Mapping Assist overview

Mapping assist uses a pre-trained artificial intelligence (AI) algorithm and community mappings to suggest mappings for the fields in the nodes that you add when building a flow.

You can generate mapping suggestions after you add an action to a flow. These suggestions identify output from previous nodes in the flow that might be suitable as input values for the fields in the current node.

When you populate the target fields for an action in your flow, you can click Generate mapping suggestions to preview and apply generated suggestions. You can also see mapping suggestions, if available, when you click Insert a mapping Insert a mapping icon to map an individual field.
Screenshot that shows suggested mappings for a field. The first mapping is marked with a community icon and the next two mappings are marked with semantic mapping icons.
To identify matches, mapping assist uses a pre-trained algorithm that applies fuzzy matching and semantic rules. These semantic mapping suggestions are indicated by a rating icon Rating icon that indicates the confidence percentage of the mapping. Hover the mouse over the icon to view the percentage.
When social learning is enabled, suggestions can also be based on community mappings. Community mappings are shown if other App Connect users have made the same source-to-target mapping. These community mappings are indicated by a community icon Community suggestion icon that indicates the confidence percentage of the mapping. A mapping that is used fewer than 10 times by the community has a confidence level of 85%. A mapping that is used 10 times or more, has a confidence level of 90%, which increases to 95% for a mapping that's used 25 times, and to 100% when it's used 50 times. In the following example, when you hover over the community icon, you see that the mapping has a rating of 85%, which is based on the same source-to-target mappings in the App Connect user community.
Screenshot that shows when you hover over the confidence rating for a community suggestion, the confidence percentage is 85%
Note: Social learning is enabled by default. When social learning is enabled, the field mappings in your flows and other users' flows are anonymized and aggregated. This aggregated data is then used to provide these community mapping suggestions to all users. If you don't want your data to be used to create mapping suggestions, you can disable social learning. When you disable it, you won't see any community suggestions when you use mapping assist. To disable social learning, click Instance settings Navigation pane icon that represents the Instance settings page. and turn off social learning.
Mapping assist algorithm techniques for estimating matches
The mapping assist algorithm currently provides mapping suggestions for simple (not nested) fields, and navigates parent hierarchy structures to suggest the best possible matches for nested fields.
Mapped nested fields
For nested array fields, mapping assist returns suggestions as follows.
  • Mapping suggestions are generated for exact schema matches, where the source array and destination array (schema structure and field names) are identical. For example, this behavior applies if you want to transfer data across two separate instances of an application for which you set up separate App Connect accounts.
    Mapped array fields for exact schema matches
  • Mapping suggestions are also generated for an array of complex fields by using artificial intelligence (AI) modeling to identify the best possible matches that can be inserted automatically as the best suggestions, or inserted manually at field level.
    Mapping suggestions for complex array fields

If an action has multiple fields with the same or a similar name, a mapping that is identified as a best suggestion is inserted automatically only into the field that is deemed to be the closest match. For example, if there are two fields called Email and Email adress, an Email mapping suggestion will have a higher level of accuracy associated with the Email field, and is inserted automatically into that field only. To populate any of the other fields with the same mapping suggestion, you must insert that mapping into the field manually.

The data types of the fields are also considered when suggesting matches; for example, source and destination fields with identical names, but different data types, are not considered a match. In the following example where JSON schema has been generated in the CSV parser, no mapping will be suggested for the Catalog ID integer field (123) because the source Catalog ID field in the generated schema is set to a string data type. If changed to integer, a suggested mapping with a 100% rating is expected between these two fields.

Simple one-to-one field mapping
Community mapping history used for estimating matches
If social learning is enabled, App Connect Designer collects and stores the anonymized mapping data of community users, excluding any personal information. The sole purpose of this data collection is to preserve mapping selections between source and target nodes so that they can be learned and presented as future mapping suggestions to App Connect users.

Disclaimer: You are responsible for any example data (including any personal information) used for, and any decision to proceed with, any automated flows which are suggested for your convenience when IBM® App Connect is used. IBM has no responsibility for any such automated flows and IBM warranties and support will not apply to them; they are used at your risk. IBM might periodically modify the underlying learning models in App Connect through updates, fixes, or patches in order to improve App Connect performance.