Mapping your data by applying smart suggestions
When you add an action to a flow in App Connect Designer, you can use Mapping Assist to help you populate the fields for the action with mapped data from previous nodes. Mapping Assist generates smart suggestions that are the best matches for the target fields.
About Mapping Assist
Mapping Assist uses a pre-trained algorithm and mapping histories to suggest mappings for the fields in an action when you build a flow. Mapping suggestions are generated as soon as you add an action to a flow, and these suggestions identify output from previous nodes in the flow, which could possibly serve as input for the fields in the action.
Suggestions of the best possible matches (top suggestions) can be automatically
inserted into fields with a single click. These top suggestions are estimated to have an 80% or
higher level of accuracy or relevance, and the count (N suggestions
) identifies the
total number of fields that will be populated with mappings.
In any fields that remain unpopulated, you can click to check for suggested mappings. If found, these mappings are shown in a Suggested mappings list, which displays up to five suggestions with assigned percentage matches with a 30% threshold. For each mapping, the field hierarchy identifies the origin of the content, such as the application or node name, action, and object; for example, Salesforce/Retrieve contacts/Contacts, Request filter parameters/Object/where, BigCommerce/Update order/orders, and For each: Parsed CSV row.
Techniques applied for matching
To identify matches, Mapping Assist uses a pre-trained algorithm that applies fuzzy matching and semantic rules, as well as mapping histories that are stored for started flows.
- Mapping Assist algorithm techniques for estimating matches
-
The Mapping Assist algorithm currently provides mapping suggestions for simple (unnested) fields, and navigates parent hierarchy structures to suggest the best possible matches for 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 would apply if
you wanted to transfer data across two separate instances of an application for which you had set up
separate App Connect accounts.
- 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 automatically inserted
as top suggestions, or manually inserted at a field level.
If an action has multiple fields with the same or a similar name, a mapping that is identified as a top suggestion will be automatically inserted 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 will be auto-inserted into that field only. If you want to populate any of the other fields with the same mapping suggestion, you must manually insert that mapping into the field.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 sourceCatalog ID
field in the generated schema is set to astring
data type. If changed tointeger
, a suggested mapping with a 100% rating is expected between these two fields. - 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 would apply if
you wanted to transfer data across two separate instances of an application for which you had set up
separate App Connect accounts.
- Mapping history techniques for estimating matches
-
App Connect Designer instances in which Mapping Assist is enabled will collect and store your mapping data in an internal database by tracking the mapping history of any flow that you start. The sole purpose of the data collection is to preserve mapping selections between source and destination nodes so that they can be learnt and presented as future mapping suggestions that reflect your personal preferences.
For example, if you have a started flow that includes an Insightly/Create lead/First name (destination) field, which is populated with mapped content from a Salesforce/Retrieve contact/First name (source) field, this mapping is preserved and will be suggested with a 100% rating in any future flows that you develop with these two applications if the same actions and node order apply.
The following rules apply:
- Mapping histories are stored for one-to-one field mappings between application (connector)
nodes. The Request, Response, and toolbox nodes are excluded.
- Mappings that include multiple field selections, transformations, or JSONata functions are not
stored. For example:
Note: The information collected by Mapping Assist is not used by any other App Connect Designer instance, or in any other capacity by IBM, and the mapping histories will be deleted when your App Connect Designer instance is deleted. - Mapping histories are stored for one-to-one field mappings between application (connector)
nodes. The Request, Response, and toolbox nodes are excluded.
Applying mapping suggestions
If Mapping Assist is enabled, when you add an action to a flow, you'll see a
message (briefly) above the filter bar. Generating suggestions
After all the potential mappings are discovered, the top N
suggestions are shown
next to an Insert suggestions link. (If multiple mappings with matching
ratings of 80% or higher are discovered for the same field, those mappings are excluded from the top
suggestions count to enable you to choose the best match. You can manually inspect these mappings
and make a choice as described later.)
To auto-populate the fields with the top suggestions, complete the following steps:
- Click Insert suggestions. Those fields are populated and the remaining
fields remain blank. Note:
- Existing mappings are preserved in any fields that you might have manually populated before clicking Insert suggestions.
- If multiple mappings with matching ratings of 80% or higher are discovered for the same field, that field will not be auto-populated. You will instead be expected to manually inspect such mappings to determine the best match for the data that you want to map, as described later.
- Hover over the mapping in each auto-populated field and check the tooltip to verify that the
inserted data from the specified node is the best match.
If you want to replace the mapping in an auto-populated field, you can do so as follows:
- Delete the mapping from the field. (When you delete the mapping, notice that its
top suggestions
count (N suggestions
) is displayed again, and you can auto-populate the field again by clicking Insert suggestions. If you choose to add your own preferred mapping as described in the following steps, the count is removed and Insert suggestions is disabled.) - Remove the focus from the field and then click within the field again to display the
Suggested mappings list. Alternatively, click the Insert a
mapping icon to open the Available
mappings list and then click Suggested mappings. Then choose
another mapping:
- Inspect the mappings and then choose any one that you prefer. (You can view the percentage
matches of these mappings by hovering over the rating icon .)
- If you would prefer to choose from a wider range of mappings, click All
mappings to switch to the Available mappings list, which displays
all mappings from all the previous nodes in the flow. Expand the sections to locate and select your
required mapping.Tip: From the Available mappings list, you can switch back to the Suggested mappings list by clicking Suggested mappings.
- Inspect the mappings and then choose any one that you prefer. (You can view the percentage
matches of these mappings by hovering over the rating icon .)
In any remaining fields that were not automatically populated, you can manually insert mappings into each field as follows:
- Click within the field.
- If suggested mappings are available for this field, the Suggested
mappings list is immediately displayed. These mappings fall into two categories, and you
can make a selection as follows:
- Multiple mappings with the same percentage match: Inspect any such mappings to decide on the best match and then click the mapping that you prefer.
- Mappings with individual percentage matches of 30% or higher: Inspect such mappings to decide on the best match and then click the mapping that you prefer.
If you prefer to choose from a wider range of mappings, you can click All mappings to switch to the Available mappings list.
- If no suggested mappings are discovered for this field, click the Insert a mapping icon to open the Available mappings list and then select your required mapping from one of the sections.
If no top suggestions are discovered for an action, or if suggested mappings are not discovered
for any of the fields, the initial Generating suggestions
message is replaced by a
grayed-out Insert suggestions link when the discovery process completes.
For either of these cases, you must manually select mappings by clicking within the fields:
- If suggested mappings are available, you can use the Suggested mappings list to inspect and select mappings.
- If no suggested mappings were found, you can use the Insert a mapping
icon to choose a mapping from the Available mappings
list. Note that the Suggested mappings link will be grayed out in this list.