Table of Contents

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  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Match Transformation
  30. Match Transformations in Field Analysis
  31. Match Transformations in Identity Analysis
  32. Normalizer Transformation
  33. Merge Transformation
  34. Parser Transformation
  35. Python Transformation
  36. Rank Transformation
  37. Read Transformation
  38. Relational to Hierarchical Transformation
  39. REST Web Service Consumer Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. SQL Transformation
  44. Standardizer Transformation
  45. Union Transformation
  46. Update Strategy Transformation
  47. Web Service Consumer Transformation
  48. Parsing Web Service SOAP Messages
  49. Generating Web Service SOAP Messages
  50. Weighted Average Transformation
  51. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer 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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