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. Macro Transformation
  30. Match Transformation
  31. Match Transformations in Field Analysis
  32. Match Transformations in Identity Analysis
  33. Normalizer Transformation
  34. Merge Transformation
  35. Parser Transformation
  36. Python Transformation
  37. Rank Transformation
  38. Read Transformation
  39. Relational to Hierarchical Transformation
  40. REST Web Service Consumer Transformation
  41. Router Transformation
  42. Sequence Generator Transformation
  43. Sorter Transformation
  44. SQL Transformation
  45. Standardizer Transformation
  46. Union Transformation
  47. Update Strategy Transformation
  48. Web Service Consumer Transformation
  49. Parsing Web Service SOAP Messages
  50. Generating Web Service SOAP Messages
  51. Weighted Average Transformation
  52. Window Transformation
  53. Write Transformation
  54. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer 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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