Data model

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Attributes type

The Data Model supports the following attribute types:

• STRING
• NUMBER
• BOOLEAN
• OBJECT
• ARRAY
  • ARRAY OF STRINGS
  • ARRAY OF NUMBERS
  • ARRAY OF BOOLEANS
  • ARRAY OF OBJECTS
  • ARRAY OF ENUMS
• ENUM

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Data model reference

You can use data model reference feature to view attribute usage within the data model. You can now easily see where a specific attribute is being used by accessing the “View References” feature. This feature provides a list of process keys associated with each attribute and displays possible references, such as UI Elements.

For UI Elements, the references include the element label, node name, and UI Element key. Additionally, the context of the reference is provided, showing the node name and the UI element type along with its label. Users can conveniently navigate to the context by clicking the provided link to the node’s UI page.

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Sensitive data

To protect your data and your customer’s data, you can hide data that could be visible in the process details or in the browser’s console. You can now also secret data for a specific key.

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Reporting

The Use in Reporting tag is used for keys that will be used further in the reporting plugin.

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Generating data model

A data model can be generated using data values from a process instance. This can be done by either merging the data model with an existing one or replacing it entirely.

To generate a data model, follow these steps:

1. Open FlowX.AI Designer.
2. Go to the Definitions tab and select the desired process definition.
3. Select the Data Model tab and then click Generate data model button.
4. Add the process instance of the process from which you want to generate the data model.
5. Choose whether to replace the existing data model or merge it with the new one.
6. Click the Load Data button to display the data model body.
7. Finally, click Save button to save the generated data model.

By generating a data model, you can ensure that your data is structured and organized in a way that is appropriate for your business needs. It can also help you to identify any inconsistencies or errors in the data, allowing you to correct them before they cause problems down the line.