What are Aggregation Methods?

When retrieving detailed data from tag based time series data sources (i.e. Historians), Flow makes use of a number of aggregation methods to standardize how this data is summarized and stored in the Flow System. In a previous lab, you created a measure that calculated the average value of the Boiler Temperature for each hour. The aggregation method you used was “Average”, but there are a few others you should know about. Let’s discuss each of these aggregation methods in detail.

Note: Aggregation Methods are different from Aggregated Measures!

Let’s use a standard set of detailed data, and then apply each of the aggregation methods to the same data set. Here is our data set:

Point Timestamp Raw Value
1 05:55:03 1.6
2 06:03:23 2.3
3 06:07:30 2.5
4 06:10:22 3
5 06:15:52 2.1
6 06:20:40 1.9
7 06:25:13 2.5
8 06:29:44 2.3
9 06:35:14 1.6
10 06:41:14 1.8
11 06:46:03 2.3
12 06:52:35 2.9
13 06:55:26 2.2
14 07:05:40 1.4


Which, graphically, looks like this if we plot the points. The shaded area is the time period we are interested in for our summary information.



Flow uses a “Stair Step” interpolation between each point, like this:



Because the first point in our data set is outside of our summary time period, Flow uses it as a “boundary” value with a timestamp of exactly 06:00:00.

For each time period, you can apply one of the standard aggregation methods on these data points, which will return a single value for that time period.  This single value is calculated differently for each of the aggregation methods.


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