Deterministic Matching

Deterministic Matching - Zingg Enterprise Feature

Zingg Enterprise allows the ability to plug rule based deterministic matching along with already Zingg AI's probabilistic matching. If the data contains sure identifiers like emails, SSNs, passport ids etc, we can use these attributes to resolve records. The deterministic matching flow is weaved into Zingg's flow to ensure that each record which has a match finds one, probabilistically, deterministically or both. If the data has known identifiers, Zingg Enterprise's deterministic matching highly improves both matching accuracy and performance.

Example for configuring it in json:

    "deterministicMatching":[  
        {  
           "matchCondition":[{"fieldName":"fname"},{"fieldName":"stNo"},{"fieldName":"add1"}]  
        },  
        {  
           "matchCondition":[{"fieldName":"fname"},{"fieldName":"dob"},{"fieldName":"ssn"}]  
        },   
        {  
           "matchCondition":[{"fieldName":"fname"},{"fieldName":"email"}]  
        }  
    ]  

Python code example:

detMatchNameAdd = DeterministicMatching('fname','stNo','add1')  
detMatchNameDobSsn = DeterministicMatching('fname','dob','ssn')  
detMatchNameEmail = DeterministicMatching('fname','email')  
args.setDeterministicMatchingCondition(detMatchNameAdd,detMatchNameDobSsn,detMatchNameEmail)  

How will it work:

Above conditions would translate into following:

  1. Those rows which have exactly same fname, stNo and add1 => exact match with max score 1 OR

  2. Those rows which have exactly same fname, dob and ssn => exact match with max score 1 OR

  3. Those rows which have exactly same fname and email => exact match with max score 1

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