Deterministic Matching

Ensuring higher matching accuracy and performance

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 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:

The above conditions would translate into the 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