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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

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Process Flow for Field Match Analysis

Process Flow for Field Match Analysis

The following process flow summarizes the steps that you take to configure a Match transformation for field match analysis. You can define a process that uses the Match transformation alone or that uses the Match transformation and other transformations.
When you add a Match transformation to a mapping in field match analysis, add an upstream Key Generator transformation to the mapping.
To prepare the data for the Match transformation, perform the following steps:
  1. Organize the source data records into groups.
    Use a Key Generator transformation to assign a group key value to each record. The group assignments reduce the number of computations that the Match transformation must perform.
  2. Verify that the data source records contain unique sequence identifier values. You can use a Key Generator transformation to create the values.
Perform the following steps in the Match transformation:
  1. Specify field analysis as the match type, and specify the number of data sources.
    If you configure the transformation to analyze two data sets, select a master data set.
    Use the
    Match Type
     view to set the type and the number of data sources.
  2. Define a match analysis strategy. Select an algorithm, and assign a pair of columns to the algorithm.
    Use the
    Strategies
     view to define the strategy.
  3. Specify the method that the transformation uses to generate the match analysis results.
  4. Set the match threshold value. The match threshold is the minimum score that can identify two records as duplicates of one another.
    Use the
    Match Output
     view to select the output method and the match threshold.
    You can set the match threshold in a Match transformation or a Weighted Average transformation. Use the Weighted Average transformation if you create a match mapplet.
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