Aggregations in Eliona
Eliona offers dynamic real-time aggregations, which allow for efficient evaluation of large amounts of data. Instead of analyzing raw sensor data point by point, users can choose different aggregation methods to quickly identify relevant correlations.
What are Aggregations?
Aggregations summarize data over a specific period to make patterns and trends more easily visible. This helps to:
Identify long-term developments
Identify irregularities or outliers more quickly
Make reports & visualizations clearer
Enable efficient calculations for large amounts 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 summed and divided by their number. Application: Useful for displaying typical values of a sensor over a certain period, e.g.:
Average temperature per hour
Average energy consumption per day
Sum Counter
Calculation: The values within a time interval are summed up. Application: Useful when the total amount of a measured variable needs to be determined, e.g.:
Total water consumption per day
Total number of person 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 a month
Total number of items produced on a production line
Minimum (Min)
Calculation: The smallest value within the selected aggregation interval is output. Application: Helpful when you need to determine how low a measured variable 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 (TWA)
Calculation: Each measured value is weighted according to the time it was valid within the aggregation interval. Values that are present frequently or for longer periods influence the average more strongly than values that occur briefly.
Application: Particularly useful for measurement series in which values rarely change or outliers should not be overweighted, e.g.:
Temperature profiles with rare peak values
Energy consumption with individual load peaks
Status measurements where the duration of a value is crucial
Flexibility in Aggregation
In every view that supports aggregations, users can freely decide:
Time period of the aggregation (minutes, hours, days, weeks, months...)
Aggregation method (average, minimum, maximum, etc.)
Immediate updating of the calculated values without re-configuration
This flexibility allows data to be optimally adapted to the respective analysis needs—be it for dashboards, reports, automations, or rules.
Where can Aggregation be used?
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