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1. Microsoft Fuzzy Lookup Machine
2. Microsoft Fuzzy Lookup Add-in For Excel
3. Fuzzy Lookup For Mac
4. Microsoft Fuzzy Lookup Mac Serial
5. Microsoft Fuzzy Lookup Add In

Mar 09, 2017 Have you ever attempted to use VLOOKUP in Excel but been frustrated when it does not return any matches? Fuzzy Lookup is an Excel add-on that takes an input, searches for the best match it can find, and returns that best match along with a similarity rating. Apr 04, 2014 A brief tutorial on how to use Microsoft Excel Fuzzy Lookup add-on for Excel. Fuzzy Lookup in Excel from Microsoft Labs - Episode 2271 - Duration: 7:51. MrExcel.com 9,045 views.

APPLIES TO: SQL Server SSIS Integration Runtime in Azure Data Factory Azure Synapse Analytics (SQL DW)

The Fuzzy Lookup transformation performs data cleaning tasks such as standardizing data, correcting data, and providing missing values.

Note

The Fuzzy Lookup Add-In for Excel was developed by Microsoft Research and performs fuzzy matching of textual data in Microsoft Excel. It can be used to identify fuzzy duplicate rows within a single table or to fuzzy join similar rows between two different tables. Mar 09, 2017  Fuzzy Lookup is an Excel add-on that takes an input, searches for the best match it can find, and returns that best match along with a similarity rating. Have you ever attempted to use VLOOKUP in Excel but been frustrated when it does not return any matches? Fuzzy Lookup is an Excel add-on that takes an input, searches for the best match it can. May 22, 2017 This is a solid Excel tip that will help you clean up your data in minutes. Use an Excel Add-In to easily perform approximate string matching (i.e. Jan 11, 2019 Fix Fuzzy Windows 10 Text on High Resolution Displays. Won’t need to use the manual method for fixing everything that has fuzzy/blurry text. Microsoft is holding its Windows 10.

For more detailed information about the Fuzzy Lookup transformation, including performance and memory limitations, see the white paper, Fuzzy Lookup and Fuzzy Grouping in SQL Server Integration Services 2005.

The Fuzzy Lookup transformation differs from the Lookup transformation in its use of fuzzy matching. The Lookup transformation uses an equi-join to locate matching records in the reference table. It returns records with at least one matching record, and returns records with no matching records. In contrast, the Fuzzy Lookup transformation uses fuzzy matching to return one or more close matches in the reference table.

A Fuzzy Lookup transformation frequently follows a Lookup transformation in a package data flow. First, the Lookup transformation tries to find an exact match. If it fails, the Fuzzy Lookup transformation provides close matches from the reference table.

The transformation needs access to a reference data source that contains the values that are used to clean and extend the input data. The reference data source must be a table in a SQL Server database. The match between the value in an input column and the value in the reference table can be an exact match or a fuzzy match. However, the transformation requires at least one column match to be configured for fuzzy matching. If you want to use only exact matching, use the Lookup transformation instead.

This transformation has one input and one output.

Only input columns with the DT_WSTR and DT_STR data types can be used in fuzzy matching. Exact matching can use any DTS data type except DT_TEXT, DT_NTEXT, and DT_IMAGE. For more information, see Integration Services Data Types. Columns that participate in the join between the input and the reference table must have compatible data types. For example, it is valid to join a column with the DTS DT_WSTR data type to a column with the SQL Server nvarchar data type, but invalid to join a column with the DT_WSTR data type to a column with the int data type.

You can customize this transformation by specifying the maximum amount of memory, the row comparison algorithm, and the caching of indexes and reference tables that the transformation uses. New microsoft word mac multilevel lists free.

The amount of memory that the Fuzzy Lookup transformation uses can be configured by setting the MaxMemoryUsage custom property. You can specify the number of megabytes (MB), or use the value 0, which lets the transformation use a dynamic amount of memory based on its needs and the physical memory available. The MaxMemoryUsage custom property can be updated by a property expression when the package is loaded. For more information, see Integration Services (SSIS) Expressions, Use Property Expressions in Packages, and Transformation Custom Properties.

Controlling Fuzzy Matching Behavior

The Fuzzy Lookup transformation includes three features for customizing the lookup it performs: maximum number of matches to return per input row, token delimiters, and similarity thresholds.

The transformation returns zero or more matches up to the number of matches specified. Specifying a maximum number of matches does not guarantee that the transformation returns the maximum number of matches; it only guarantees that the transformation returns at most that number of matches. If you set the maximum number of matches to a value greater than 1, the output of the transformation may include more than one row per lookup and some of the rows may be duplicates.

The transformation provides a default set of delimiters used to tokenize the data, but you can add token delimiters to suit the needs of your data. The Delimiters property contains the default delimiters. Tokenization is important because it defines the units within the data that are compared to each other.

The similarity thresholds can be set at the component and join levels. The join-level similarity threshold is only available when the transformation performs a fuzzy match between columns in the input and the reference table. The similarity range is 0 to 1. The closer to 1 the threshold is, the more similar the rows and columns must be to qualify as duplicates. You specify the similarity threshold by setting the MinSimilarity property at the component and join levels. To satisfy the similarity that is specified at the component level, all rows must have a similarity across all matches that is greater than or equal to the similarity threshold that is specified at the component level. That is, you cannot specify a very close match at the component level unless the matches at the row or join level are equally close.

Each match includes a similarity score and a confidence score. The similarity score is a mathematical measure of the textural similarity between the input record and the record that Fuzzy Lookup transformation returns from the reference table. The confidence score is a measure of how likely it is that a particular value is the best match among the matches found in the reference table. The confidence score assigned to a record depends on the other matching records that are returned. For example, matching St. and Saint returns a low similarity score regardless of other matches. If Saint is the only match returned, the confidence score is high. If both Saint and St. appear in the reference table, the confidence in St. is high and the confidence in Saint is low. However, high similarity may not mean high confidence. For example, if you are looking up the value Chapter 4, the returned results Chapter 1, Chapter 2, and Chapter 3 have a high similarity score but a low confidence score because it is unclear which of the results is the best match.

The similarity score is represented by a decimal value between 0 and 1, where a similarity score of 1 means an exact match between the value in the input column and the value in the reference table. The confidence score, also a decimal value between 0 and 1, indicates the confidence in the match. If no usable match is found, similarity and confidence scores of 0 are assigned to the row, and the output columns copied from the reference table will contain null values.

Sometimes, Fuzzy Lookup may not locate appropriate matches in the reference table. This can occur if the input value that is used in a lookup is a single, short word. For example, helo is not matched with the value hello in a reference table when no other tokens are present in that column or any other column in the row.

The transformation output columns include the input columns that are marked as pass-through columns, the selected columns in the lookup table, and the following additional columns:

• _Similarity, a column that describes the similarity between values in the input and reference columns.

• _Confidence, a column that describes the quality of the match.

The transformation uses the connection to the SQL Server database to create the temporary tables that the fuzzy matching algorithm uses.

Running the Fuzzy Lookup Transformation

When the package first runs the transformation, the transformation copies the reference table, adds a key with an integer data type to the new table, and builds an index on the key column. Next, the transformation builds an index, called a match index, on the copy of the reference table. The match index stores the results of tokenizing the values in the transformation input columns, and the transformation then uses the tokens in the lookup operation. The match index is a table in a SQL Server database.

When the package runs again, the transformation can either use an existing match index or create a new index. If the reference table is static, the package can avoid the potentially expensive process of rebuilding the index for repeat sessions of data cleaning. If you choose to use an existing index, the index is created the first time that the package runs. If multiple Fuzzy Lookup transformations use the same reference table, they can all use the same index. To reuse the index, the lookup operations must be identical; the lookup must use the same columns. You can name the index and select the connection to the SQL Server database that saves the index.

If the transformation saves the match index, the match index can be maintained automatically. This means that every time a record in the reference table is updated, the match index is also updated. Maintaining the match index can save processing time, because the index does not have to be rebuilt when the package runs. You can specify how the transformation manages the match index.

The following table describes the match index options.

Microsoft Fuzzy Lookup Machine

Maintenance of the Match Index Table

The GenerateAndMaintainNewIndex option installs triggers on the reference table to keep the match index table and the reference table synchronized. If you have to remove the installed trigger, you must run the sp_FuzzyLookupTableMaintenanceUnInstall stored procedure, and provide the name specified in the MatchIndexName property as the input parameter value.

You should not delete the maintained match index table before running the sp_FuzzyLookupTableMaintenanceUnInstall stored procedure. If the match index table is deleted, the triggers on the reference table will not execute correctly. All subsequent updates to the reference table will fail until you manually drop the triggers on the reference table.

The SQL TRUNCATE TABLE command does not invoke DELETE triggers. If the TRUNCATE TABLE command is used on the reference table, the reference table and the match index will no longer be synchronized and the Fuzzy Lookup transformation fails. While the triggers that maintain the match index table are installed on the reference table, you should use the SQL DELETE command instead of the TRUNCATE TABLE command.

Note

When you select Maintain stored index on the Reference Table tab of the Fuzzy Lookup Transformation Editor, the transformation uses managed stored procedures to maintain the index. These managed stored procedures use the common language runtime (CLR) integration feature in SQL Server. By default, CLR integration in SQL Server is not enabled. To use the Maintain stored index functionality, you must enable CLR integration. For more information, see Enabling CLR Integration.

Because the Maintain stored index option requires CLR integration, this feature works only when you select a reference table on an instance of SQL Server where CLR integration is enabled.

Row Comparison

When you configure the Fuzzy Lookup transformation, you can specify the comparison algorithm that the transformation uses to locate matching records in the reference table. If you set the Exhaustive property to True, the transformation compares every row in the input to every row in the reference table. This comparison algorithm may produce more accurate results, but it is likely to make the transformation perform more slowly unless the number of rows is the reference table is small. If the Exhaustive property is set to True, the entire reference table is loaded into memory. To avoid performance issues, it is advisable to set the Exhaustive property to True during package development only.

If the Exhaustive property is set to False, the Fuzzy Lookup transformation returns only matches that have at least one indexed token or substring (the substring is called a q-gram) in common with the input record. To maximize the efficiency of lookups, only a subset of the tokens in each row in the table is indexed in the inverted index structure that the Fuzzy Lookup transformation uses to locate matches. When the input dataset is small, you can set Exhaustive to True to avoid missing matches for which no common tokens exist in the index table.

Caching of Indexes and Reference Tables

When you configure the Fuzzy Lookup transformation, you can specify whether the transformation partially caches the index and reference table in memory before the transformation does its work. If you set the WarmCaches property to True, the index and reference table are loaded into memory. When the input has many rows, setting the WarmCaches property to True can improve the performance of the transformation. When the number of input rows is small, setting the WarmCaches property to False can make the reuse of a large index faster.

Microsoft Fuzzy Lookup Add-in For Excel

Temporary Tables and Indexes

At run time, the Fuzzy Lookup transformation creates temporary objects, such as tables and indexes, in the SQL Server database that the transformation connects to. The size of these temporary tables and indexes is proportionate to the number of rows and tokens in the reference table and the number of tokens that the Fuzzy Lookup transformation creates; therefore, they could potentially consume a significant amount of disk space. The transformation also queries these temporary tables. You should therefore consider connecting the Fuzzy Lookup transformation to a non-production instance of a SQL Server database, especially if the production server has limited disk space available.

The performance of this transformation may improve if the tables and indexes it uses are located on the local computer. If the reference table that the Fuzzy Lookup transformation uses is on the production server, you should consider copying the table to a non-production server and configuring the Fuzzy Lookup transformation to access the copy. By doing this, you can prevent the lookup queries from consuming resources on the production server. In addition, if the Fuzzy Lookup transformation maintains the match index-that is, if MatchIndexOptionsis set to GenerateAndMaintainNewIndex-the transformation may lock the reference table for the duration of the data cleaning operation and prevent other users and applications from accessing the table.

Configuring the Fuzzy Lookup Transformation

You can set properties through SSIS Designer or programmatically.

For more information about the properties that you can set in the Advanced Editor dialog box or programmatically, click one of the following topics:

Related Tasks

For details about how to set properties of a data flow component, see Set the Properties of a Data Flow Component.

Fuzzy Lookup Transformation Editor (Reference Table Tab)

Use the Reference Table tab of the Fuzzy Lookup Transformation Editor dialog box to specify the source table and the index to use for the lookup. The reference data source must be a table in a SQL Server database.

Note

The Fuzzy Lookup transformation creates a working copy of the reference table. The indexes described below are created on this working table by using a special table, not an ordinary SQL Server index. The transformation does not modify the existing source tables unless you select Maintain stored index. In this case, it creates a trigger on the reference table that updates the working table and the lookup index table based on changes to the reference table.

Note

The Exhaustive and the MaxMemoryUsage properties of the Fuzzy Lookup transformation are not available in the Fuzzy Lookup Transformation Editor, but can be set by using the Advanced Editor. In addition, a value greater than 100 for MaxOutputMatchesPerInput can be specified only in the Advanced Editor. For more information on these properties, see the Fuzzy Lookup Transformation section of Transformation Custom Properties.

Options

OLE DB connection manager
Select an existing OLE DB connection manager from the list, or create a new connection by clicking New.

New
Create a new connection by using the Configure OLE DB Connection Manager dialog box.

Generate new index
Specify that the transformation should create a new index to use for the lookup.

Reference table name
Select the existing table to use as the reference (lookup) table.

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Store new index
Select this option if you want to save the new lookup index.

New index name
If you have chosen to save the new lookup index, type a descriptive name for the index.

Maintain stored index
If you have chosen to save the new lookup index, specify whether you also want SQL Server to maintain the index.

Note

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When you select Maintain stored index on the Reference Table tab of the Fuzzy Lookup Transformation Editor, the transformation uses managed stored procedures to maintain the index. These managed stored procedures use the common language runtime (CLR) integration feature in SQL Server. By default, CLR integration in SQL Server is not enabled. To use the Maintain stored index functionality, you must enable CLR integration. For more information, see Enabling CLR Integration.

Make computer run faster microsoft. Because the Maintain stored index option requires CLR integration, this feature works only when you select a reference table on an instance of SQL Server where CLR integration is enabled.

Use existing index
Specify that the transformation should use an existing index for the lookup.

Name of an existing index
Select a previously created lookup index from the list.

Fuzzy Lookup Transformation Editor (Columns Tab)

Use the Columns tab of the Fuzzy Lookup Transformation Editor dialog box to set properties for input and output columns.

Options

Available Input Columns
Drag input columns to connect them to available lookup columns. These columns must have matching, supported data types. Select a mapping line and right-click to edit the mappings in the Create Relationships dialog box.

Name
View the names of the available input columns.

Pass Through
Specify whether to include the input columns in the output of the transformation.

Available Lookup Columns
Use the check boxes to select columns on which to perform fuzzy lookup operations.

Lookup Column
Select lookup columns from the list of available reference table columns. Your selections are reflected in the check box selections in the Available Lookup Columns table. Selecting a column in the Available Lookup Columns table creates an output column that contains the reference table column value for each matching row returned.

Output Alias
Type an alias for the output for each lookup column. The default is the name of the lookup column with a numeric index value appended; however, you can choose any unique, descriptive name.

Fuzzy Lookup Transformation Editor (Advanced Tab)

Use the Advanced tab of the Fuzzy Lookup Transformation Editor dialog box to set parameters for the fuzzy lookup.

Options

Maximum number of matches to output per lookup
Specify the maximum number of matches the transformation can return for each input row. The default is 1.

Similarity threshold
Set the similarity threshold at the component level by using the slider. The closer the value is to 1, the closer the resemblance of the lookup value to the source value must be to qualify as a match. Increasing the threshold can improve the speed of matching since fewer candidate records need to be considered.

Token delimiters
Specify the delimiters that the transformation uses to tokenize column values.

See Also

Microsoft Fuzzy Lookup Add In

Lookup Transformation
Fuzzy Grouping Transformation
Integration Services Transformations