Trends

Quick Reduce

Quick Reduce is a fast and lightweight data reduction method. It samples data at fixed intervals, reducing the total number of displayed points without performing deeper statistical calculations. This makes it ideal for large datasets where performance is more critical than precise accuracy.

How it works:

  • The system selects every n-th point based on the available range and the Max Points setting.
  • No smoothing or filtering is applied.
  • Sudden peaks or important outliers may be skipped if they fall between sampling intervals.

Practical Use Case – Industrial Context

Scenario: Live Monitoring of Conveyor Belt Speed

In a packaging factory, multiple conveyor belts are moving at high frequency. You want to display real-time belt speed trends on the HMI for all 10 belts.

Challenge:

  • The data is updated every second.
  • Over time, this results in thousands of data points per trend line.
  • Full data display causes lag on entry-level touch panels.

Solution:
Use Quick Reduce for each trend:

  • Shows a smooth scrolling visual of the speed trends.
  • Ensures consistent frontend performance.
  • Operators can still see general flow, slowdowns, or stops.
Max Points
### Quick Reduce **Quick Reduce** is a fast and lightweight data reduction method. It samples data at **fixed intervals**, reducing the total number of displayed points without performing deeper statistical calculations. This makes it ideal for large datasets where performance is more critical than precise accuracy. #### How it works: - The system selects every _n-th_ point based on the available range and the `Max Points` setting. - No smoothing or filtering is applied. - Sudden peaks or important outliers may be skipped if they fall between sampling intervals. --- ###Practical Use Case – Industrial Context #### Scenario: **Live Monitoring of Conveyor Belt Speed** In a packaging factory, multiple conveyor belts are moving at high frequency. You want to display **real-time belt speed trends** on the HMI for all 10 belts. **Challenge:** - The data is updated every second. - Over time, this results in thousands of data points per trend line. - Full data display causes lag on entry-level touch panels. **Solution:** Use **Quick Reduce** for each trend: - Shows a smooth scrolling visual of the speed trends. - Ensures consistent frontend performance. - Operators can still see general flow, slowdowns, or stops.