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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

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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