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!
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
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Soundex或Metaphone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。
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