
In the upper left section, you can observe an estimation for cases currently in execution, indicating their status regarding target duration compliance:

At the bottom left, a table provides a comprehensive overview of ongoing runs along with their associated predictions. This table includes the following details:
Moreover, the table features filtering functionality by a value-attribute pair. Each row in the table is equipped with an icon on the right, enabling users to navigate directly to the tracking viewer filtered by the corresponding case ID.

In the upper right section, a breakdown of risk by attributes is presented, featuring three distinct graphs. Users have the option to choose the attribute by which to group the data, and a button facilitates switching between the following graphs:
Risk Estimation of Ongoing Predictions: This graph illustrates, for each value of the selected attribute, the number of cases falling under each of the three risk levels. These levels are represented by stacked bars: red for cases above the target duration, yellow for cases at risk of non-compliance with the target duration, and green for cases below the target duration. Users can toggle between absolute and percentage representations.

The graph on the right displays the various attribute values and the time required for monthly decreases to attain the value defined in the selector within the chosen timeframe (3, 6, 9, or 12 months). Additionally, it offers customization options via a drop-down list, enabling users to select either the “current best performance attribute” (aligned with the choice in the “Expected performance” component filter) or “target duration”.

Sometimes, it may not be possible to make predictions on running cases because there are no previous existing variants. An ornage message, as shown below, will be displayed:

If there are no cases that receive a true prediction (all correspond to the new variant), an error will be returned indicating that there is not enough historical data to provide a prediction.
If the percentage of cases is mixed, that is, there are cases that are predicted and others that are not, only the cases with a prediction will be shown and a warning will be included to inform that the prediction is not for all the data.