Trends

Data Reduction Type

Selects the algorithm used to reduce and simplify large datasets for better performance and readability in the trend view.

These reduction types define how the raw data is processed when the number of data points exceeds the Max Points limit.

Type	Description
Quick Reduce	Fast sampling method; selects points at fixed intervals without deep analysis. Very fast, but may miss important values.
Reduce	More accurate algorithm that avoids skipping critical values like NULL. Preserves overall trends better than Quick Reduce.
Local Extremes	Preserves local minimum and maximum values within each sampling window. Ideal for keeping peaks and drops visible.
Average	Applies a simple moving average over fixed intervals. Smoothes out small fluctuations but may hide sudden changes.
Median	Splits data into intervals and returns the median value per interval. More robust than average when handling outliers.

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Use Case Tips:

• Use Quick Reduce for fast rendering during zooming or scrolling.
• Use Local Extremes when spikes, alarms, or anomalies are important.
• Use Average or Median for smoother trend lines in slow-moving processes.
• Use Reduce if data integrity (e.g. avoiding NULL skips) is a priority.

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