Optum, Inc.

Daniel G McCreary

Mark Gregory Megerian

Parker J Erickson

Alex Li

Joshua A Meekhof

Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, for recommending and/or defining cross-schema mappings, or a mapping from a data element type of a first schema to one of a second schema. In one example embodiment, a method is provided. The method includes identifying a plurality of canonical data element types belonging to a canonical schema and an external data element type belonging to an external schema. The method includes obtaining a multi-dimensional representation for each data element type and determining a predicted similarity score for each canonical data element type using comparison results from a mapper machine learning model. The method further includes generating a recommendation data object indicating one or more mappings between the external data element type and a selected subset of the plurality of canonical data element types, the subset selected according to predicted similarity score.

Predictive recommendations for schema mapping

There is a need for faster and more accurate predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes identifying a first predictive entity embedding for the first predictive entity and a second predictive entity embedding for a second predictive entity; determining, using a similarity determination machine learning model and based at least in part on the first predictive entity embedding and the second predictive entity embedding, a predicted cross-entity similarity measure; and performing one or more prediction-based actions based at least in part on the predicted cross-entity similarity measure.

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St. Louis Park, MN, United States of America

Daniel G McCreary