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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

Probabilistic Models

Probabilistic Models

A probabilistic model identifies tokens by the types of information that they contain and by the positions that they occupy in an input string.
A probabilistic model contains reference data values and label values. The reference data values represent the data on an input port that you connect to the transformation. The label values describe the types of information that the reference data values contain. You assign a label to each reference data value in the model.
To link the reference data values to the labels in a probabilistic model, you compile the model. The compilation process generates a series of logical associations between the data values and the labels. When you run a mapping that reads the model, the Data Integration Service applies the model logic to the transformation input data. The Data Integration Service returns the label that most accurately describes the input data values.
You create a probabilistic model in the Developer tool. The Model repository stores the probabilistic model object. The Developer tool writes the data values, the labels, and the compilation data to a file in the Informatica directory structure.

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