Creating Manual Match Rule - discovery - 23.1

Spectrum Discovery Guide

Product type
Software
Portfolio
Verify
Product family
Spectrum
Product
Spectrum > Discovery
Version
23.1
Language
English
Product name
Spectrum Discovery
Title
Spectrum Discovery Guide
Topic type
How Do I
Overview
Reference
First publish date
2007
ft:lastEdition
2024-02-07
ft:lastPublication
2024-02-07T17:21:58.768552

To create a manual match rule, perform these steps:
  1. On the Source Details page, click the Create matching rule button.
    The Create Match Rule page is displayed.
  2. On the Create Match Rule page, go to the Manual Match Process section and click the Create Rule button.
    The Manual Match Rule page is displayed. On the Manual Match Rule page, you can:
    • Configure one of the predefined rules from Template Rules located on the left corner of the page which you can use as-is, or

    • Configure a new match rule and publish it to the repository for re-use.

  3. Enter a unique name for your match rule in the Rule name field.
  4. Optional: Select a match key from the Linked Match Key drop-down that you need to link to the match rule. You can see a preview of the match key too by clicking the Preview Match Key button. As you click, a new window opens, where you can further modify or use the same match key per your needs.
    Note: You can unlink the match key at any point in time by clearing the match key. It is mandatory to publish the rule again to accommodate the changes.
  5. Specify the dataflow fields, parent or child; you want to use in the match rule and match rule hierarchy.
    • Click button and enter a name for the parent below Match when not true.
    • Click button and select a field to add to the parent from the drop-down list below Match when not true.
      Note: All children under a parent must use the same logical operator. If you want to use different logical operators between fields, you must first create intermediate parents.
  6. Define these parent options as listed in the table below, which are displayed on the parent node:
    Option Description

    Match when not true

    It changes the logical operator for the parent from and to and not. If you select this option, records will only match if they do not match the logic defined in this parent.
    Note: If you select the Match when not true option, it negates the Matching Method options. For more information, see the section Negative Match Conditions in the Spectrum Data Quality Guide.
    Matching Method Select one of these from the drop-down list to determine if a parent is a match or non-match:
    • All true: A parent is considered a match if all children are determined to match. This method creates an "and" connector between children.
    • Any true: A parent is considered a match if at least one child is determined to match. This method creates an "or" connector between children.
    • Based on threshold: A parent is considered a match if at least one child is determined to match. This method creates an "or" connector between children.

      If you select this option, the Threshold field enables you to specify a threshold value. The Scoring Method determines which logical connector to use. The thresholds at the parent cannot be higher than the threshold of the children. For more information, see the matching method-to-scoring method matrix below this table.

    Missing Data Select one of these from the drop-down list to specify how to score blank data in a field:
    • Ignore blanks: Ignores the field if it contains blank data.
    • Count as 0: Scores the field as 0 if it contains blank data.
    • Count as 100: Scores the field as 100 if it contains blank data.
    • Compare blanks: Scores the suspect and candidate fields as 100 if they both contain blank data; otherwise, scores the suspect and candidate fields as 0.
    Scoring Method Select one of these from the drop-down list to determine the matching score:
    • Weighted Average: Uses the weight of each child to determine the average match score.
    • Average: Uses the average score of each child to determine the score of a parent.
    • Maximum: Uses the highest child score to determine the score of a parent.
    • Minimum: Uses the lowest child score to determine the score of a parent.
    • Vector Summation: Uses the vector summation of each child score to determine the score of the parent. The formula for calculation is:

      sqrt(a^2+b^2+c^2) / sqrt(n), where a, b, and c are the scores of three children, and n is the number of children.

      For more information, see the matching method-to-scoring method matrix below this table.

    Evaluate Click the Evaluate button to evaluate match rule. For more information, see Evaluating a match rule.
    Copy settings to It allows you to copy the same settings for any number of elements.
    1. Use the drop-down list to select or de-select the elements.
    2. Click Apply adjacent to the Copy Settings to field to copy and apply the same settings for the selected elements.
    Note: You can copy the parent settings to a parent element and child settings to a child element only.
    Matching Method-to-Scoring Method Matrix
    The table below shows the logical relationship between Matching Method and Scoring Method and how each combination changes the logic used during match processing.
    Scoring Method Matching Method Comments
    Any true All true Based on threshold
    Weighted Average NA and and

    Only available when All true or Based on threshold are selected as the Matching Method.

    Average NA and and
    Vector Summation NA and and
    Maximum or NA or Only available when All true or Based on threshold are selected as the Matching Method.
    Minimum or NA or
  7. Define these child options as listed in the table below, which are displayed on the child node:
    Option Description

    Match when not true

    It changes the logical operator from and to not. If you select this option, the match rule will only evaluate to true if the records do not match the logic defined in this child.

    For example, if you want to identify individuals who are associated with multiple accounts, you could create a match rule that matches the name but where the account number does not match. You would use the Match when not true option for the child that matches the account number.

    Candidate field

    Select this to map the child record field you select from the drop-down list to a field in the input file.

    Cross match against Select this to choose one or more field names from the drop-down list to match different fields to one another between two records.
    Threshold

    Enter the threshold that must be met at the individual field level for that field to be determined a match.

    Missing Data Select one of these from the drop-down list to specify how to score blank data in a field:
    • Ignore blanks: Ignores the field if it contains blank data.
    • Count as 0: Scores the field as 0 if it contains blank data.
    • Count as 100: Scores the field as 100 if it contains blank data.
    • Compare blanks: Scores the suspect and candidate fields as 100 if they both contain blank data; otherwise, scores the suspect and candidate fields as 0.
    Scoring Method Select one of these from the drop-down list to determine the matching score:
    • Weighted Average: Uses the weight of each algorithm to determine the average match score.
    • Average: Uses the average score of each algorithm to determine the match score.
    • Maximum: Uses the highest algorithm score to determine the match score.
    • Minimum: Uses the lowest algorithm score to determine the match score.
    • Vector Summation: Uses vector summation of the score of each algorithm to determine the match score. This scoring method is useful if you want a higher vector summation match score in one or more algorithms to get proportionately represented in the final match score. The formula for calculating the final score is:

      sqrt(a^2+b^2+c^2) / sqrt(n), where a, b, and c are the scores of three different algorithms, and n is the number of algorithms used.

    Evaluate Click the Evaluate button to evaluate match rule. For more information, see Evaluating a match rule.
    Profile Statistics Click the Profile Statistics button to see the column profile statistics, which is displayed in a side-panel. For more information, see Viewing Column Profile Statistics.
    Copy Settings to It allows you to copy the same settings for any number of elements.
    1. Use the drop-down list to select or de-select the elements.
    2. Click Apply adjacent to the Copy Settings to field to copy and apply the same settings for the selected elements.
    Note: You can copy the parent settings to a parent element and child settings to a child element only.
  8. To configure algorithms for your child type, click Configure Algorithms on the child options node to add one or more of these algorithms to determine the match in the field values:
    Note: Use Search to selectively configure the algorithms.

    String Matching Algorithms

    Acronym
    It determines whether a business name matches its acronym by looking for acronym data; otherwise, it creates an acronym using the first character of every word.

    For example, Internal Revenue Service and its acronym IRS would be considered a match and return a match score of 100.

    Character Frequency
    It determines the frequency of occurrence of each character in a string and compares the overall frequencies between two strings.
    Exact Match
    It determines if two strings are the same.
    Initials
    It matches the initials for parsed personal names.
    Name Variant
    It determines whether two names are variants of each other. The algorithm returns a match score of 100 if two names are variations of each other, and a match score of 0 if two names are not variations of each other.

    For example, JOHN is a variation of JAKE and returns a match score of 100. JOHN is not a variant of HENRY and returns a match score of 0.

    Click Edit to specify the name variant options. For more information, see the section Name Variant Finder in the Spectrum Data Quality Guide.

    Numeric String
    It compares address lines by separating the numerical attributes of an address line from the characters. See the examples below.
    • In the string address 1234 Main Street Apt 567, the numerical attributes of the string (1234567) are parsed and handled differently from the remaining string value (Main Street Apt). The algorithm first matches numeric data in the string with the numeric algorithm. If the numeric data match is 100, the alphabetic data is matched using Edit distance and Character Frequency. The final match score is calculated as follows:

      (numericScore + (EditDistanceScore + CharacterFrequencyScore) / 2) / 2)

    • If you calculate the match score of these two addresses:

      123 Main St Apt 567
      123 Maon St Apt 567

      the match score would be 95.5, calculated as follows:

      Numeric Score = 100
      Edit Distance = 91
      Character Frequency = 91

      91 + 91 = 182
      182/2 = 91
      100 + 91 = 191
      191/2 = 95.5

    SubString
    It determines whether one string occurs within another.

    Phonetic Algorithms

    Daitch-Mokotoff Soundex
    A Phonetic algorithm that allows greater accuracy in matching of Slavic and Yiddish surnames with similar pronunciation but differences in spelling. Coded names are six digits long, and multiple possible encodings can be returned for a single name. This option was developed to respond to the limitations of Soundex in the processing of Germanic or Slavic surnames.
    Double Metaphone
    It determines the similarity between two strings based on a phonetic representation of their characters. Double Metaphone is an improved version of the Metaphone algorithm and attempts to account for the many irregularities found in different languages.
    Koeln
    Indexes names by sound as they are pronounced in German. Allows names with the same pronunciation to be encoded to the same representation so that they can be matched, despite minor differences in spelling. The result is always a sequence of numbers; special characters and white spaces are ignored. This option was developed to respond to the limitations of Soundex.
    Metaphone

    It determines the similarity between two English-language strings based on a phonetic representation of their characters. This option was developed to respond to the limitations of Soundex.

    Metaphone (Spanish)

    It determines the similarity between two strings based on a phonetic representation of their characters. This option was developed to respond to the limitations of Soundex.

    Metaphone3

    It improves upon the Metaphone and Double Metaphone algorithms with a more exact consonant and internal vowel settings that allow you to produce words or names more or less closely matched to search terms on a phonetic basis. Metaphone3 increases the accuracy of phonetic encoding to 98%. This option was developed to respond to the limitations of Soundex.

    Nysiis
    It is a phonetic code algorithm that matches an approximate pronunciation to an exact spelling and indexes words that are pronounced similarly—part of the New York State Identification and Intelligence System.
    For example, consider that you are looking for someone's information in a database of people. You believe that the person's name sounds like "John Smith," but it is spelled "Jon Smath". If you conducted a search looking for an exact match for "John Smith," no results would be returned. However, if you index the database using the NYSIIS algorithm and search using the NYSIIS algorithm again, the correct match will be returned because both "John Smith" and "Jon Smath" are indexed as "JANSNATH" by the algorithm. This option was developed to respond to limitations of Soundex; it handles some multicharacter n-grams and maintains relative vowel positioning, whereas Soundex does not.
    Note: This algorithm does not process non-alpha characters; records containing them will fail during processing.
    Phonix
    It preprocesses name strings by applying more than 100 transformation rules to single characters or sequences of several characters. Nineteen of those rules are applied only if the characters are at the beginning of the string, while 12 of the rules are applied only if they are at the middle of the string, and 28 of the rules are applied only if they are at the end of the string. The transformed name string is encoded into a code that is comprised of a starting letter followed by three digits (removing zeros and duplicate numbers). This option was developed to respond to the limitations of Soundex; it is more complex and, therefore, slower than Soundex.
    Sonnex
    It determines the similarity between two French-language strings based on the phonetic representation of their characters. It returns a Sonnex coded key of the selected fields.
    Soundex
    It determines the similarity between two strings based on a phonetic representation of their characters.
    Syllable Alignment
    It combines phonetic information with edit distance based calculations. Converts the strings to be compared into their corresponding sequences of syllables and calculates the number of edits required to convert one sequence of syllables to the other.

    Similarity and Distance Measures

    Edit Distance
    It determines the similarity between two strings based on the number of deletions, insertions, or substitutions required to transform one string into another.
    Euclidean Distance
    It provides a similarity measure between two strings using the vector space of combined terms as the dimensions. It also determines the greatest common divisor of two integers. It takes a pair of positive integers and forms a new pair that consists of the smaller number and the difference between the larger and smaller numbers. The process repeats until the numbers are equal. That number then is the greatest common divisor of the original pair.

    For example, 21 is the greatest common divisor of 252 and 105: (252 = 12 × 21; 105 = 5 × 21); since 252 − 105 = (12 − 5) × 21 = 147, the GCD of 147 and 105 is also 21.

    Jaro-Winkler Distance
    It determines the similarity between two strings based on the number of character replacements it takes to transform one string into another. This option was developed for short strings, such as personal names.
    Keyboard Distance
    It determines the similarity between two strings based on the number of deletions, insertions, or substitutions required to transform one string to the other, weighted by the position of the keys on the keyboard.

    Click Edit to specify the type of keyboard you are using: QWERTY (U.S.), QWERTZ (Austria and Germany), or AZERTY (France).

    Kullback-Liebler Distance
    It determines the similarity between two strings based on the differences between the distribution of words in the two strings.
    NGram Distance

    It calculates in text or speech the probability of the next term based on the previous n terms, which can include phonemes, syllables, letters, words, or base pairs and can consist of any combination of letters.

    Click Edit to enter the size of the NGram; the default is 2.

    NGram Similarity
    It determines the similarity between two strings based on the length of the longest common subsequence of phonemes, syllables, letters, words, or base pairs.

    Click Edit to specify these options:

    • Ngram size: Enter the size of the NGram. The default is 2.
    • Drop Noise Characters: Select to replace punctuation with space.
    • Drop Spaces: Select to merge words.

    Date Algorithms

    Date
    It compares date fields regardless of the date format in the input records. Click Edit to specify these:
    • General Options—Require Month: It prevents a date that consists only of a year from matching.
    • General Options—Require Day: It prevents a date that consists only of a month and year from matching.
    • General Options—Match Transposed MM/DD: Where month and day are provided in numeric format, it compares suspect month to candidate day and suspect day to candidate month as well as the standard comparison of the suspect month to candidate month and suspect day to candidate day.
    • General Options—Prefer DD/MM/YYYY format over MM/DD/YYYY: It contributes to date parsing in cases where both month and day are provided in numeric format, and their identification can not be determined by context.

      For example, given the numbers 5 and 13, the parser will automatically assign 5 to the month and 13 to the day because there are only 12 months in a year. However, given the numbers 5 and 12 (or any two numbers 12 and under), the parser will assume whichever number is first to be the month.

      If you select this option, it ensures that the parser reads the first number as the day rather than the month.

    • Range Options—Overall: It allows you to set the maximum number of days between matching dates. See the examples below.
      • If you enter an overall range of 35 days and your candidate date is December 31, 2000, a suspect date of February 5, 2001, would be a match, but a suspect date of February 6 would not.
      • If you enter an overall range of 1 day and your candidate date is January 2000, a suspect date of 1999 would be a match (comparing December 31, 1999), but a suspect date of January 2001 would not.
    • Range Options—Year: It allows you to set the number of years between matching dates, independent of month, and day. See the examples below.
      • If you enter a year range of 3 and your candidate date is January 31, 2000, a suspect date of January 31, 2003, would be a match, but a suspect date of February 2003 would not.
      • If your candidate date is 2000, a suspect date of March 2003 would be a match because months are not in conflict, and it's within the three-year range.
    • Range Options—Month: It allows you to set the number of months between matching dates, independent of year and day.

      For example, if you enter a month range of 4 and your candidate date is January 1, 2000, a suspect date of May 2000 is a match because there is no day conflict and it's within the four-month range, but a suspect date of May 2, 2000, is not, because of the day's conflict.

    • Range Options—Day: It allows you to set the number of days between matching dates, independent of year and month.

      For example, if you enter a day range of 5 and your candidate date is January 1, 2000, a suspect date of January 2000 is a match because there is no day conflict but a suspect date of December 27, 1999, is not, because of the month's conflict.

    Matching Algorithm-to-Scoring Method Matrix

    The table below describes the logical relationship between the number of algorithms you can use based on the parent Scoring Method you select.

    Scoring Method Algorithms
    Single Multiple
    Weighted Average NA Yes
    Average NA Yes
    Vector Summation Yes Yes
    Maximum NA Yes
    Minimum NA Yes
  9. Click Ok.
    Note:
    • If you define n number of parent and child elements, use Filter to selectively look for the elements.
    • If you want to expand or collapse all the tree nodes, click the Expand all and Collapse all buttons.
    • To view the table's profile statistics, click the Profile Statistics button, which opens a side-panel to view the statistics. For more information, see Viewing Profile Statistics.
    You can now save or publish the rule. For more information, see Saving and publishing the rule.