Method and apparatus for automatically mapping physical data models/objects to logical data models and business terms

A system for implementing an automatic mapping module, the system comprising:

• a database that includes a raw physical data model of an application;
• and
• a processor coupled to the database via a communication network, wherein the processor is configured to:
• extract the raw physical data model of the application from the database;
• convert physical object names associated with the raw physical data model into English terms based on a taxonomy expansion list;
• apply a plurality of standardization and contextualization rules to the English terms generated from converting the physical object names;
• output names based on applying the plurality of standardization and contextualization rules to the English terms;
• apply fuzzy logic and machine learning routines and matching algorithms for matching the names to predefined logical terms to identify potential data exposures and matching columns within tables of a schema to the predefined logical terms;
• and
• automatically generate a mapping of physical objects or elements in the application with logical attributes and related business terms,
• wherein the applying fuzzy logic and machine learning routines and matching algorithms further comprising:
• implementing a fuzzy matching rule engine configured to utilize text length or tokenization algorithms to identify a best match against the predefined logical terms;
• and
• assigning a probability of accuracy value to each matched term;
• implementing an automated voting process engine, that implements a weight-based voting process, configured to select an algorithm with a maximum hit value as the best match;
• and
• populating a data catalog with the matched terms when the assigned probability of accuracy value satisfies a predetermined threshold value.

A non-transitory computer readable medium configured to store instructions for implementing an automatic mapping module, wherein, when executed, the instructions cause a processor to perform the following:

• accessing a database that includes a raw physical data model of an application;
• extracting the raw physical data model of the application from the database;
• converting physical object names associated with the raw physical data model into English terms based on a taxonomy expansion list;
• applying a plurality of standardization and contextualization rules to the English terms generated from converting the physical object names;
• outputting names based on applying the plurality of standardization and contextualization rules to the English terms;
• applying fuzzy logic and machine learning routines and matching algorithms for matching the names to predefined logical terms to identify potential data exposures and matching columns within tables of a schema to the predefined logical terms;
• automatically generating a mapping of physical objects or elements in the application with logical attributes and related business terms,
• wherein the applying fuzzy logic and machine learning routines and matching algorithms further comprising:
• implementing a fuzzy matching rule engine configured to utilize text length or tokenization algorithms to identify a best match against the predefined logical terms;
• and
• assigning a probability of accuracy value to each matched term;
• implementing an automated voting process engine, that implements a weight-based voting process, configured to select an algorithm with a maximum hit value as the best match;
• and
• populating a data catalog with the matched terms when the assigned probability of accuracy value satisfies a predetermined threshold value.