Abstract: There is provided a computer implemented data driven classification and troubleshooting system and method. The system has an interface application enabled to receive data. The system has an associative memory software in communication with the interface application via an API. The associative memory software has an associative memory and a machine learning algorithm. The system has one or more individual areas, within the associative memory, requiring one or more troubleshooting actions to improve accuracy of the individual areas. The system has at least one troubleshooting tool enabled by the interface application. The at least one troubleshooting tool enables or performs the troubleshooting actions. The system has a quality rating metric (QRM) that measures a strength and an assurance that one or more predictions of the associative memory are correct. The one or more troubleshooting actions results in improving the accuracy and the performance of the associative memory.

Abstract: A computer implemented data driven classification and data quality checking system is provided. The system has an interface application enabled to receive data and has an associative memory software. The system has a data driven associative memory model configured to categorize one or more fields of received data and to analyze the received data. The system has a data quality rating metric associated with the received data. The system has a machine learning data quality checker for the received data, and is configured to add the received data to a pool of neighboring data, if the data quality rating metric is greater than or equal to a data quality rating metric threshold. The machine learning data quality checker is configured to generate and communicate 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.

Abstract: 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.

Abstract: There is provided a computer implemented data driven classification and troubleshooting system and method. The system has an interface application enabled to receive data. The system has an associative memory software in communication with the interface application via an API. The associative memory software has an associative memory and a machine learning algorithm. The system has one or more individual areas, within the associative memory, requiring one or more troubleshooting actions to improve accuracy of the individual areas. The system has at least one troubleshooting tool enabled by the interface application. The at least one troubleshooting tool enables or performs the troubleshooting actions. The system has a quality rating metric (QRM) that measures a strength and an assurance that one or more predictions of the associative memory are correct. The one or more troubleshooting actions results in improving the accuracy and the performance of the associative memory.

Abstract: A computer implemented data driven classification and data quality checking system is provided. The system has an interface application enabled to receive data and has an associative memory software. The system has a data driven associative memory model configured to categorize one or more fields of received data and to analyze the received data. The system has a data quality rating metric associated with the received data. The system has a machine learning data quality checker for the received data, and is configured to add the received data to a pool of neighboring data, if the data quality rating metric is greater than or equal to a data quality rating metric threshold. The machine learning data quality checker is configured to generate and communicate 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.

Abstract: 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.