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: 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: 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 analyzing transaction information that involves storing each one of a plurality of transactions in an associative memory with an associated cluster identification number. A given one of the transactions is selected for analysis, the given one of the transactions having a specific cluster identification number. An entity analytics engine is used to search and obtain a first subplurality of transactions from the associative memory that are similar to the given transaction by having a common attribute or entity and assigning each of the transactions a similarity score. Each one of the transactions is further analyzed to determine if it would be beneficial to form a formal transaction relationship with an organization involved with at least one of the transactions of the cluster.

Abstract: A method for analyzing transaction information that involves storing each one of a plurality of transactions in an associative memory with an associated cluster identification number. A given one of the transactions is selected for analysis, the given one of the transactions having a specific cluster identification number. An entity analytics engine is used to search and obtain a first subplurality of transactions from the associative memory that are similar to the given transaction by having a common attribute or entity and assigning each of the transactions a similarity score. Each one of the transactions is further analyzed to determine if it would be beneficial to form a formal transaction relationship with an organization involved with at least one of the transactions of the cluster.