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Table of Contents

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

Transformation Guide

Transformation Guide

Labeler Transformation Overview

Labeler Transformation Overview

The Labeler transformation is a passive transformation that analyzes input port fields and writes text labels that describe the data in each field.
You use a Labeler transformation when you want to understand the types of information that a port contains. Use a Labeler transformation when you do not know the types of information on a port, or when you want to identify records that do not contain the expected types of information on a port.
A label is a string one or more characters that describes an input string. You configure the Labeler transformation to assign labels to input strings based on the data that each string contain.
When you configure the transformation, you specify the types of character or string to search for, and you specify the label that the transformation writes as output when it finds the associated character or string. You enter the character and string types to search for, and the labels to use, when you configure a labeling operation. Or, you use reference data objects to specify the characters, strings, and labels.
You configure the transformation to perform character labeling or token labeling:
Character Labeling
Writes a label that describes the character structure of the input string, including punctuation and spaces. The transformation writes a single label for each row in a column. For example, the Labeler transformation can label the ZIP Code 10028 as "nnnnn," where "n" stands for a numeric character.
Token Labeling
Writes a label that describes the type of information in the input string. The transformation writes a label for each token identified in the input data. For example, you can configure the Labeler transformation to label the string "John J. Smith" with the tokens "Word Init Word."
A token is a delimited value in an input string.
When the Labeler finds a character or string that matches a label that you specify, it writes the label name to a new output port.
The Labeler transformation uses reference data to identify characters and tokens. You select the reference data object when you configure an operation in a Labeler strategy.

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