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:
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.