HiveMQ Pulse Calculations

HiveMQ Pulse calculations are mathematical operations performed across nodes to produce new values such as sum (SUM) or insights such as overall equipment effectiveness (OEE).

In HiveMQ Pulse, calculations occur as close to the data source as possible.
This distributed approach enables instant feedback for real-time decision-making, minimizes cloud dependency, and optimizes resource use.

HiveMQ Pulse Calculation Types

The HiveMQ Pulse distributed calculation engine offers different categories of mathematical operations and analytical functions to process data and generate insights where they are needed most.

Calculation functions can be configured for data nodes that hold operational data (Field, and Tag nodes) to process real-time data and generate actionable insights for industrial operations.

Table 1. Simple Functions in HiveMQ Pulse
Name	Definition	Expression Language	Examples
Absolute	Returns the absolute value of a number. Negative values are converted to their positive equivalent.	`abs(value)`	`abs(-15.7)` returns `15.7`
Division	Divides the first input value by the second input value. Returns null or error if the divisor is zero.	`/`	`100/4` returns `25`
Minus	Subtracts the second input value from the first input value.	`-`	`85-20` returns `65`
Min	Returns the smallest value from a set of numeric inputs or data points over a specified timeframe.	`Min`	`Min(12.5, 8.3, 15.7, 9.1)` returns `8.3`, `(Min(temperature_sensor, "5m")` returns the minimum value over 5 minutes
Max	Returns the largest value from a set of numeric inputs or data points over a specified timeframe.	`Max`	`Max(12.5, 8.3, 15.7, 9.1)` returns `15.7`, `Max(pressure_sensor, "1h")` returns the maximum value over 1 hour
Product	Multiplies all input values together to produce a single result.	`Product(value1 * value2 * valueN )`	`Product(2, 3, 1.5)` returns `9`
Sum	Adds all input values together to produce a cumulative total.	`Sum(value1, value2, …, valueN) or Sum(tag_name, time_window)`	`Sum(10, 15, 5, 20)` returns `50` or `Sum(production_count, "1d")` returns the daily total
SMA	Simple Moving Average. Calculates the average of historical tag values over a time period or set of data points.	`SMA(tag_name, window_size) or SMA(tag_name, sample_count`	`SMA(temperature, 10)` returns a 10-sample moving average
Rolling Sum	Calculates the cumulative sum of values within a sliding timeframe and continuously updates as new data points arrive.	`Rolling_Sum(tag_name, window_size)`	`Rolling_Sum(parts_produced, "1h")` returns the hourly production total (sliding window)

Table 2. Statistical and Aggregation Functions in HiveMQ Pulse
Name	Definition	Expression Language	Examples
Count	Returns the number of valid (non-null) data points within a specified timeframe or sample set.	`COUNT`	`COUNT(production_cycles, "1d")` returns a daily cycle count
First	Returns the earliest (oldest) value from a time-series data stream within a specified timeframe.	`FIRST(tag_name, time_window)`	`FIRST(batch_weight, "1d")` returns the initial batch weight of the day
Last	Returns the most recent (latest) value from a time-series data stream or dataset.	`LAST(tag_name) or LAST(tag_name, time_window)`	`LAST(temperature_sensor)` returns the current temperature reading
MAX_WINDOW	Sets the upper limit of the time interval permitted for data aggregation in statistical computations and time-series analysis functions.	`MAX_WINDOW = "24h" or system-configured value`	`MEAN(temperature, MAX_WINDOW)` limits aggregation to the configured duration
MIN_WINDOW	Sets the minimum time interval required for meaningful statistical calculations and data aggregation operations.	`MIN_WINDOW = "30s" or system-configured value`	`STDEV(pressure, MIN_WINDOW)` requires at least the minimum duration for calculation
Mean	Calculates the arithmetic average of all values within a specified timeframe or sample set (the sum of all values / the count of values).	`MEAN(tag_name, time_window) o r MEAN(tag_name, sample_count)`	`MEAN(temperature_sensor, "1h")` returns the average temperature over 1 hour
Median	Returns the middle value from a sorted dataset.	`MEDIAN(tag_name, time_window) o r MEDIAN(tag_name, sample_count)`	`MEDIAN(cycle_time, 20)` returns a median of the last 20 cycle times
STDDEV	Standard Deviation. Calculates the square root of the average squared differences between each data point and the arithmetic mean	`STDEV(tag_name, time_window) or STDDEV(tag_name, sample_count)`	`STDDEV(temperature_reading, 100)` returns the temperature stability of the last 100 readings
Variance	Calculates the average of the squared differences between each data point and the arithmetic mean of the dataset	`VARIANCE(tag_name, sample_count)`	VARIANCE(pressure_sensor, "2h") returns a 2-hour pressure variability measurement
EMA	Exponential Moving Average. Calculates a weighted moving average with more weight on recent values.	`EMA(tag_name, alpha) or EMA(tag_name, window_size, alpha)`	`EMA(temperature, 0.3)` returns an exponential average with 30% weighting on new values
OEE	Overall Equipment Effectiveness_. Combines availability, performance and quality factors to calculate a single productivity metric.	`OEE(availability, performance, quality)`	`OEE(480, actual_runtime, 100, actual_rate, good_count, total_count)` returns a detailed OEE from components