UTL_MATCH with examples in Oracle for fuzzy logic | duplicate detection

The UTL_MATCH package in Oracle provides functions for string matching, which are useful for fuzzy matching and comparing strings based on their similarity. Here are some key functions within the UTL_MATCH package and examples of how to use them:

Key Functions

1. EDIT_DISTANCE

   • Computes the Levenshtein distance between two strings.
   • Measures the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one string into the other.

2. EDIT_DISTANCE_SIMILARITY

   • Computes a similarity score between two strings based on the Levenshtein distance.
   • The score ranges from 0 to 100, with 100 indicating identical strings.

3. JARO_WINKLER

   • Computes the Jaro-Winkler distance between two strings.
   • Suitable for comparing short strings such as names.

4. JARO_WINKLER_SIMILARITY

   • Computes a similarity score between two strings based on the Jaro-Winkler distance.
   • The score ranges from 0 to 100, with 100 indicating identical strings.

Examples

EDIT_DISTANCE

-- Calculate the Levenshtein distance between 'kitten' and 'sitting'

SELECT UTL_MATCH.EDIT_DISTANCE('kitten', 'sitting') AS edit_distance FROM dual;

-- Output: 3

EDIT_DISTANCE_SIMILARITY

-- Calculate the similarity score between 'kitten' and 'sitting'

SELECT UTL_MATCH.EDIT_DISTANCE_SIMILARITY('kitten', 'sitting') AS edit_distance_similarity FROM dual;

-- Output: 57 (approximately, indicating moderate similarity)

JARO_WINKLER

-- Calculate the Jaro-Winkler distance between 'dixon' and 'dicksonx'

SELECT UTL_MATCH.JARO_WINKLER('dixon', 'dicksonx') AS jaro_winkler_distance FROM dual;

-- Output: 0.8133333333333332 (distance metric, not similarity score)

JARO_WINKLER_SIMILARITY

-- Calculate the Jaro-Winkler similarity score between 'dixon' and 'dicksonx'

SELECT UTL_MATCH.JARO_WINKLER_SIMILARITY('dixon', 'dicksonx') AS jaro_winkler_similarity FROM dual;

-- Output: 81 (indicating high similarity)

Practical Example

Let's consider a practical example where you have a table of customer names, and you want to find names that are similar to a given input name.

-- Create a table to store customer names CREATE TABLE customers ( customer_id NUMBER PRIMARY KEY, customer_name VARCHAR2(100) ); -- Insert sample data INSERT INTO customers (customer_id, customer_name) VALUES (1, 'John Smith'); INSERT INTO customers (customer_id, customer_name) VALUES (2, 'Jon Smyth'); INSERT INTO customers (customer_id, customer_name) VALUES (3, 'Jane Doe'); INSERT INTO customers (customer_id, customer_name) VALUES (4, 'Johnny Smith'); -- Find customers with names similar to 'John Smith' SELECT customer_id, customer_name, UTL_MATCH.JARO_WINKLER_SIMILARITY(customer_name, 'John Smith') AS similarity FROM customers ORDER BY similarity DESC;

Explanation

1. Table Creation and Data Insertion: The customers table is created, and sample customer names are inserted.
2. Similarity Query: The query calculates the Jaro-Winkler similarity score between each customer's name and the input name 'John Smith'. The results are ordered by the similarity score in descending order, showing the most similar names first.

Conclusion

The UTL_MATCH package provides powerful tools for fuzzy matching and string comparison in Oracle. By using functions like EDIT_DISTANCE, EDIT_DISTANCE_SIMILARITY, JARO_WINKLER, and JARO_WINKLER_SIMILARITY, you can effectively handle and compare strings in scenarios where exact matches are not expected or required. These functions can be particularly useful in applications such as data cleaning, duplicate detection, and search enhancement.