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Table of Contents

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  1. Preface
  2. Working with Transformations
  3. Address Validator Transformation
  4. Aggregator Transformation
  5. Association Transformation
  6. Bad Record Exception Transformation
  7. Case Converter Transformation
  8. Classifier Transformation
  9. Cleanse transformation
  10. Comparison Transformation
  11. Custom Transformation
  12. Custom Transformation Functions
  13. Consolidation Transformation
  14. Data Masking Transformation
  15. Data Masking Examples
  16. Decision Transformation
  17. Duplicate Record Exception Transformation
  18. Dynamic Lookup Cache
  19. Expression Transformation
  20. External Procedure Transformation
  21. Filter Transformation
  22. HTTP Transformation
  23. Identity Resolution Transformation
  24. Java Transformation
  25. Java Transformation API Reference
  26. Java Expressions
  27. Java Transformation Example
  28. Joiner Transformation
  29. Key Generator Transformation
  30. Labeler Transformation
  31. Lookup Transformation
  32. Lookup Caches
  33. Match Transformation
  34. Match Transformations in Field Analysis
  35. Match Transformations in Identity Analysis
  36. Merge Transformation
  37. Normalizer Transformation
  38. Parser Transformation
  39. Rank Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. Source Qualifier Transformation
  44. SQL Transformation
  45. Using the SQL Transformation in a Mapping
  46. Stored Procedure Transformation
  47. Standardizer Transformation
  48. Transaction Control Transformation
  49. Union Transformation
  50. Unstructured Data Transformation
  51. Update Strategy Transformation
  52. Weighted Average Transformation
  53. XML Transformations

Transformation Guide

Transformation Guide

When to Use a Labeler Transformation

When to Use a Labeler Transformation

The Labeler transformation writes a descriptive label for each value on a port.
The following examples describe some of the types of analysis you can perform with a Labeler transformation.
Find records with contact data
Configure the transformation with a reference table that contains a list of first names. Create a token labeling strategy to label any string that matches a value in the reference table. When you review the output data, any record that contains the label is likely to identify a person.
Find business records
Configure the transformation with a token set that contains a list of business suffixes, such as Inc, Corp, and Ltd. Create a token labeling strategy to label any string that matches a value in the reference table. When you review the output data, any record that contains the label is likely to identify a business.
Use a token set of business suffixes you want to identify any business name. You can use a reference table of business names if you are certain that the table contains all the businesses you want to identify. For example, you can use a reference table that lists the corporations on the New York Stock Exchange.
Find telephone number data
Configure the transformation with character set that defines the character structure of a telephone number. For example, you can use a character set that recognizes different patterns of punctuation symbols and digits as United States telephone numbers. You can review the data to find records that do not contain the correct digits for a telephone number.
The character labels may use the following characters to analyze the column data:
c=punctuation character n=digit s=space
The following table shows sample telephone number structures:
Character Structure
Telephone number
cnnncsnnncnnncnnnnn
(212) 555-1212
nnnnnnnnnn
2125551212
cnnncnnncnnnn
+212-555-1212

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