Abstract: A device configured to identify a first link between a value of a first data element in a first plurality of data elements and values of a first set of data elements in a second plurality of data elements and to remove the first link between the first data element and the first set of data elements. The device is further configured to input the data elements into a machine learning model that is configured to output a second link between the first data element and a second set of data elements. The device is further configured to create an entry in a relationship table that identifies the first data element and the second set of data elements. The device is further configured to generate a data stream with the first data element and the second set of data elements and to output the data stream.

Abstract: Systems for dynamic data reorganization and query optimization are provided. In some aspects, a database query may be received and evaluated to determine whether it meets one or more criteria. If the database query meets the one or more criteria, the query may be executed and the output of the executed database may be transmitted to the user device. If the database query does not meet the one or more criteria, data associated with the database query may be dynamically modified and/or reorganized (e.g., using machine learning). Modified data may be generated based on the dynamically modified and/or reorganized data. The modified data may be stored in an optimized query cache. An updated query may be generated based on the modified data and stored in the optimized query cache. The updated query may be executed and an output of the executed updated query may be transmitted to the user device.

Abstract: Systems, computer program products, and methods are described herein for evaluating, validating, correcting, and loading data feeds based on artificial intelligence input. The present invention may be configured to receive a data feed from a source for loading to a target data structure, analyze, based on historical feed data, metadata of the data feed to determine a likelihood of the data feed failing to load, and determine whether the likelihood of the data feed failing to load satisfies a threshold. The present invention may be configured to load the data feed to the target data structure, determine, after loading the data feed to the target data structure, whether the data feed failed to load, and either correct errors in the data feed or add error-containing portions of the data feed to a failed data log.

Abstract: A device configured to identify a first link between a value of a first data element in a first plurality of data elements and values of a first set of data elements in a second plurality of data elements and to remove the first link between the first data element and the first set of data elements. The device is further configured to input the data elements into a machine learning model that is configured to output a second link between the first data element and a second set of data elements. The device is further configured to create an entry in a relationship table that identifies the first data element and the second set of data elements. The device is further configured to generate a data stream with the first data element and the second set of data elements and to output the data stream.

Abstract: Systems, computer program products, and methods are described herein for monitoring and automatically controlling batch processing. The present invention may be configured to receive a plurality of data processing requests and determine a processing plan for the plurality of data processing requests. The present invention may be configured to provide, to processing applications and based on the processing plan, actions for performance by the processing applications to complete the plurality of data processing requests. The present invention may be configured to predict, while the processing applications are performing the actions and using a completion time predicting machine learning model, completion times for the plurality of data processing requests.

Abstract: Systems, computer program products, and methods are described herein for evaluating, validating, correcting, and loading data feeds based on artificial intelligence input. The present invention may be configured to receive a data feed from a source for loading to a target data structure, analyze, based on historical feed data, metadata of the data feed to determine a likelihood of the data feed failing to load, and determine whether the likelihood of the data feed failing to load satisfies a threshold. The present invention may be configured to load the data feed to the target data structure, determine, after loading the data feed to the target data structure, whether the data feed failed to load, and either correct errors in the data feed or add error-containing portions of the data feed to a failed data log.

Abstract: Aspects of the disclosure relate to management of databases in different server environments. In particular, various aspects of this disclosure relate to correction, synchronization, and/or migration of databases between different database servers. A feed file that is rejected from loading in a database associated with a source server may prioritized in a destination server. A feed file hierarchy of the rejected feed file may be determined and the destination server may process loading of the rejected feed file to a database based on the determine feed file hierarchy. Any corrections applied at the destination server may also be applied at the source server.

Abstract: A system accesses a set of devices transferring a data element from a source device to a destination device. The system determines a transformation type implemented on the data element at each device. The system generates an array that uniquely defines the data element at each device. The array comprises the transformation type and an identifier of the device. The system generates a transformation dependency map that represents a set of transformation types implemented on the data element at different devices. The system determines a set of data transfer paths for the data element from the source device to the destination device based on the transformation dependency map. The system selects the shortest data transfer path for the data element that corresponds to the least number of hops between devices. The system communicates the data element from the source device to the destination device using the shortest data transfer path.

Abstract: A system accesses a set of devices transferring a plurality of data elements from a source device to a destination device. The system determines that a first subset of data elements from among the plurality of data elements is transformed in a first subset of devices. The system determines that a second subset of data elements from among the plurality of data elements is transformed in a second subset of devices. The system splits the plurality of data elements into the first subset of data elements and the second subset of data elements. The system communicates the first subset of data elements using a first transfer path through the first subset of devices. The system communicates the second subset of data elements using a second transfer path through the second subset of devices.

Abstract: A system accesses a set of devices transferring a data element from a source device to a destination device. The system determines a transformation type implemented on the data element at each device. The system generates an array that uniquely defines the data element at each device. The array comprises the transformation type and an identifier of the device. The system generates a transformation dependency map that represents a set of transformation types implemented on the data element at different devices. The system determines a set of data transfer paths for the data element from the source device to the destination device based on the transformation dependency map. The system selects the shortest data transfer path for the data element that corresponds to the least number of hops between devices. The system communicates the data element from the source device to the destination device using the shortest data transfer path.

Abstract: A device configured to identify a first link between a value of a first data element in a first plurality of data elements and values of a first set of data elements in a second plurality of data elements and to remove the first link between the first data element and the first set of data elements. The device is further configured to input the data elements into a machine learning model that is configured to output a second link between the first data element and a second set of data elements. The device is further configured to create an entry in a relationship table that identifies the first data element and the second set of data elements. The device is further configured to generate a data stream with the first data element and the second set of data elements and to output the data stream.

Abstract: Systems, computer program products, and methods are described herein for monitoring and automatically controlling batch processing. The present invention may be configured to receive a plurality of data processing requests and determine a processing plan for the plurality of data processing requests. The present invention may be configured to provide, to processing applications and based on the processing plan, actions for performance by the processing applications to complete the plurality of data processing requests. The present invention may be configured to determine a state of the plurality of data processing requests, determine, using an event state decision machine learning model, remedial actions to resolve an error state, and provide instructions to the processing applications to perform the remedial actions.

Abstract: Systems for dynamic data reorganization and query optimization are provided. In some aspects, a database query may be received and evaluated to determine whether it meets one or more criteria. If the database query meets the one or more criteria, the query may be executed and the output of the executed database may be transmitted to the user device. If the database query does not meet the one or more criteria, data associated with the database query may be dynamically modified and/or reorganized (e.g., using machine learning). Modified data may be generated based on the dynamically modified and/or reorganized data. The modified data may be stored in an optimized query cache. An updated query may be generated based on the modified data and stored in the optimized query cache. The updated query may be executed and an output of the executed updated query may be transmitted to the user device.

Abstract: Aspects of the disclosure relate to management of databases in different server environments. In particular, various aspects of this disclosure relate to correction, synchronization, and/or migration of databases between different database servers. A feed file that is rejected from loading in a database associated with a source server may prioritized in a destination server. A feed file hierarchy of the rejected feed file may be determined and the destination server may process loading of the rejected feed file to a database based on the determine feed file hierarchy. Any corrections applied at the destination server may also be applied at the source server.

Abstract: Systems, computer program products, and methods are described herein for monitoring and automatically controlling batch processing. The present invention may be configured to receive a plurality of data processing requests and determine a processing plan for the plurality of data processing requests. The present invention may be configured to provide, to processing applications and based on the processing plan, actions for performance by the processing applications to complete the plurality of data processing requests. The present invention may be configured to determine a state of the plurality of data processing requests, determine, using an event state decision machine learning model, remedial actions to resolve an error state, and provide instructions to the processing applications to perform the remedial actions.

Abstract: Systems, computer program products, and methods are described herein for monitoring and automatically controlling batch processing. The present invention may be configured to receive a plurality of data processing requests and determine a processing plan for the plurality of data processing requests. The present invention may be configured to provide, to processing applications and based on the processing plan, actions for performance by the processing applications to complete the plurality of data processing requests. The present invention may be configured to predict, while the processing applications are performing the actions and using a completion time predicting machine learning model, completion times for the plurality of data processing requests.

Abstract: Aspects of the disclosure relate to management of databases in different server environments. In particular, various aspects of this disclosure relate to correction, synchronization, and/or migration of databases between different database servers. A feed file that is rejected from loading in a database associated with a source server may prioritized in a destination server. A feed file hierarchy of the rejected feed file may be determined and the destination server may process loading of the rejected feed file to a database based on the determine feed file hierarchy. Any corrections applied at the destination server may also be applied at the source server.

Abstract: Aspects of the disclosure relate to management of databases in different server environments. In particular, various aspects of this disclosure relate to correction, synchronization, and/or migration of databases between different database servers. A feed file that is rejected from loading in a database associated with a source server may prioritized in a destination server. A feed file hierarchy of the rejected feed file may be determined and the destination server may process loading of the rejected feed file to a database based on the determine feed file hierarchy. Any corrections applied at the destination server may also be applied at the source server.

Abstract: Aspects of the disclosure relate to management of databases in different server environments. In particular, various aspects of this disclosure relate to correction, synchronization, and/or migration of databases between different database servers. A feed file that is rejected from loading in a database associated with a source server may prioritized in a destination server. A feed file hierarchy of the rejected feed file may be determined and the destination server may process loading of the rejected feed file to a database based on the determine feed file hierarchy. Any corrections applied at the destination server may also be applied at the source server.

Abstract: Aspects of the disclosure relate to management of databases in different server environments. In particular, various aspects of this disclosure relate to correction, synchronization, and/or migration of databases between different database servers. A feed file that is rejected from loading in a database associated with a source server may prioritized in a destination server. A feed file hierarchy of the rejected feed file may be determined and the destination server may process loading of the rejected feed file to a database based on the determine feed file hierarchy. Any corrections applied at the destination server may also be applied at the source server.