Entity Resolution

Definition

Asserts that records with similar values across the configured target fields are resolved as the same entity and share a single distinction field value.

Overview

Entity Resolution is a multi-field rule. You pick one or more target fields that describe the entity (for example, name, industry, country, or phone), assign each field a blocking or comparison role, and choose how its values are compared. The platform clusters records whose weighted comparison score meets the composite match threshold. Each cluster is assigned a unique cluster identifier (_qualytics_entity_id).

Once clusters are built, the rule checks the distinction field: every record in the same cluster must share the same value of the distinction field. Clusters that hold more than one value of the distinction field are flagged.

Typical use cases:

• Match customer or company records with name and address variations.
• Consolidate duplicate entities across systems that record slightly different spellings.
• Identify fuzzy matches for deduplication before pushing records downstream.

Field Scope

Calculated: Entity Resolution does not take a fixed list of fields. Instead, the platform derives the evaluated fields from the target fields you configure. Each entry names one field, its role, and its comparison type. The distinction field is configured separately.

Distinction Field: Accepted Types

Target Field Roles

Target Field Types and Comparisons

The same exact comparison behaves differently by role. With the Block role, a disagreement prevents the pair from being scored. With the Compare role, a disagreement contributes 0.0, but stronger evidence from other comparison fields can still make the pair a match.

General Properties

The filter allows you to define a subset of data upon which the rule will operate.

It requires a valid Spark SQL expression that determines the criteria rows in the DataFrame should meet. This means the expression specifies which rows the DataFrame should include based on those criteria. Since it's applied directly to the Spark DataFrame, traditional SQL constructs like WHERE clauses are not supported.

O_TOTALPRICE > 1000

C_MKTSEGMENT = 'BUILDING'

WHERE O_TOTALPRICE > 1000

WHERE C_MKTSEGMENT = 'BUILDING'

O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'

(L_SHIPDATE = '1998-09-02' OR L_RECEIPTDATE = '1998-09-01') AND L_RETURNFLAG = 'R'

WHERE O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'

O_TOTALPRICE > 1000, O_ORDERSTATUS = 'O'

RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - INSTR('-', O_ORDERPRIORITY)
) = 'URGENT'

LEVENSHTEIN(C_NAME, 'Supplier#000000001') < 7

RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - CHARINDEX('-', O_ORDERPRIORITY)
) = 'URGENT'

EDITDISTANCE(C_NAME, 'Supplier#000000001') < 7

O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM {{_qualytics_self}}
    WHERE O_TOTALPRICE > 1000
)

O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM ORDERS
    WHERE O_TOTALPRICE > 1000
)

Anomaly Types

Next Steps