DATA DRIVEN CLASSIFICATION AND DATA QUALITY CHECKING METHOD

摘要

A method for improving accuracy and quality of received data is provided. The method provides a computer implemented data driven classification and data quality checking system. The method uses the associative memory software to build a data driven associative memory model that enables a machine learning data quality checker for receiving data. The method categorizes one or more fields of received data, analyzes the received data, and calculates a data quality rating metric, by comparing the received data with a pool of neighboring data in the category of field of the received data. The method accepts and adds the received data, if the data quality rating metric is greater than or equal to a data quality rating metric threshold, and generates and communicates an alert of a potential error in the received data, if the data quality rating metric is less than the data quality rating metric threshold.

主权项

1. A method for improving accuracy and quality of received data, the method comprising the steps of:
providing a computer implemented data driven classification and data quality checking system having an interface application in communication with an associative memory software, operating on one or more computers; using the associative memory software to build a data driven associative memory model that enables a machine learning data quality checker for receiving data; categorizing one or more fields of received data with the data driven associative memory model; analyzing the received data with the data driven associative memory model; calculating, with the data driven associative memory model, a data quality rating metric associated with the received data, by comparing the received data with a pool of neighboring data in the category of field of the received data; accepting and adding the received data to the pool of neighboring data by a machine learning data quality checker, if the data quality rating metric is greater than or equal to a data quality rating metric threshold; and generating and communicating with the machine learning data quality checker, via the interface application, an alert of a potential error in the received data in the category of field of the received data, if the data quality rating metric is less than the data quality rating metric threshold.