Comparing Inexact String Matching Methods for Large Scale Entity Matching

the Advisor was originally built with the purpose of helping users build a strong bibliography by extending the document set obtained at first-level search. To do this however, diverse datasets containing critical metadata for this project to work must be matched. Thus, inexact string matching is needed but for a long-time inexact string matching has had issues with both accuracy and runtime on large scale data. Thus, this project utilizes a two-phase method. Using this two-phase method has allowed the Advisor to be revived through gaining important metadata but how does it compare against other applications? This project compares the two-phase method that is proposed against popular applications with inexact string matching capabilities such as PostgreSQL, Elasticsearch, and MongoDB.