this. So, for example, if I would anticipate
one of these was a Leading Indicator and the other were a Lagging Indicator --
let's just switch this to Yesterday + 6 hours
to Today + 6 hours -- now, I've got a set of data
that's exactly one hour apart,
and it could make for a good cross correlation. So, there's a number of times
where it's kind of useful to have a way to slice
through the data in a way that, well, that
is producing results that are going to be the
same for different variables that are collected at different frequencies,
but still maintain an accuracy. You know, given our
data compression algorithm these will still be accurate
compared to what data was coming in at the time.