SQL 模糊连接 - MSSQL

han*_*olo 6 sql t-sql fuzzy-search fuzzy-logic fuzzy-comparison

我有两组数据。现有客户和潜在客户。

我的主要目标是弄清楚是否有任何潜在客户已经是现有客户。但是,跨数据集的客户命名约定是不一致的。

现有客户

Customer /  ID
Ed's Barbershop /   1002
GroceryTown /   1003
Candy Place /   1004
Handy Man / 1005
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潜在客户

Customer
Eds Barbershop
Grocery Town
Candy Place
Handee Man
Beauty Salon
The Apple Farm
Igloo Ice Cream
Ride-a-Long Bikes
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我想写一些像下面这样的选择语句来达到我的目标:

SELECT a.Customer, b.ID
FROM PotentialCustomers a LEFT JOIN
     ExistingCustomers B
     ON a.Customer = b.Customer
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结果将类似于:

Customer /  ID
Eds Barbershop  / 1002
Grocery Town    / 1003
Candy Place / 1004
Handee Man  / 1005
Beauty Salon /  NULL
The Apple Farm /    NULL
Igloo Ice Cream / NULL
Ride-a-Long Bikes / NULL
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我对 Levenshtein Distance 和 Double Metaphone 的概念有些熟悉,但我不确定如何在这里应用它。

理想情况下,我希望 SELECT 语句的 JOIN 部分读取如下内容:LEFT JOIN ExistingCustomers as B WHERE a.Customer LIKE b.Customer但我知道语法不正确。

欢迎任何建议。谢谢!

Kas*_*shi 6

以下是如何使用 Levenshtein Distance 完成此操作:

创建这个函数:(先执行这个)

CREATE FUNCTION ufn_levenshtein(@s1 nvarchar(3999), @s2 nvarchar(3999))
RETURNS int
AS
BEGIN
 DECLARE @s1_len int, @s2_len int
 DECLARE @i int, @j int, @s1_char nchar, @c int, @c_temp int
 DECLARE @cv0 varbinary(8000), @cv1 varbinary(8000)

 SELECT
  @s1_len = LEN(@s1),
  @s2_len = LEN(@s2),
  @cv1 = 0x0000,
  @j = 1, @i = 1, @c = 0

 WHILE @j <= @s2_len
  SELECT @cv1 = @cv1 + CAST(@j AS binary(2)), @j = @j + 1

 WHILE @i <= @s1_len
 BEGIN
  SELECT
   @s1_char = SUBSTRING(@s1, @i, 1),
   @c = @i,
   @cv0 = CAST(@i AS binary(2)),
   @j = 1

  WHILE @j <= @s2_len
  BEGIN
   SET @c = @c + 1
   SET @c_temp = CAST(SUBSTRING(@cv1, @j+@j-1, 2) AS int) +
    CASE WHEN @s1_char = SUBSTRING(@s2, @j, 1) THEN 0 ELSE 1 END
   IF @c > @c_temp SET @c = @c_temp
   SET @c_temp = CAST(SUBSTRING(@cv1, @j+@j+1, 2) AS int)+1
   IF @c > @c_temp SET @c = @c_temp
   SELECT @cv0 = @cv0 + CAST(@c AS binary(2)), @j = @j + 1
 END

 SELECT @cv1 = @cv0, @i = @i + 1
 END

 RETURN @c
END
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(Joseph Gama 开发的函数)

然后只需使用此查询来获取匹配项

SELECT A.Customer,
       b.ID,
       b.Customer
FROM #POTENTIALCUSTOMERS a
     LEFT JOIN #ExistingCustomers b ON dbo.ufn_levenshtein(REPLACE(A.Customer, ' ', ''), REPLACE(B.Customer, ' ', '')) < 5;
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创建该函数后完成脚本:

IF OBJECT_ID('tempdb..#ExistingCustomers') IS NOT NULL
    DROP TABLE #ExistingCustomers;

CREATE TABLE #ExistingCustomers
(Customer VARCHAR(255),
 ID       INT
);

INSERT INTO #ExistingCustomers
VALUES
('Ed''s Barbershop',
 1002
);

INSERT INTO #ExistingCustomers
VALUES
('GroceryTown',
 1003
);

INSERT INTO #ExistingCustomers
VALUES
('Candy Place',
 1004
);

INSERT INTO #ExistingCustomers
VALUES
('Handy Man',
 1005
);

IF OBJECT_ID('tempdb..#POTENTIALCUSTOMERS') IS NOT NULL
    DROP TABLE #POTENTIALCUSTOMERS;

CREATE TABLE #POTENTIALCUSTOMERS(Customer VARCHAR(255));

INSERT INTO #POTENTIALCUSTOMERS
VALUES('Eds Barbershop');

INSERT INTO #POTENTIALCUSTOMERS
VALUES('Grocery Town');

INSERT INTO #POTENTIALCUSTOMERS
VALUES('Candy Place');

INSERT INTO #POTENTIALCUSTOMERS
VALUES('Handee Man');

INSERT INTO #POTENTIALCUSTOMERS
VALUES('Beauty Salon');

INSERT INTO #POTENTIALCUSTOMERS
VALUES('The Apple Farm');

INSERT INTO #POTENTIALCUSTOMERS
VALUES('Igloo Ice Cream');

INSERT INTO #POTENTIALCUSTOMERS
VALUES('Ride-a-Long Bikes');

SELECT A.Customer,
       b.ID,
       b.Customer
FROM #POTENTIALCUSTOMERS a
     LEFT JOIN #ExistingCustomers b ON dbo.ufn_levenshtein(REPLACE(A.Customer, ' ', ''), REPLACE(B.Customer, ' ', '')) < 5;
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在这里,您可以在http://www.kodyaz.com/articles/fuzzy-string-matching-using-levenshtein-distance-sql-server.aspx找到 T-SQL 示例

  • 使用这个: SELECT A.Customer, b.ID, b.Customer, dbo.ufn_levenshtein(REPLACE(A.Customer, ' ', ''), REPLACE(B.Customer, ' ', '')) FROM #POTENTIALCUSTOMERS a LEFT JOIN #ExistingCustomers b ON dbo.ufn_levenshtein(REPLACE(A.Customer, ' ', ''), REPLACE(B.Customer, ' ', '')) &lt; 5; (2认同)

iam*_*ave 5

尝试在 SQL 中执行此操作将是一项持续的挑战,而且您不太可能获胜。您可以通过删除非 az 或 0-9 字符或尝试诸如SoundexMetaphone匹配或Levenshtein Distance之类的方法来走得很远,但总会有另一种边缘情况您在所有替换、通配符、语音化中都没有发现或简单的捏造。

如果您确实找到了足够准确的东西,那么您就会遇到性能问题。

简而言之,您最大的希望是沿着 SQLCLR 路线前进,并在此过程中学习大量 C#,或者根本不麻烦,只需从源头清理数据或创建一个需要不断维护的“干净”名称查找表随着新变种的出现。