Aggregations in Eliona
Eliona offers dynamic real-time aggregationsthat make it possible to efficiently evaluate large volumes of data. Instead of analyzing raw sensor data point by point, users can choose various aggregation methods to quickly identify relevant relationships.
What are aggregations?
Aggregations summarize data over a specific period of time to make patterns and trends easier to see. This helps to:
Long-term developments to identify
irregularities or outliers to identify more quickly
Reports & visualizations to make clearer
Efficient calculations for large volumes of data
Users can choose the aggregation interval and method at any time to display data in the way that makes the most sense for their analysis.
Available aggregation methods
Depending on the use case, various aggregation methods are available:
Average (Mean)
Calculation: All values within the aggregation period are added together and divided by their number. Application: Useful for displaying typical values of a sensor over a specific period, e.g.:
Average temperature per hour
Average energy consumption per day
Sum counter
Calculation: The values within a time interval are summed. Application: Useful when the total amount of a measured variable is to be determined, e.g.:
Total water consumption per day
Total number of people movements per hour
Cumulative counter
Calculation: Continuously adds values over time and returns the accumulated value. Application: Particularly suitable for continuously increasing values, such as:
Total energy consumption over one month
Units produced in a production line
Minimum (Min)
Calculation: The smallest value within the selected aggregation interval is output. Application: Helpful when it is necessary to determine how low a measured value was, e.g.:
Lowest temperature of the day
Minimum humidity during a week
Maximum (Max)
Calculation: The highest value within the aggregation period is output. Application: Useful for analyzing peak values, e.g.:
Highest power load of a building per hour
Maximum speed of a machine during an operating cycle
Time-weighted average (Time Weighted Average / TWA)
Calculation: Each measured value is weighted according to the time for which it was valid within the aggregation interval. Values that occur frequently or last longer have a stronger influence on the average than values that occur only briefly.
Application: Particularly useful in measurement series in which values change rarely or outliers should not be overemphasized, e.g.:
Temperature trends with rare peak values
Energy consumption with individual load peaks
State measurements where the duration of a value is decisive
Flexibility in aggregation
Users can freely decide in any view that supports aggregations:
Aggregation period (minutes, hours, days, weeks, months...)
Aggregation method (average, minimum, maximum, etc.)
Immediate update of the calculated values without reconfiguration
This flexibility makes it possible to optimally adapt data to the respective analysis needs - whether for dashboards, reports, automations, or rules.
Where can aggregation be used?
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