## Documentation

## Metrics

Metrics are the elements of a subject you want to monitor. All metric types - ingested, calculated and simulated - have several visualization and visibility options:

Attribute | Description |
---|---|

Unit | Provide the unit of measurement. E.g. hPa, Celcius or meters. |

Decimal precision | Determine how many figures are shown after the comma |

Value lower bound | Determines the default lower bound in graphs. Leaving empty will result in a lower bound based on the data. |

Value upper bound | Determines the default upper bound in graphs. Leaving empty will result in a upper bound based on the data. |

Value type | Choose from ‘Continuous’ or ‘Discrete’. Continuous values can take on any value, such as temperature values. Discrete values can only take on certain values, for instance (binary) integer values such as 0 for closed and 1 for open. This is used by the web app to optimize visualization, like showing a step line graph for raw measurement values. |

Stale time (minutes) | Expected interval in between consecutive measurements. The graph will show gaps when data is not received within the stale time. |

Preferred color | Choose whether you would like to configure a preferred color when displaying this metric on a chart. Preferred colors will be displayed when it does not conflict with other metric colors being displayed on a given chart. |

Hide | Choose whether you want to see the metric on the subject dashboard |

### Ingested metric

An ingested metric receives data from a device, sensor or other external system. Example: a sensor that is sending values for both temperature and humidity.

Attribute | Description |
---|---|

Name | Give the ingested metric a descriptive name so you can easily recognize and find it. |

Data type | You can choose if the ingested data is a number, location or text value. You can check our docs about sending measurements to get to know how you integrate with our platform over HTTP, MQTT or CoAP |

External ID | Provide the ID the metric has in your own systems. In this way you can easily map metric to the ID naming conventions they relate to in your own systems. This external ID is also used to construct the default ingestion IDs you have to provide when sending measurements for ingested metrics. |

Mapping level | Ingestion IDs are used when sending measurements, which will map to a certain metric of a specific subject. In most cases the ingestion ID for a metric is configured on the subjects containing the metric. However, in some cases a child subject may be responsible for sending measurements for its parent subject (e.g. a sensor as child subject which measures the temperature of an asset which is its parent subject). In those cases it may be useful to configure ingestion IDs on the child subjects instead. This makes it possible to move a subject to another parent without having to update the ingestion ID. |

#### Measurement filtering

In certain situations, you may not have the ability to filter the data that you wish to send from your device to the Blockbax Platform. This can result in a lot of measurements that add little value to the insights you wish to obtain. In order to optimize costs we allow you to define a filtering that determines when a measurement is processed, or skipped.

There are currently 3 options to perform filtering on measurements:

Filter | Description |
---|---|

Value | Evaluate the incoming measurement to a defined threshold. This way you can compare whether the measurement is greater than, less than, equal to or unequal to a specified value. |

Difference | Compare the incoming measurement to its previous valid measurement by comparing the difference, absolute difference, percentage difference, or the absolute percentage difference to a specified value. |

Date | Compare the difference with the date of the previous measurement to a specified interval in milliseconds, seconds, minutes or hours. |

### Calculated metric

A calculated metric can be computed from the values of one or more metrics and properties within a subject. A simple example will be an electrical station for which you want to compute the wattage by multiplying voltage with amperage (this is made visible in the examples).

The main elements of a calculation are the name (1) and type (2) together with the arguments (3) that can be used in the calculation (4).

#### Arguments

In a calculation, metrics and properties of type Number, Text and Location within the subject type can be used. These metrics and properties are referred to as the arguments of the calculation.

Attribute | Description |
---|---|

Source | Either a metric or a property present on the subject type |

Alias | A name without spaces that can be used to refer to the argument in the computation |

Max age | For metric sources this specifies how long ago a measurement may have been received to be used for the calculation |

For metrics configured as argument, it is possible to use their value and/or date in the computation. For example, if we add a Wattage metric as argument with alias `wattage`

, we can use its value with `wattage.number`

and its date in milliseconds with `wattage.date`

.

For properties configured as argument, it is possible to use their value. For example, if we add a property Height with alias `height`

, we can use its value with `height.number`

.

The field name for the value of the metric or property differs per type:

- Numeric metrics and properties:
`.number`

- Textual metrics and properties:
`.text`

- Location metrics and properties:
`.latitude`

,`.longitude`

and`.altitude`

The value of numeric and textual metrics and properties can also be retrieved with `.value`

, but this is deprecated in favor of `.number`

and `.text`

.

##### Max age explained

A calculation is always triggered when a measurement is received on a metric that is used in the calculation. Next, measurements of the other metrics are retrieved based on the max age that is configured. Here are some examples:

Max age metric 1 | Max age metric 2 | Results |
---|---|---|

0 seconds | 0 seconds | The calculation will only proceed when both metrics have a measurement with the same timestamp (in milliseconds) |

0 seconds | empty | Measurement for metric 1 received, the last known measurement of metric 2 will be used for the calculation. Measurement for metric 2 received, the calculation will only proceed when there is a measurement of metric 1 with the same timestamp (in milliseconds) |

empty | 60 seconds | Measurement for metric 1 received, the calculation will proceed when there is measurement of metric 2 which is not older than 60 seconds. Measurement for metric 2 received, the last known measurement of metric 1 will be used for the calculation |

#### Calculation

The calculation is defined using the editor in a user-friendly language. The language allows you to:

- define variables;
- apply basic arithmetic operations;
- use functions for more advanced operations;
- save and load values in between consecutive calculations;
- use conditionals;
- output the result of your calculation.

Last but not least, formulating a calculation is getting more convenient when you use the editor features and watch the examples.

##### Define variables

For your convenience and readability of your calculation it is possible to split your calculation into smaller calculations. Each of the smaller calculations may be saved in a variable definition. An example of this is `wattage = voltage.number * amperage.number`

in the example at the start of this section. This allows referring to the result of the computation `voltage.number * amperage.number`

via the name `wattage`

.

##### Arithmetic operations

All basic arithmetic operations can be applied to values in your calculation:

Operation | Symbol | Example | Comment |
---|---|---|---|

Addition | + | 5 + 7 | - |

Subtraction | - | 10 - 5 | - |

Multiplication | * | 3 * 9 | - |

Division | / | 10 / 2 | Division by 0 will produce no result |

Parenthesis | ( ) | (5 + 7) * 8 | Here 5 + 7 takes precedence over multiplication by 8 |

Multiplication and division take precedence over addition and subtraction, as per the standard arithmetic rules. Any (decimal) number values, variable definitions, loaded values or input argument values can be used in arithmetic. Operations can also be chained. Some examples:

```
load sum with default 0
a = voltage.number * 5.5
b = a - 7 + 8 / 2
c = sum + b
save c as sum
d = (c + 10) * 5
output d
```

##### Functions

Many default computations are available as functions. Functions can be used anywhere in your computation where a numeric value is expected, and can take as input any literal value, input argument value, loaded value or arithmetic. For example: compute and output the maximum value of a metric argument, with alias `inputMetric`

, by using the `max`

function:

```
load maxValue
newMaxValue = max(maxValue, inputMetric.number)
save newMaxValue as maxValue
output newMaxValue
```

## abs(value)

Computes the absolute value of a number value.

Parameter | Description |
---|---|

value | Input value to compute absolute value for |

## acos(value)

Computes the angle in degrees whose cosine is the specified number.

Parameter | Description |
---|---|

value | Value representing a cosine |

## asin(value)

Computes the angle in degrees whose sine is the specified number.

Parameter | Description |
---|---|

value | Value representing a sine |

## atan(value)

Computes the angle in degrees whose tangent is the specified number.

Parameter | Description |
---|---|

value | Value representing a tangent |

## atan2(y,x)

Computes the angle in degrees whose tangent is the quotient of two specified numbers

Parameter | Description |
---|---|

y | The y coordinate of a point |

x | The x coordinate of a point |

## ceil(value)

Rounds decimal numbers down to the closest integer value.

Parameter | Description |
---|---|

value | Value to round |

## cos(degrees)

Computes the cosine of the provided number of degrees.

Parameter | Description |
---|---|

degrees | Input in degrees |

## distanceBetween(latitudeFrom,longitudeFrom,latitudeTo,longitudeTo)

Computes the Geodetic (across the Earth’s surface) distance in meters between two points using the WGS84 coordinate reference system.

Parameter | Description |
---|---|

latitudeFrom | From position latitude in degrees |

longitudeFrom | From position longitude in degrees |

latitudeTo | To position latitude in degrees |

longitudeTo | To position latitude in degrees |

## distanceBetweenXY(xFrom,yFrom,xTo,yTo)

Computes the Euclidean (straight) distance between two 2D points. Distance is in the same unit as provided coordinates.

Parameter | Description |
---|---|

xFrom | From position x coordinate |

yFrom | From position y coordinate |

xTo | To position x coordinate |

yTo | To position y coordinate |

## distanceBetweenXYZ(xFrom,yFrom,zFrom,xTo,yTo,zTo)

Computes the Euclidean (straight) distance between two 3D points. Distance is in the same unit as provided coordinates.

Parameter | Description |
---|---|

xFrom | From position x coordinate |

yFrom | From position y coordinate |

zFrom | From position z coordinate |

xTo | To position x coordinate |

yTo | To position y coordinate |

zTo | To position z coordinate |

## floor(value)

Rounds decimal numbers down to the closest integer value.

Parameter | Description |
---|---|

value | Value to round |

## log(value)

Computes the natural logarithm of a value.

Parameter | Description |
---|---|

value | Input value to compute logarithm for |

## max(value1,value2)

Computes the maximum value of two number values.

Parameter | Description |
---|---|

value1 | First value to compare |

value2 | Second value to compare |

## min(value1,value2)

Computes the minimum value of two number values.

Parameter | Description |
---|---|

value1 | First value to compare |

value2 | Second value to compare |

## now()

Provides the current time in milliseconds since Unix Epoch.

## pi()

Provides the value of Pi with eight decimals of precision.

## pow(value,power)

Computes the power of a number value.

Parameter | Description |
---|---|

value | Input value to compute the power of |

power | The power to raise the value to; fractional and negative numbers are supported |

Example: `pow(3, 2)`

means three to the power of two which results in 9.

## random(lower,upper)

Generates a random decimal number from a continuously uniform distribution between provided upper and lower bounds.

Parameter | Description |
---|---|

lower | Lower bound for random number (inclusive) |

upper | Upper bound for random number (exclusive) |

## remainder(dividend,divisor)

Computes the remainder of a division. For example, the remainder of `9 / 2.5`

is `1.5`

. The remainder always takes the sign of the dividend.

Parameter | Description |
---|---|

dividend | Dividend of the division |

divisor | Divisor of the division |

## round(value,precision)

Rounds decimal numbers to a specified number of decimals. Precision rounded down to closest integer value.

Parameter | Description |
---|---|

value | Value to round |

precision | Number of decimals to round to (max 8). Decimals precision values are rounded down to nearest integer. |

## sin(degrees)

Computes the sine of the provided number of degrees.

Parameter | Description |
---|---|

degrees | Input in degrees |

## sqrt(value)

Computes the square root of a number value.

Parameter | Description |
---|---|

value | Input value to compute square root for |

## tan(degrees)

Computes the tangent of the provided number of degrees.

Parameter | Description |
---|---|

degrees | Input in degrees |

## toDegrees(radians)

Converts an angle from radians to degrees.

Parameter | Description |
---|---|

degrees | Input angle in radians |

## toRadians(degrees)

Converts an angle from degrees to radians.

Parameter | Description |
---|---|

degrees | Input angle in degrees |

##### Save and load values

It is possible to save and load values between consecutive evaluations of the calculation. A numeric value can be saved at the end of a calculation, so that it can be loaded the next time the calculation is evaluated. **A maximum of 10 values can be saved and if a value exceeds 8 decimal places, it will be rounded off**. For example: we want to compare the value of a metric to its previous value.

```
load previousValue
difference = bitcoin.number - previousValue
save bitcoin.number as previousValue
output difference
```

Values must be loaded at the beginning of a calculation with a name that that is later used to save the value. See `load previousValue`

will create a variable with name *previousValue*. If a variable is loaded, a value must be saved in it at some point during the calculation. See `save bitcoin.number as previousValue`

which saves the current bitcoin metric value in previousValue to be used by the next evaluation of the calculation.

It is possible to use the value of a loaded variable to update its own value. For example when we are keeping a sum:

```
load bitcoinSum with default 0
newSum = bitcoinSum + bitcoin.number
save newSum as bitcoinSum
output newSum
```

In this case a default must be provided when loading the variable, see `load valueSum with default 0`

. This default value is used when the load variable does not yet have a value, if this default was not present it would never be possible to set a value initially.

##### Conditionals

Within the calculated metric editor you are able to make use of conditionals to extend the ability to perform operations based on certain conditions. This is achieved by using `if`

, `then`

and `else`

statements. To further extend the ability to make use of conditionals in the platform, you can combine conditions using `and`

/ `or`

statements.

## if-then-else

If-then-else statements are used to to allow conditional execution of statements. A condition needs to be stated after the `if`

statement. If the said condition is true, the `then`

value will be used. If the condition returns false the `else`

value will be used.

```
if <condition> then <value1> else <value2>
```

## checking for equality

A condition can check whether two values are the same or not with the operators `==`

(equal to) and `!=`

(not equal to). This is supported for both numeric:

```
voltage.number == 230
```

and textual values:

```
soilType.text != "Peat moss"
```

## comparing numbers

Numbers can also be compared with the operators `>`

(greater than), `>=`

(greater than or equal to), `<`

(less than), and `<=`

(less than or equal to):

```
temperature.number > 25
```

## and

Using the `and`

operator allows you to combine two conditions into a single expression, in this case both conditions need to return true in order for the `and`

operator to return true.

In case of one of the conditions returning false, the operator will also return false.

The syntax can be seen below:

```
<condition1> and <condition2>
```

To illustrate how you can combine `if`

, `then`

and `else`

statements with `and`

, see the sample syntax below:

```
if <condition1> and <condition2> then <value1> else <value2>
```

## or

With the `or`

operator, you are able to combine two conditions into a single expression. If at least one of these conditions are found to be true then the `or`

operator will also return true.

```
<condition1> or <condition2>
```

To illustrate how you can combine `if`

, `then`

and `else`

statements with `or`

, see the sample syntax below:

```
if <condition1> or <condition2> then <value1> else <value2>
```

## hasValue

With a `hasValue`

condition, you can check whether a metric or a property has value. Normally, inspecting the value of a metric for which there is currently no measurement, means that the calculation will be terminated and that there is no output. If you want to make a decision based on the presence of a value, you can use `hasValue`

:

```
setpoint = if hasValue(setpointManual.number)
then setpointManual.number
else setpointSchedule.number
```

##### Output

Each calculation must specify exactly **one** output value at the end of the calculation as follows: `output <value>`

. In this case `<value>`

can be any (decimal) number value, arithmetic, variable definition, loaded value or argument value.

- A calculated metric is triggered when a measurement of one of the arguments is received.
- A calculation will only take place when all variables within the calculation are present.
- Above condition holds for each saved value and output independently. So a loaded value could be saved in a previous evaluation while no output was generated.
- If an output value exceeds 8 decimal places, it will be rounded off.

##### Editor features

The editor has some smart features which makes it easy to use, but these features are not visible from the outside. The features are listed in the table and showed in the video below the table.

Feature | Action | Description |
---|---|---|

Extra info | hover | Use the mouse to hoover over the elements in the editor to see extra information. |

Option list | ctrl + space | Get to see the list options you can select in the current context. |

Option description | ctrl + space | Hit ctrl + space a second time to see the description of the option. |

Commenting | // or /_ -> _/ | Put `//` at the start of a line to write a single-line comment. Alternatively start a multi-line comment with `/*` and end it with `*/` |

Red underline | N/A | A (part of) the calculation has a red underline when the editor detects a possible mistake. Hover the red underline for more details and suggestions. |

#### Examples

This section show some examples to make calculated metrics concrete and explain the settings.

##### Wattage calculation

The wattage will only be calculated when when voltage and amperage are received with the same timestamp. This is due to zero seconds max age setting for both arguments.

##### Heat index

The heat index will be calculated with the last known temperature measurement when the humidity measurement is received and with a humidity measurement not older than 10 seconds when a temperature measurement is received.

##### Speed

The speed is calculated with the last known location which contains the latitude, longitude and date values. This example includes save and load values, functions and arithmetic operations which all together shows the power and possibilities of a calculated metric.

##### Load Factor

In order to calculate the load factor, there are various conditions that need to be met in order to create a representative calculated metric. In this case, we are calculating the load factor when rP and qP are both positive, else a 0 value is returned as output for the calculated metric. This example shows the ability to use conditionals.

### Simulated metric

A simulated metric generates data based on the configuration. Example: a two hour sinus.

Attribute | Description |
---|---|

Name | Give the simulated metric a descriptive name so you can easily recognize and find it. |

Simulation type | Choose between a random walk and sinus two hours. |

Measurement interval | Choose with which frequency measurements are generated. |

Value lower bound | Determine what the lowest value will be for the measurements that are generated. |

Value upper bound | Determine what the highest value will be for the measurements that are generated. |

### Forecasted metric

A forecasted metric utilizes historical data to forecast future values of another existing metric. In order to achieve this artificial intelligence (AI) is utilized. When you setup a forecasted metric, your existing data is prepared and a deep learning model is trained for your specific forecast. A new incoming measurement on the metric to forecast will instantly lead to a new measurement on the forecasted metric with its forecasted future value and future date. Conceptually it is thus quite similar to a calculated metric which ‘calculates’ future values.

The main configuration elements of a forecasted metric are the name (1), the metric to forecast and how far in the future forecasts should be made (2), and training input metrics (3) that consists of the metric to forecast as well as additional metrics that have a correlation and thus can help in creating a more accurate forecast. Relevant properties will be selected automatically as training data.

#### Forecast accuracy

In order to monitor the accuracy of your forecasts two key graphs are present for evaluating averaged hourly forecast accuracy over the past week. The first graph (1) compares actual measurements with forecasted values, providing insight into how well your model matches real data. The second graph (2) shows absolute errors, indicating accuracy. You can select whether you want to to see aggregated accuracy metrics over all subjects or for a specific subjects (3).

Immediately following the initial training, these graphs are populated with forecasts from one week prior to provide an immediate assessment of the model’s performance. Data from this period is excluded from the training to ensure that accuracy metrics are not skewed.

#### Recent trainings

Models are updated monthly to reflect recent trends by initiating retraining with the active model’s inputs. Following initial training, any input alterations trigger a new training. Models that achieve higher accuracy (4) are accepted (1) and will become active (2). On the accuracy graphs you will see when a model has become active (see ‘Version 4’ indicator in previous image). Models that are not accepted are only shown in the recent trainings overview.

Training times for completed models appear in the training duration column (3). Current training progress is represented by a progress bar. Users have the option to enable notifications for updates on training completion (5).

If altering the model’s inputs doesn’t improve accuracy, the changes can be easily undone.

#### Considerations

In order to get the most accurate forecasts, please take the following considerations in mind:

- Whether specific historic data lends itself for forecasting is very use case dependent and with that accuracy expectations may differ widely per use case. Use domain knowledge to assess whether you expect there are underlying dynamics in place which could make a relatively accurate forecast feasible.
- Forecast accuracy heavily depends on the amount and quality of currently available data.
- Without sufficient data over a sufficient period, model training cannot occur, leading to a failed training.
- Adding or removing input metrics can significantly impact model accuracy, either positively or negatively. Use domain knowledge to assess their relevance before adding them.
- Metrics with high levels of randomness or noise may be difficult to predict or be inherently unpredictable. Over longer forecast periods, cummulative effect of noise can worsen forecast quality.

Please contact us if you want to get more information or get an appointment to see if your use case is suitable for forecasting.