blocks|key|341460|text|我将使用SQL+Server全文索引，它将允许您进行搜索并返回不仅包含单词而且可能有拼写错误的内容。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|341461|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I would use SQL Server Full Text Indexing, which will allow you to do searches and return things that not only contain the word but also may have a misspelling.

blocks|key|3323504|text|可以使用SQL+Server中的SOUNDEX和related+DIFFERENCE函数查找相似的名称。MSDN上的引用是here。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3323505|entityMap|0|LINK|mutability|MUTABLE|url|http://msdn.microsoft.com/en-us/library/ms187384.aspx^0|1P|4|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

You can use the SOUNDEX and related DIFFERENCE function in SQL Server to find similar names. The reference on MSDN is <a href="http://msdn.microsoft.com/en-us/library/ms187384.aspx" rel="nofollow noreferrer">here</a>.

blocks|key|343873|text|除了这里的其他好信息之外，你可能想要考虑使用通常被认为比SOUNDEX更好的Double+Metaphone语音算法。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|343874|Tim+Pfeiffer在他的文章Double+Metaphone+Sounds+Great+Convert+the+C%2B%2B+Double+Metaphone+algorithm+to+T-SQL+(最初是SQL+Mag，然后是SQL+Server+Pro)中详细介绍了一个使用SQL语言的实现。|343875|这将有助于匹配有轻微拼写错误的名称，例如，Carl+vs.+Karl。|343876|更新：实际的可下载代码似乎已经消失了，但似乎克隆了原始代码的here's+an+implementation+found+on+a+github+repo|style|BOLD|343877|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Soundex|1|http://en.wikipedia.org/wiki/Double_Metaphone|2|https://www.itprotoday.com/software-development/double-metaphone-sounds-great|3|https://web.archive.org/web/20091207025605/http://www.sqlmag.com:80/Articles/ArticleID/26094/pg/1/1.html|4|https://web.archive.org/web/20130725060153/http://sqlmag.com:80/t-sql/double-metaphone-sounds-great|5|https://github.com/mb16/geocoderNet/blob/master/build/sql/doubleMetaphone.sql^0|S|7|0|12|G|1|0|H|29|2|2V|7|3|36|E|4|0|0|0|2|U|1B|5|0^^$0|@$1|2|3|4|5|6|7|13|8|@]|9|@$A|14|B|15|1|16]|$A|17|B|18|1|19]]|C|$]]|$1|D|3|E|5|6|7|1A|8|@]|9|@$A|1B|B|1C|1|1D]|$A|1E|B|1F|1|1G]|$A|1H|B|1I|1|1J]]|C|$]]|$1|F|3|G|5|6|7|1K|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|1L|8|@$A|1M|B|1N|J|K]]|9|@$A|1O|B|1P|1|1Q]]|C|$]]|$1|L|3|-4|5|6|7|1R|8|@]|9|@]|C|$]]]|M|$N|$5|O|P|Q|C|$R|S]]|T|$5|O|P|Q|C|$R|U]]|V|$5|O|P|Q|C|$R|W]]|X|$5|O|P|Q|C|$R|Y]]|Z|$5|O|P|Q|C|$R|10]]|11|$5|O|P|Q|C|$R|12]]]]

In addition to the other good info here, you might want to consider using the <a href="http://en.wikipedia.org/wiki/Double_Metaphone" rel="nofollow noreferrer">Double Metaphone</a> phonetic algorithm which is generally considered to be better than <a href="http://en.wikipedia.org/wiki/Soundex" rel="nofollow noreferrer">SOUNDEX</a>. 

Tim Pfeiffer details an implementation in SQL in his article <a href="https://www.itprotoday.com/software-development/double-metaphone-sounds-great" rel="nofollow noreferrer">Double Metaphone Sounds Great Convert the C++ Double Metaphone algorithm to T-SQL</a> (originally in <a href="https://web.archive.org/web/20091207025605/http://www.sqlmag.com:80/Articles/ArticleID/26094/pg/1/1.html" rel="nofollow noreferrer" title="ARCHIVE of http:&#47;&#47;www.sqlmag.com:80/Articles/ArticleID/26094/pg/1/1.html">SQL Mag</a> &amp; then in <a href="https://web.archive.org/web/20130725060153/http://sqlmag.com:80/t-sql/double-metaphone-sounds-great" rel="nofollow noreferrer" title="ARCHIVE of http:&#47;&#47;sqlmag.com/t-sql/double-metaphone-sounds-great">SQL Server Pro</a>). 

That will assist in matching names with slight misspellings, e.g., Carl vs. Karl.

Update: The actual downloadable code seems to be gone, but <a href="https://github.com/mb16/geocoderNet/blob/master/build/sql/doubleMetaphone.sql" rel="nofollow noreferrer">here's an implementation found on a github repo</a> that appears to have cloned the original code

blocks|key|3323590|text|关于去重的事情，你的字符串拆分和匹配是很好的第一次剪切。如果有关于数据的已知项目可以用来减少工作负载和/或产生更好的结果，那么利用它们总是很好的。请记住，通常对于去重，完全消除手动工作是不可能的，尽管您可以通过自动捕获尽可能多的内容，然后生成“不确定性案例”的报告来使这一点变得更容易。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3323591|关于名称匹配:+SOUNDEX对于匹配的质量是很糟糕的，特别是对于你正在尝试做的工作类型来说，因为它会匹配离目标太远的东西。最好结合使用双变音位结果和Levenshtein距离来执行名称匹配。有了适当的偏置，这真的工作得很好，并且可能在清理你的已知知识后用于第二次传递。|3323592|您可能还希望考虑使用SSIS包，并研究模糊查找和分组转换(http://msdn.microsoft.com/en-us/library/ms345128(SQL.90).aspx)。|offset|length|3323593|也可以使用SQL+Full-Text+Search+(http://msdn.microsoft.com/en-us/library/cc879300.aspx)，但可能不适合您的特定问题域。|3323594|entityMap|0|LINK|mutability|MUTABLE|url|http://msdn.microsoft.com/en-us/library/ms345128(SQL.90).aspx|1|http://msdn.microsoft.com/en-us/library/cc879300.aspx^0|0|0|T|1P|0|0|R|1H|1|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|V|8|@]|9|@$F|W|G|X|1|Y]]|A|$]]|$1|H|3|I|5|6|7|Z|8|@]|9|@$F|10|G|11|1|12]]|A|$]]|$1|J|3|-4|5|6|7|13|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]|R|$5|M|N|O|A|$P|S]]]]

Regarding de-duping things your string split and match is great first cut. If there are known items about the data that can be leveraged to reduce workload and/or produce better results, it is always good to take advantage of them. Bear in mind that often for de-duping it is impossible to entirely eliminate manual work, although you can make that much easier by catching as much as you can automatically and then generating reports of your "uncertainty cases."

Regarding name matching: SOUNDEX is horrible for quality of matching and especially bad for the type of work you are trying to do as it will match things that are too far from the target. It's better to use a combination of double metaphone results and the Levenshtein distance to perform name matching. With appropriate biasing this works really well and could probably be used for a second pass after doing a cleanup on your knowns.

You may also want to consider using an SSIS package and looking into Fuzzy Lookup and Grouping transformations (http://msdn.microsoft.com/en-us/library/ms345128(SQL.90).aspx).

Using SQL Full-Text Search (http://msdn.microsoft.com/en-us/library/cc879300.aspx) is a possibility as well, but is likely not appropriate to your specific problem domain.

blocks|key|340397|text|我个人使用的是Jaro-Winkler算法的CLR实现，它似乎工作得很好-它在处理长度超过15个字符的字符串方面有点困难，不喜欢匹配电子邮件地址，但其他方面都很好-完整的实现指南可以在here中找到|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|340398|如果由于某种原因无法使用CLR函数，也许可以尝试通过SSIS包运行数据(使用模糊转换查找)-详细的here|340399|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Jaro-Winkler|1|http://isolvable.blogspot.com/2011/05/jaro-winkler-fast-fuzzy-linkage.html|2|http://www.sqlservercentral.com/articles/Integration%2BServices%2B%2528SSIS%2529/65616/^0|7|C|0|2K|4|1|0|1D|4|2|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@$A|S|B|T|1|U]|$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|6|7|Y|8|@]|9|@$A|Z|B|10|1|11]]|C|$]]|$1|F|3|-4|5|6|7|12|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]|P|$5|I|J|K|C|$L|Q]]]]

I personally use a CLR implementation of the <a href="http://en.wikipedia.org/wiki/Jaro-Winkler" rel="nofollow">Jaro-Winkler</a> algorithm which seems to work pretty well - it struggles a bit with strings longer than about 15 characters and doesn't like matching email addresses but otherwise is quite good - full implementation guide can be found <a href="http://isolvable.blogspot.com/2011/05/jaro-winkler-fast-fuzzy-linkage.html" rel="nofollow">here</a>

If you are unable to use CLR functions for whatever reasons, maybe you could try running the data through an SSIS package (using the fuzzy transformation lookup) - detailed <a href="http://www.sqlservercentral.com/articles/Integration+Services+%28SSIS%29/65616/" rel="nofollow">here</a>

blocks|key|343911|text|从Master+Data+Services的第一个版本开始，您就可以使用比SOUNDEX实现的更高级的模糊逻辑算法。因此，假设您已经安装了MDS，您将能够在mdq模式(MDS数据库)中找到一个名为Similarity()的函数。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|343912|有关其工作原理的更多信息，请访问：http://blog.hoegaerden.be/2011/02/05/finding-similar-strings-with-fuzzy-logic-functions-built-into-mds/|343913|entityMap|0|LINK|mutability|MUTABLE|url|http://blog.hoegaerden.be/2011/02/05/finding-similar-strings-with-fuzzy-logic-functions-built-into-mds/^0|26|3|2Q|C|0|H|2V|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@$9|Q|A|R|B|C]|$9|S|A|T|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|U|8|@]|D|@$9|V|A|W|1|X]]|E|$]]|$1|H|3|-4|5|6|7|Y|8|@]|D|@]|E|$]]]|I|$J|$5|K|L|M|E|$N|O]]]]

Since the first release of Master Data Services, you've got access to more advanced fuzzy logic algorithms than what SOUNDEX implements. So provided that you've got MDS installed, you'll be able to find a function called Similarity() in the mdq schema (MDS database).

More info on how it works: <a href="http://blog.hoegaerden.be/2011/02/05/finding-similar-strings-with-fuzzy-logic-functions-built-into-mds/">http://blog.hoegaerden.be/2011/02/05/finding-similar-strings-with-fuzzy-logic-functions-built-into-mds/</a>

blocks|key|340437|text|我发现SQL+Server提供给您进行模糊匹配的东西相当笨拙。我使用自己的CLR函数，使用Levenshtein距离算法和一些权重，真的很幸运。使用该算法，我创建了一个名为GetSimilarityScore的UDF，它接受两个字符串并返回一个介于0.0和1.0之间的分数。匹配度越接近1.0越好。然后，使用>=0.8等阈值进行查询，以获得最可能的匹配。如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|340438|if+object_id('tempdb..#similar')+is+not+null+drop+table+#similar
select+a.id,+(
++++select+top+1+x.id
+++from+MyTable+x
+++where+x.id+<>+a.id
+++order+by+dbo.GetSimilarityScore(a.MyField,+x.MyField)+desc
)+as+MostSimilarId
into+#similar
from+MyTable+a

select+*,+dbo.GetSimilarityScore(a.MyField,+c.MyField)
from+MyTable+a
join+#similar+b+on+a.id+=+b.id
join+MyTable+c+on+b.MostSimilarId+=+c.id|code-block|syntax|javascript|340439|只是不要在很大的桌子上这样做。这是一个缓慢的过程。|340440|下面是CLR+UDF：|340441|'''+<summary>
'''+Compute+the+distance+between+two+strings.
'''+</summary>
'''+<param+name="s1">The+first+of+the+two+strings.</param>
'''+<param+name="s2">The+second+of+the+two+strings.</param>
'''+<returns>The+Levenshtein+cost.</returns>
<Microsoft.SqlServer.Server.SqlFunction()>+_
Public+Shared+Function+ComputeLevenstheinDistance(ByVal+string1+As+SqlString,+ByVal+string2+As+SqlString)+As+SqlInt32
++++If+string1.IsNull+OrElse+string2.IsNull+Then+Return+SqlInt32.Null
++++Dim+s1+As+String+=+string1.Value
++++Dim+s2+As+String+=+string2.Value

++++Dim+n+As+Integer+=+s1.Length
++++Dim+m+As+Integer+=+s2.Length
++++Dim+d+As+Integer(,)+=+New+Integer(n,+m)+{}

++++'+Step+1
++++If+n+=+0+Then+Return+m
++++If+m+=+0+Then+Return+n

++++'+Step+2
++++For+i+As+Integer+=+0+To+n
++++++++d(i,+0)+=+i
++++Next

++++For+j+As+Integer+=+0+To+m
++++++++d(0,+j)+=+j
++++Next

++++'+Step+3
++++For+i+As+Integer+=+1+To+n
++++++++'Step+4
++++++++For+j+As+Integer+=+1+To+m
++++++++++++'+Step+5
++++++++++++Dim+cost+As+Integer+=+If((s2(j+-+1)+=+s1(i+-+1)),+0,+1)

++++++++++++'+Step+6
++++++++++++d(i,+j)+=+Math.Min(Math.Min(d(i+-+1,+j)+%2B+1,+d(i,+j+-+1)+%2B+1),+d(i+-+1,+j+-+1)+%2B+cost)
++++++++Next
++++Next
++++'+Step+7
++++Return+d(n,+m)
End+Function

'''+<summary>
'''+Returns+a+score+between+0.0-1.0+indicating+how+closely+two+strings+match.++1.0+is+a+100%25
'''+T-SQL+equality+match,+and+the+score+goes+down+from+there+towards+0.0+for+less+similar+strings.
'''+</summary>
<Microsoft.SqlServer.Server.SqlFunction()>+_
Public+Shared+Function+GetSimilarityScore(string1+As+SqlString,+string2+As+SqlString)+As+SqlDouble
++++If+string1.IsNull+OrElse+string2.IsNull+Then+Return+SqlInt32.Null

++++Dim+s1+As+String+=+string1.Value.ToUpper().TrimEnd("+"c)
++++Dim+s2+As+String+=+string2.Value.ToUpper().TrimEnd("+"c)
++++If+s1+=+s2+Then+Return+1.0F+'+At+this+point,+T-SQL+would+consider+them+the+same,+so+I+will+too

++++Dim+flatLevScore+As+Double+=+InternalGetSimilarityScore(s1,+s2)

++++Dim+letterS1+As+String+=+GetLetterSimilarityString(s1)
++++Dim+letterS2+As+String+=+GetLetterSimilarityString(s2)
++++Dim+letterScore+As+Double+=+InternalGetSimilarityScore(letterS1,+letterS2)

++++'Dim+wordS1+As+String+=+GetWordSimilarityString(s1)
++++'Dim+wordS2+As+String+=+GetWordSimilarityString(s2)
++++'Dim+wordScore+As+Double+=+InternalGetSimilarityScore(wordS1,+wordS2)

++++If+flatLevScore+=+1.0F+AndAlso+letterScore+=+1.0F+Then+Return+1.0F
++++If+flatLevScore+=+0.0F+AndAlso+letterScore+=+0.0F+Then+Return+0.0F

++++'+Return+weighted+result
++++Return+(flatLevScore+*+0.2F)+%2B+(letterScore+*+0.8F)
End+Function

Private+Shared+Function+InternalGetSimilarityScore(s1+As+String,+s2+As+String)+As+Double
++++Dim+dist+As+SqlInt32+=+ComputeLevenstheinDistance(s1,+s2)
++++Dim+maxLen+As+Integer+=+If(s1.Length+>+s2.Length,+s1.Length,+s2.Length)
++++If+maxLen+=+0+Then+Return+1.0F
++++Return+1.0F+-+Convert.ToDouble(dist.Value)+/+Convert.ToDouble(maxLen)
End+Function

'''+<summary>
'''+Sorts+all+the+alpha+numeric+characters+in+the+string+in+alphabetical+order
'''+and+removes+everything+else.
'''+</summary>
Private+Shared+Function+GetLetterSimilarityString(s1+As+String)+As+String
++++Dim+allChars+=+If(s1,+"").ToUpper().ToCharArray()
++++Array.Sort(allChars)
++++Dim+result+As+New+StringBuilder()
++++For+Each+ch+As+Char+In+allChars
++++++++If+Char.IsLetterOrDigit(ch)+Then
++++++++++++result.Append(ch)
++++++++End+If
++++Next
++++Return+result.ToString()
End+Function

'''+<summary>
'''+Removes+all+non-alpha+numeric+characters+and+then+sorts
'''+the+words+in+alphabetical+order.
'''+</summary>
Private+Shared+Function+GetWordSimilarityString(s1+As+String)+As+String
++++Dim+words+As+New+List(Of+String)()
++++Dim+curWord+As+StringBuilder+=+Nothing
++++For+Each+ch+As+Char+In+If(s1,+"").ToUpper()
++++++++If+Char.IsLetterOrDigit(ch)+Then
++++++++++++If+curWord+Is+Nothing+Then
++++++++++++++++curWord+=+New+StringBuilder()
++++++++++++End+If
++++++++++++curWord.Append(ch)
++++++++Else
++++++++++++If+curWord+IsNot+Nothing+Then
++++++++++++++++words.Add(curWord.ToString())
++++++++++++++++curWord+=+Nothing
++++++++++++End+If
++++++++End+If
++++Next
++++If+curWord+IsNot+Nothing+Then
++++++++words.Add(curWord.ToString())
++++End+If

++++words.Sort(StringComparer.OrdinalIgnoreCase)
++++Return+String.Join("+",+words.ToArray())
End+Function|340442|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|D|7|S|8|@]|9|@]|A|$E|F]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

I've found that the stuff SQL Server gives you to do fuzzy matching is pretty clunky. I've had really good luck with my own CLR functions using the Levenshtein distance algorithm and some weighting. Using that algorithm, I've then made a UDF called GetSimilarityScore that takes two strings and returns a score between 0.0 and 1.0. The closer to 1.0 the match is, the better. Then, query with a threshold of >=0.8 or so to get the most likely matches. Something like this:

<pre><code>if object_id('tempdb..#similar') is not null drop table #similar
select a.id, (
 select top 1 x.id
 from MyTable x
 where x.id &lt;&gt; a.id
 order by dbo.GetSimilarityScore(a.MyField, x.MyField) desc
) as MostSimilarId
into #similar
from MyTable a

select *, dbo.GetSimilarityScore(a.MyField, c.MyField)
from MyTable a
join #similar b on a.id = b.id
join MyTable c on b.MostSimilarId = c.id
</code></pre>

Just don't do it with really large tables. It's a slow process.

Here's the CLR UDFs:

<pre><code>''' &lt;summary&gt;
''' Compute the distance between two strings.
''' &lt;/summary&gt;
''' &lt;param name="s1"&gt;The first of the two strings.&lt;/param&gt;
''' &lt;param name="s2"&gt;The second of the two strings.&lt;/param&gt;
''' &lt;returns&gt;The Levenshtein cost.&lt;/returns&gt;
&lt;Microsoft.SqlServer.Server.SqlFunction()&gt; _
Public Shared Function ComputeLevenstheinDistance(ByVal string1 As SqlString, ByVal string2 As SqlString) As SqlInt32
 If string1.IsNull OrElse string2.IsNull Then Return SqlInt32.Null
 Dim s1 As String = string1.Value
 Dim s2 As String = string2.Value

 Dim n As Integer = s1.Length
 Dim m As Integer = s2.Length
 Dim d As Integer(,) = New Integer(n, m) {}

 ' Step 1
 If n = 0 Then Return m
 If m = 0 Then Return n

 ' Step 2
 For i As Integer = 0 To n
 d(i, 0) = i
 Next

 For j As Integer = 0 To m
 d(0, j) = j
 Next

 ' Step 3
 For i As Integer = 1 To n
 'Step 4
 For j As Integer = 1 To m
 ' Step 5
 Dim cost As Integer = If((s2(j - 1) = s1(i - 1)), 0, 1)

 ' Step 6
 d(i, j) = Math.Min(Math.Min(d(i - 1, j) + 1, d(i, j - 1) + 1), d(i - 1, j - 1) + cost)
 Next
 Next
 ' Step 7
 Return d(n, m)
End Function

''' &lt;summary&gt;
''' Returns a score between 0.0-1.0 indicating how closely two strings match. 1.0 is a 100%
''' T-SQL equality match, and the score goes down from there towards 0.0 for less similar strings.
''' &lt;/summary&gt;
&lt;Microsoft.SqlServer.Server.SqlFunction()&gt; _
Public Shared Function GetSimilarityScore(string1 As SqlString, string2 As SqlString) As SqlDouble
 If string1.IsNull OrElse string2.IsNull Then Return SqlInt32.Null

 Dim s1 As String = string1.Value.ToUpper().TrimEnd(" "c)
 Dim s2 As String = string2.Value.ToUpper().TrimEnd(" "c)
 If s1 = s2 Then Return 1.0F ' At this point, T-SQL would consider them the same, so I will too

 Dim flatLevScore As Double = InternalGetSimilarityScore(s1, s2)

 Dim letterS1 As String = GetLetterSimilarityString(s1)
 Dim letterS2 As String = GetLetterSimilarityString(s2)
 Dim letterScore As Double = InternalGetSimilarityScore(letterS1, letterS2)

 'Dim wordS1 As String = GetWordSimilarityString(s1)
 'Dim wordS2 As String = GetWordSimilarityString(s2)
 'Dim wordScore As Double = InternalGetSimilarityScore(wordS1, wordS2)

 If flatLevScore = 1.0F AndAlso letterScore = 1.0F Then Return 1.0F
 If flatLevScore = 0.0F AndAlso letterScore = 0.0F Then Return 0.0F

 ' Return weighted result
 Return (flatLevScore * 0.2F) + (letterScore * 0.8F)
End Function

Private Shared Function InternalGetSimilarityScore(s1 As String, s2 As String) As Double
 Dim dist As SqlInt32 = ComputeLevenstheinDistance(s1, s2)
 Dim maxLen As Integer = If(s1.Length &gt; s2.Length, s1.Length, s2.Length)
 If maxLen = 0 Then Return 1.0F
 Return 1.0F - Convert.ToDouble(dist.Value) / Convert.ToDouble(maxLen)
End Function

''' &lt;summary&gt;
''' Sorts all the alpha numeric characters in the string in alphabetical order
''' and removes everything else.
''' &lt;/summary&gt;
Private Shared Function GetLetterSimilarityString(s1 As String) As String
 Dim allChars = If(s1, "").ToUpper().ToCharArray()
 Array.Sort(allChars)
 Dim result As New StringBuilder()
 For Each ch As Char In allChars
 If Char.IsLetterOrDigit(ch) Then
 result.Append(ch)
 End If
 Next
 Return result.ToString()
End Function

''' &lt;summary&gt;
''' Removes all non-alpha numeric characters and then sorts
''' the words in alphabetical order.
''' &lt;/summary&gt;
Private Shared Function GetWordSimilarityString(s1 As String) As String
 Dim words As New List(Of String)()
 Dim curWord As StringBuilder = Nothing
 For Each ch As Char In If(s1, "").ToUpper()
 If Char.IsLetterOrDigit(ch) Then
 If curWord Is Nothing Then
 curWord = New StringBuilder()
 End If
 curWord.Append(ch)
 Else
 If curWord IsNot Nothing Then
 words.Add(curWord.ToString())
 curWord = Nothing
 End If
 End If
 Next
 If curWord IsNot Nothing Then
 words.Add(curWord.ToString())
 End If

 words.Sort(StringComparer.OrdinalIgnoreCase)
 Return String.Join(" ", words.ToArray())
End Function
</code></pre>

blocks|key|341567|text|这样做吧|type|unstyled|depth|inlineStyleRanges|entityRanges|data|341568|+++++++++create+table+person(
+++++++++personid+int+identity(1,1)+primary+key,
+++++++++firstname+varchar(20),
+++++++++lastname+varchar(20),
+++++++++addressindex+int,
+++++++++sound+varchar(10)
+++++++++)|code-block|syntax|javascript|341569|然后创建一个触发器|341570|+++++++++create+trigger+trigoninsert+for+dbo.person
+++++++++on+insert+
+++++++++as
+++++++++declare+@personid+int;
+++++++++select+@personid=personid+from+inserted;
+++++++++update+person
+++++++++set+sound=soundex(firstname)+where+personid=@personid;|341571|现在我能做的就是创建一个类似下面这样的过程|341572|+++++++++create+procedure+getfuzzi(@personid+int)
++++++++++as
+++++++++declare+@sound+varchar(10);
+++++++++set+@sound=(select+sound+from+person+where+personid=@personid;
+++++++++select+personid,firstname,lastname,addressindex+from+person
+++++++++where+sound=@sound|341573|这将返回与为特定personid提供的名称几乎匹配的所有名称|341574|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|W|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|Y|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

do it this way

<pre><code> create table person(
 personid int identity(1,1) primary key,
 firstname varchar(20),
 lastname varchar(20),
 addressindex int,
 sound varchar(10)
 )
</code></pre>

and later on create a trigger

<pre><code> create trigger trigoninsert for dbo.person
 on insert 
 as
 declare @personid int;
 select @personid=personid from inserted;
 update person
 set sound=soundex(firstname) where personid=@personid;
</code></pre>

now what i can do is i can create a procedure which looks something like this

<pre><code> create procedure getfuzzi(@personid int)
 as
 declare @sound varchar(10);
 set @sound=(select sound from person where personid=@personid;
 select personid,firstname,lastname,addressindex from person
 where sound=@sound
</code></pre>

this will return you all the names that are nearly in match with the names provided by for a particular personid

Pasting RedFilter code in two parts ,so as to avoid link rot

References: 
<a href="https://github.com/mb16/geocoderNet/blob/master/build/sql/doubleMetaphone.sql" rel="nofollow noreferrer">https://github.com/mb16/geocoderNet/blob/master/build/sql/doubleMetaphone.sql</a>
Part1:

<pre><code>--WEB LISTING 1: Double Metaphone Script

-------------------------------------
IF OBJECT_ID('fnIsVowel') IS NOT NULL BEGIN DROP FUNCTION fnIsVowel END
GO;
CREATE FUNCTION fnIsVowel( @c char(1) )
RETURNS bit
AS
BEGIN
 IF (@c = 'A') OR (@c = 'E') OR (@c = 'I') OR (@c = 'O') OR (@c = 'U') OR (@c = 'Y') 
 BEGIN
 RETURN 1
 END
 --'ELSE' would worry SQL Server, it wants RETURN last in a scalar function
 RETURN 0
END
GO;
-----------------------------------------------
IF OBJECT_ID('fnSlavoGermanic') IS NOT NULL BEGIN DROP FUNCTION fnSlavoGermanic 
END
GO;
CREATE FUNCTION fnSlavoGermanic( @Word char(50) )
RETURNS bit
AS
BEGIN
 --Catch NULL also...
 IF (CHARINDEX('W',@Word) &gt; 0) OR (CHARINDEX('K',@Word) &gt; 0) OR 
(CHARINDEX('CZ',@Word) &gt; 0)
 --'WITZ' test is in original Lawrence Philips C++ code, but appears to be a subset of the first test for 'W'
 -- OR (CHARINDEX('WITZ',@Word) &gt; 0)
 BEGIN
 RETURN 1
 END
 --ELSE
 RETURN 0
END
GO;
---------------------------------------------------------------------------------------------------------------------------------
----------------------
--Lawrence Philips calls for a length argument, but this has two drawbacks:
--1. All target strings must be of the same length
--2. It presents an opportunity for subtle bugs, ie fnStringAt( 1, 7, 'Search me please', 'Search' ) returns 0 (no matter what is in the searched string)
--So I've eliminated the argument and fnStringAt checks the length of each target as it executes

--DEFAULTS suck with UDFs. Have to specify DEFAULT in caller - why bother?
IF OBJECT_ID('fnStringAtDef') IS NOT NULL BEGIN DROP FUNCTION fnStringAtDef END
GO;
CREATE FUNCTION fnStringAtDef( @Start int, @StringToSearch varchar(50), 
 @Target1 varchar(50), 
 @Target2 varchar(50) = NULL,
 @Target3 varchar(50) = NULL,
 @Target4 varchar(50) = NULL,
 @Target5 varchar(50) = NULL,
 @Target6 varchar(50) = NULL )
RETURNS bit
AS
BEGIN
 IF CHARINDEX(@Target1,@StringToSearch,@Start) &gt; 0 RETURN 1
 --2 Styles, test each optional argument for NULL, nesting further tests
 --or just take advantage of CHARINDEX behavior with a NULL arg (unless 65 compatibility - code check before CREATE FUNCTION?
 --Style 1:
 --IF @Target2 IS NOT NULL
 --BEGIN
 -- IF CHARINDEX(@Target2,@StringToSearch,@Start) &gt; 0 RETURN 1
 -- (etc.)
 --END
 --Style 2:
 IF CHARINDEX(@Target2,@StringToSearch,@Start) &gt; 0 RETURN 1
 IF CHARINDEX(@Target3,@StringToSearch,@Start) &gt; 0 RETURN 1
 IF CHARINDEX(@Target4,@StringToSearch,@Start) &gt; 0 RETURN 1
 IF CHARINDEX(@Target5,@StringToSearch,@Start) &gt; 0 RETURN 1
 IF CHARINDEX(@Target6,@StringToSearch,@Start) &gt; 0 RETURN 1
 RETURN 0
END
GO;
-------------------------------------------------------------------------------------------------
IF OBJECT_ID('fnStringAt') IS NOT NULL BEGIN DROP FUNCTION fnStringAt END
GO;
CREATE FUNCTION fnStringAt( @Start int, @StringToSearch varchar(50), @TargetStrings 
varchar(2000) )
RETURNS bit
AS
BEGIN
 DECLARE @SingleTarget varchar(50)
 DECLARE @CurrentStart int
 DECLARE @CurrentLength int

 --Eliminate special cases
 --Trailing space is needed to check for end of word in some cases, so always append comma
 --loop tests should fairly quickly ignore ',,' termination
 SET @TargetStrings = @TargetStrings + ','

 SET @CurrentStart = 1
 --Include terminating comma so spaces don't get truncated
 SET @CurrentLength = (CHARINDEX(',',@TargetStrings,@CurrentStart) - 
@CurrentStart) + 1
 SET @SingleTarget = SUBSTRING(@TargetStrings,@CurrentStart,@CurrentLength)
 WHILE LEN(@SingleTarget) &gt; 1
 BEGIN
 IF SUBSTRING(@StringToSearch,@Start,LEN(@SingleTarget)-1) = 
LEFT(@SingleTarget,LEN(@SingleTarget)-1)
 BEGIN
 RETURN 1
 END
 SET @CurrentStart = (@CurrentStart + @CurrentLength)
 SET @CurrentLength = (CHARINDEX(',',@TargetStrings,@CurrentStart) - 
@CurrentStart) + 1
 IF NOT @CurrentLength &gt; 1 --getting trailing comma 
 BEGIN
 BREAK
 END
 SET @SingleTarget = 
SUBSTRING(@TargetStrings,@CurrentStart,@CurrentLength)
 END
 RETURN 0
END
GO;
------------------------------------------------------------------------
IF OBJECT_ID('fnDoubleMetaphoneTable') IS NOT NULL BEGIN DROP FUNCTION 
fnDoubleMetaphoneTable END
GO;
CREATE FUNCTION fnDoubleMetaphoneTable( @Word varchar(50) )
RETURNS @DMP TABLE ( Metaphone1 char(4), Metaphone2 char(4) )
AS
BEGIN
 DECLARE @MP1 varchar(4), @MP2 varchar(4)
 SET @MP1 = ''
 SET @MP2 = ''
 DECLARE @CurrentPosition int, @WordLength int, @CurrentChar char(1)
 SET @CurrentPosition = 1
 SET @WordLength = LEN(@Word)

 IF @WordLength &lt; 1 
 BEGIN
 RETURN
 END

 --ensure case insensitivity
 SET @Word = UPPER(@Word)

 IF dbo.fnStringAt(1, @Word, 'GN,KN,PN,WR,PS') = 1 
 BEGIN
 SET @CurrentPosition = @CurrentPosition + 1
 END

 IF 'X' = LEFT(@Word,1)
 BEGIN
 SET @MP1 = @MP1 + 'S'
 SET @MP2 = @MP2 + 'S'
 SET @CurrentPosition = @CurrentPosition + 1
 END

 WHILE (4 &gt; LEN(RTRIM(@MP1))) OR (4 &gt; LEN(RTRIM(@MP2)))
 BEGIN
 IF @CurrentPosition &gt; @WordLength 
 BEGIN
 BREAK
 END

 SET @CurrentChar = SUBSTRING(@Word,@CurrentPosition,1)

 IF @CurrentChar IN('A','E','I','O','U','Y')
 BEGIN
 IF @CurrentPosition = 1 
 BEGIN
 SET @MP1 = @MP1 + 'A'
 SET @MP2 = @MP2 + 'A'
 END
 SET @CurrentPosition = @CurrentPosition + 1
 END
 ELSE IF @CurrentChar = 'B'
 BEGIN
 SET @MP1 = @MP1 + 'P'
 SET @MP2 = @MP2 + 'P'
 IF 'B' = SUBSTRING(@Word,@CurrentPosition + 1,1) 
 BEGIN
 SET @CurrentPosition = @CurrentPosition + 2
 END
 ELSE 
 BEGIN
 SET @CurrentPosition = @CurrentPosition + 1
 END
 END
 ELSE IF @CurrentChar = 'Ç'
 BEGIN
 SET @MP1 = @MP1 + 'S'
 SET @MP2 = @MP2 + 'S'
 SET @CurrentPosition = @CurrentPosition + 1
 END
 ELSE IF @CurrentChar = 'C'
 BEGIN
 --various germanic
 IF (@CurrentPosition &gt; 2) 
 AND (dbo.fnIsVowel(SUBSTRING(@Word,@CurrentPosition-2,1))=0) 
 AND (dbo.fnStringAt(@CurrentPosition-1,@Word,'ACH') = 1) 
 AND ((SUBSTRING(@Word,@CurrentPosition+2,1) &lt;&gt; 'I') 
 AND ((SUBSTRING(@Word,@CurrentPosition+2,1) &lt;&gt; 'E') OR 
(dbo.fnStringAt(@CurrentPosition-2,@Word,'BACHER,MACHER')=1)))
 BEGIN
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'
 SET @CurrentPosition = @CurrentPosition + 2
 END
 -- 'caesar'
 ELSE IF (@CurrentPosition = 1) AND 
(dbo.fnStringAt(@CurrentPosition,@Word,'CAESAR') = 1)
 BEGIN
 SET @MP1 = @MP1 + 'S'
 SET @MP2 = @MP2 + 'S'
 SET @CurrentPosition = @CurrentPosition + 2
 END
 -- 'chianti'
 ELSE IF dbo.fnStringAt(@CurrentPosition,@Word,'CHIA') = 1
 BEGIN
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'
 SET @CurrentPosition = @CurrentPosition + 2
 END
 ELSE IF dbo.fnStringAt(@CurrentPosition,@Word,'CH') = 1
 BEGIN
 -- Find 'michael'
 IF (@CurrentPosition &gt; 1) AND 
(dbo.fnStringAt(@CurrentPosition,@Word,'CHAE') = 1)
 BEGIN
 --First instance of alternate encoding
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'X'
 SET @CurrentPosition = @CurrentPosition + 2
 END
 --greek roots e.g. 'chemistry', 'chorus'
 ELSE IF (@CurrentPosition = 1) AND (dbo.fnStringAt(2, @Word, 
'HARAC,HARIS,HOR,HYM,HIA,HEM') = 1) AND (dbo.fnStringAt(1,@Word,'CHORE') = 0)
 BEGIN
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'
 SET @CurrentPosition = @CurrentPosition + 2
 END
 --germanic, greek, or otherwise 'ch' for 'kh' sound
 ELSE IF ((dbo.fnStringAt(1,@Word,'VAN ,VON ,SCH')=1) OR 
 (dbo.fnStringAt(@CurrentPosition-
2,@Word,'ORCHES,ARCHIT,ORCHID')=1) OR 
 (dbo.fnStringAt(@CurrentPosition+2,@Word,'T,S')=1) OR 
 (((dbo.fnStringAt(@CurrentPosition-1,@Word,'A,O,U,E')=1) 
OR 
 (@CurrentPosition = 1)) 
 AND 
(dbo.fnStringAt(@CurrentPosition+2,@Word,'L,R,N,M,B,H,F,V,W, ')=1)))
 BEGIN
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'
 SET @CurrentPosition = @CurrentPosition + 2
 END
 ELSE
 BEGIN
 --is this a given?
 IF (@CurrentPosition &gt; 1) 
 BEGIN
 IF (dbo.fnStringAt(1,@Word,'MC') = 1)
 BEGIN
 --eg McHugh
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'
 END
 ELSE
 BEGIN
 --Alternate encoding
 SET @MP1 = @MP1 + 'X'
 SET @MP2 = @MP2 + 'K'
 END
 END
 ELSE
 BEGIN
 SET @MP1 = @MP1 + 'X'
 SET @MP2 = @MP2 + 'X'
 END
 SET @CurrentPosition = @CurrentPosition + 2
 END
 END
 --e.g, 'czerny'
 ELSE IF (dbo.fnStringAt(@CurrentPosition,@Word,'CZ')=1) AND 
(dbo.fnStringAt((@CurrentPosition - 2),@Word,'WICZ')=0)
 BEGIN
 SET @MP1 = @MP1 + 'S'
 SET @MP2 = @MP2 + 'X'
 SET @CurrentPosition = @CurrentPosition + 2
 END

 --e.g., 'focaccia'
 ELSE IF(dbo.fnStringAt((@CurrentPosition + 1),@Word,'CIA')=1)
 BEGIN
 SET @MP1 = @MP1 + 'X'
 SET @MP2 = @MP2 + 'X'
 SET @CurrentPosition = @CurrentPosition + 3
 END

 --double 'C', but not if e.g. 'McClellan'
 ELSE IF(dbo.fnStringAt(@CurrentPosition,@Word,'CC')=1) AND NOT 
((@CurrentPosition = 2) AND (LEFT(@Word,1) = 'M'))
 --'bellocchio' but not 'bacchus'
 IF (dbo.fnStringAt((@CurrentPosition + 2),@Word,'I,E,H')=1) AND 
(dbo.fnStringAt((@CurrentPosition + 2),@Word,'HU')=0)
 BEGIN
 --'accident', 'accede' 'succeed'
 IF (((@CurrentPosition = 2) AND 
(SUBSTRING(@Word,@CurrentPosition - 1,1) = 'A')) 
 OR (dbo.fnStringAt((@CurrentPosition - 
1),@Word,'UCCEE,UCCES')=1))
 BEGIN
 SET @MP1 = @MP1 + 'KS'
 SET @MP2 = @MP2 + 'KS'
 END
 --'bacci', 'bertucci', other italian
 ELSE
 BEGIN
 SET @MP1 = @MP1 + 'X'
 SET @MP2 = @MP2 + 'X'
 END
 SET @CurrentPosition = @CurrentPosition + 3
 END
 --Pierce's rule
 ELSE 
 BEGIN
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'
 SET @CurrentPosition = @CurrentPosition + 2
 END

 ELSE IF (dbo.fnStringAt(@CurrentPosition,@Word,'CK,CG,CQ')=1)
 BEGIN
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'
 SET @CurrentPosition = @CurrentPosition + 2
 END

 ELSE IF (dbo.fnStringAt(@CurrentPosition,@Word,'CI,CE,CY')=1)
 BEGIN
 --italian vs. english
 IF (dbo.fnStringAt(@CurrentPosition,@Word,'CIO,CIE,CIA')=1)
 BEGIN
 SET @MP1 = @MP1 + 'S'
 SET @MP2 = @MP2 + 'X'
 END
 ELSE
 BEGIN
 SET @MP1 = @MP1 + 'S'
 SET @MP2 = @MP2 + 'S'
 END
 SET @CurrentPosition = @CurrentPosition + 2
 END

 ELSE
 BEGIN
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'

 --name sent in 'mac caffrey', 'mac gregor
 IF (dbo.fnStringAt((@CurrentPosition + 1),@Word,' C, Q, G')=1)
 BEGIN
 SET @CurrentPosition = @CurrentPosition + 3
 END
 ELSE
 BEGIN
 IF (dbo.fnStringAt((@CurrentPosition + 1),@Word,'C,K,Q')=1)
 AND (dbo.fnStringAt((@CurrentPosition + 1), 2, 'CE,CI')=0)
 BEGIN
 SET @CurrentPosition = @CurrentPosition + 2
 END
 ELSE
 BEGIN
 SET @CurrentPosition = @CurrentPosition + 1
 END
 END
 END

 END
 ELSE IF @CurrentChar = 'D'
 BEGIN
 IF (dbo.fnStringAt(@CurrentPosition, @Word, 'DG')=1)
 BEGIN
 IF (dbo.fnStringAt((@CurrentPosition + 2),@Word,'I,E,Y')=1)
 BEGIN
 --e.g. 'edge'
 SET @MP1 = @MP1 + 'J'
 SET @MP2 = @MP2 + 'J'
 SET @CurrentPosition = @CurrentPosition + 3
 END
 ELSE
 BEGIN
 --e.g. 'edgar'
 SET @MP1 = @MP1 + 'TK'
 SET @MP2 = @MP2 + 'TK'
 SET @CurrentPosition = @CurrentPosition + 2
 END
 END
 ELSE IF (dbo.fnStringAt(@CurrentPosition,@Word,'DT,DD')=1)
 BEGIN
 SET @MP1 = @MP1 + 'T'
 SET @MP2 = @MP2 + 'T'
 SET @CurrentPosition = @CurrentPosition + 2
 END
 ELSE
 BEGIN
 SET @MP1 = @MP1 + 'T'
 SET @MP2 = @MP2 + 'T'
 SET @CurrentPosition = @CurrentPosition + 1
 END
 END

 ELSE IF @CurrentChar = 'F'
 BEGIN
 IF (SUBSTRING(@Word,@CurrentPosition + 1,1) = 'F')
 BEGIN
 SET @CurrentPosition = @CurrentPosition + 2
 END
 ELSE
 BEGIN
 SET @CurrentPosition = @CurrentPosition + 1
 END
 SET @MP1 = @MP1 + 'F'
 SET @MP2 = @MP2 + 'F'
 END

 ELSE IF @CurrentChar = 'G'
 BEGIN
 IF (SUBSTRING(@Word,@CurrentPosition + 1,1) = 'H')
 BEGIN
 IF (@CurrentPosition &gt; 1) AND 
(dbo.fnIsVowel(SUBSTRING(@Word,@CurrentPosition - 1,1)) = 0)
 BEGIN
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'
 SET @CurrentPosition = @CurrentPosition + 2
 END
 --'ghislane', ghiradelli
 ELSE IF (@CurrentPosition = 1)
 BEGIN 
 IF (SUBSTRING(@Word,@CurrentPosition + 2,1) = 'I')
 BEGIN
 SET @MP1 = @MP1 + 'J'
 SET @MP2 = @MP2 + 'J'
 END
 ELSE
 BEGIN
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'
 END
 SET @CurrentPosition = @CurrentPosition + 2
 END
 --Parker's rule (with some further refinements) - e.g., 'hugh'
 ELSE IF (((@CurrentPosition &gt; 2) AND (dbo.fnStringAt((@CurrentPosition 
- 2),@Word,'B,H,D')=1) )
 --e.g., 'bough'
 OR ((@CurrentPosition &gt; 3) AND (dbo.fnStringAt((@CurrentPosition 
- 3),@Word,'B,H,D')=1) )
 --e.g., 'broughton'
 OR ((@CurrentPosition &gt; 4) AND (dbo.fnStringAt((@CurrentPosition 
- 4),@Word,'B,H')=1) ) )
 BEGIN
 SET @CurrentPosition = @CurrentPosition + 2
 END
 ELSE
 BEGIN
 --e.g., 'laugh', 'McLaughlin', 'cough', 'gough', 'rough', 'tough'
 IF ((@CurrentPosition &gt; 3) 
 AND (SUBSTRING(@Word,@CurrentPosition - 1,1) = 'U') 
 AND (dbo.fnStringAt((@CurrentPosition - 
3),@Word,'C,G,L,R,T')=1) )
 BEGIN
 SET @MP1 = @MP1 + 'F'
 SET @MP2 = @MP2 + 'F'
 END
 ELSE
 BEGIN
 IF ((@CurrentPosition &gt; 1) AND 
SUBSTRING(@Word,@CurrentPosition - 1,1) &lt;&gt; 'I')
 BEGIN
 SET @MP1 = @MP1 + 'K'
 SET @MP2 = @MP2 + 'K'
 END
 END

 SET @CurrentPosition = @CurrentPosition + 2
 END
 END

 ELSE IF (SUBSTRING(@Word,@CurrentPosition + 1,1) = 'N')
 BEGIN
 IF ((@CurrentPosition = 2) AND (dbo.fnIsVowel(LEFT(@Word,1))=1) AND 
(dbo.fnSlavoGermanic(@Word)=0))
 BEGIN
 SET @MP1 = @MP1 + 'KN'
 SET @MP2 = @MP2 + 'N'
 END
 ELSE
 BEGIN
 --not e.g. 'cagney'
 IF ((dbo.fnStringAt((@CurrentPosition + 2),@Word,'EY')=0) 
 AND (SUBSTRING(@Word,@CurrentPosition + 1,1) &lt;&gt; 
'Y') AND (dbo.fnSlavoGermanic(@Word)=0))
</code></pre>

I have a table Persons with personaldata and so on. There are lots of columns but the once of interest here are: <code>addressindex</code>, <code>lastname</code> and <code>firstname</code> where <code>addressindex</code> is a unique address drilled down to the door of the apartment.
So if I have 'like below' two persons with the <code>lastname</code> and one the <code>firstnames</code> are the same they are most likely duplicates.

I need a way to list these duplicates.

<pre><code>tabledata:

personid 1
firstname "Carl"
lastname "Anderson"
addressindex 1

personid 2
firstname "Carl Peter"
lastname "Anderson"
addressindex 1
</code></pre>

I know how do this if I were to match exactly on all columns but I need fuzzy match to do the trick with (from the above example) a result like:

<pre><code>Row personid addressindex lastname firstname
1 2 1 Anderson Carl Peter
2 1 1 Anderson Carl
.....
</code></pre>

Any hints on how to solve this in a good way?

Fuzzy matching using T-SQL

SQL Server

数据库

我有一个表Persons，里面有personaldata等等。这里有很多列，但这里最感兴趣的是：addressindex、lastname和firstname，其中addressindex是深入到公寓门的唯一地址。因此，如果我有两个lastname用户和一个firstnames用户是相同的，那么他们很可能是重复的。我需...

问基于T-SQL的模糊匹配
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