Tamr can accelerate the schema mapping process by learning which input attributes are likely candidates to be mapped to unified attributes. The process works as follows. Some parts of this process are run by you and others are run by Tamr machine learning.
- Create unified attributes and partially populate them, either manually or using bootstrapping (done by you).
- Update the unified dataset by running the job (done by Tamr).
- Learn from mappings by running the job (done by Tamr).
- Generate mapping suggestions (done by Tamr).
- Review and accept mapping suggestions, or reject them (done by you).
- Repeat if necessary (done by you).
Steps 1 and 5 involve human feedback and input.
Steps 2-4 are where Tamr learns and generates suggested mappings for attributes in the unified dataset.
You can use the sliding bar to control similarity of the mapping suggestions generated by Tamr. This allows you to choose how similar attributes for Tamr suggestions should be.
You can also add new datasets to the project and repeat this process by running steps 2-4 again, and reviewing, accepting, or rejecting Tamr suggestions.
Updated 4 months ago
If you are running a mastering or categorization project, you can continue optimizing the schema for the unified dataset as needed. If, on the other hand, you are satisfied with the set of unified attributes, you can proceed to preview the records in the unified dataset.
|Schema Specifics for Mastering|
|Schema Specifics for Categorization|
|Previewing the Unified Dataset|