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:
Those rows which have exactly same
fname
,stNo
andadd1
=> exact match with max score 1 ORThose rows which have exactly same
fname
,dob
andssn
=> exact match with max score 1 ORThose rows which have exactly same
fname
andemail
=> exact match with max score 1
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