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Entity Resolution

Definition

Asserts that every distinct entity is appropriately represented once and only once

In-Depth Overview

This check performs automated entity name clustering to identify entities with similar names that likely represent the same entity. It then assigns each cluster a unique entity identifier and asserts that every row with the same entity identifier shares the same value for the designated distinction field

Field Scope

Single: The rule evaluates a single specified field.

Accepted Types

Type
String

General Properties

Name Supported
Filter
Allows the targeting of specific data based on conditions
Coverage Customization
Allows adjusting the percentage of records that must meet the rule's conditions

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.

Examples

Direct Conditions

Simply specify the condition you want to be met.

Correct usage
O_TOTALPRICE > 1000
C_MKTSEGMENT = 'BUILDING'
Incorrect usage
WHERE O_TOTALPRICE > 1000
WHERE C_MKTSEGMENT = 'BUILDING'

Combining Conditions

Combine multiple conditions using logical operators like AND and OR.

Correct usage
O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'
(L_SHIPDATE = '1998-09-02' OR L_RECEIPTDATE = '1998-09-01') AND L_RETURNFLAG = 'R'
Incorrect usage
WHERE O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'
O_TOTALPRICE > 1000, O_ORDERSTATUS = 'O'

Utilizing Functions

Leverage Spark SQL functions to refine and enhance your conditions.

Correct usage
RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - INSTR('-', O_ORDERPRIORITY)
) = 'URGENT'
LEVENSHTEIN(C_NAME, 'Supplier#000000001') < 7
Incorrect usage
RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - CHARINDEX('-', O_ORDERPRIORITY)
) = 'URGENT'
EDITDISTANCE(C_NAME, 'Supplier#000000001') < 7

Using scan-time variables

To refer to the current dataframe being analyzed, use the reserved dynamic variable {{ _qualytics_self }}.

Correct usage
O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM {{ _qualytics_self }}
    WHERE O_TOTALPRICE > 1000
)
Incorrect usage
O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM ORDERS
    WHERE O_TOTALPRICE > 1000
)

While subqueries can be useful, their application within filters in our context has limitations. For example, directly referencing other containers or the broader target container in such subqueries is not supported. Attempting to do so will result in an error.

Important Note on {{ _qualytics_self }}

The {{ _qualytics_self }} keyword refers to the dataframe that's currently under examination. In the context of a full scan, this variable represents the entire target container. However, during incremental scans, it only reflects a subset of the target container, capturing just the incremental data. It's crucial to recognize that in such scenarios, using {{ _qualytics_self }} may not encompass all entries from the target container.

Specific Properties

Name Description
Distinction Field
The field that must hold a distinct value for every distinct entity
Pair Substrings
Considers entities a match if one entity is part of the other
Pair Homophones
Considers entities a match if they sound alike, even if spelled differently
Spelling Similarity
The minimum similarity required for clustering two entity names

Anomaly Types

Type Supported
Record
Flag inconsistencies at the row level
Shape
Flag inconsistencies in the overall patterns and distributions of a field

Example

Objective: If you have a businesses table with an id field and a name field, this check can be configured to resolve name and use id as the distinction field. During each scan, similar names will be grouped and assigned the same entity identifier and any rows that share the same entity identifier but have different values for id will be identified as anomalies.

Sample Data

BUSINESS_ID BUSINESS_NAME
1 ACME Boxing
2 Frank's Flowers
3 ACME Boxes
{
    "description": "Ensure a `businesses` table with an `BUSINESS_ID` field and a `BUSINESS_NAME` field shares the same `entity identifier`",
    "coverage": 1,
    "properties": {
        "distinct_field_name":"BUSINESS_ID",
        "pair_substrings":true,
        "pair_homophones":true,
        "spelling_similarity_threshold":0.6
    },
    "tags": [],
    "fields": ["BUSINESS_NAME"],
    "additional_metadata": {"key 1": "value 1", "key 2": "value 2"},
    "rule": "entityResolution",
    "container_id": {container_id},
    "template_id": {template_id},
    "filter": "1=1"
}

Anomaly Explanation

In the sample data above, the entries with BUSINESS_ID 1 and 3 do not satisfy the rule because their BUSINESS_NAME values will be marked as similar yet they do not share the same BUSINESS_ID

graph TD
A[Start] --> B[Retrieve Original Data]
B --> C{Which entities are similar?}
C --> D[Assign each record an entity identifier]
D --> E[Cluster records by entity identifier]
E --> F{Do records with same<br/>entity identifier share the<br/>same distinction field value?}
F -->|Yes| I[End]
F -->|No| H[Mark as Anomalous]
H --> I

Last update: June 14, 2024