The Adaptive Raw Sample Method allows a very close approximation of raw data to be displayed by consuming applications, such as the Explorer Trend, at a much faster rate than was previously possible using raw data.
When analyzing data in fine detail, we often want to get the most granular data possible onto the trend. Traditionally, using the Raw sample method we could end up with millions of points of data being returned, with no way of actually showing all of these points on a screen with a resolution of 1920 pixels. Transferring all of those data points back to the web browser would also take a long time.
Adaptive Raw is an algorithm implemented in P2 Server’s calculation engine that is designed to a trace on the Trend that is virtually identical in appearance, but which renders much faster. When using Adaptive Raw, P2 Server does the work of automatically selecting the sample interval to return the most detailed data possible.
The Adaptive Raw sample method works by sending all Raw data points from the historian to P2 Server, and then determining the number of points. If there are less than 2000 points then the raw data is returned, otherwise the adaptive raw algorithm is used to return 2000 points. Essentially, the Adaptive Raw sample method chooses the best data for you. The processing returns a trace that is a very accurate representation of a traditional Raw trend, but with far less data being actually transferred to the trend.
Using this method, we have been able to open up the time ranges available for trending of data using Adaptive Raw, giving an almost perfect copy of a raw Trend, but much faster.