Bank of America Corporation

Rama Venkata S Kavali

Venugopala Rao Randhi

Damodarrao Thakkalapelli

Vijaya Kumar Vegulla

Ravindra Dabbiru

Netla Hanumantha Reddy

Aditya Kulkarni

Nagendra B Grandhye

Rajasekhar Maramreddy

Lawrence Anthony D'Silva

Jyothi Gaddam

Lawrence D'silva

Lawrence D′silva

Saritha Talluri

Manohar Aedma

Mohan Varma Kalidindi

Systems, computer program products, and methods are described herein for asynchronous data migration. The present invention is configured to identify a source file data based on an update trigger for the source file data; identify, using an artificial intelligence (AI) engine, a source file type for the source file data; determine a dataset range for the source file data to ingest to a target data file, wherein the dataset range comprises a portion of data of the source file data; and ingest the dataset range to the target data file.

Systems and methods for asynchronous data migration

In conventional networked computing systems, each system may have many atomic values shared across multiple application servers. Each atomic value may require different levels of computational resources to store, maintain, and access, and may provide a different level of usefulness within the computing system. As such, a need exists for a system of improving the efficacy of each data element with a neural network or networked computing system. The system provided herein solves with problem by identifying each data element within a computing system; crawling multiple application servers to collect usage values for each data element; calculating a computational weightage of each data elements, identifying critical values; and creating hash values for each critical value.

System and method for improving the efficacy of a data element within a neural network

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.

Systems and methods for evaluating, validating, correcting, and loading data feeds based on artificial intelligence input

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.

Data flow control and routing using machine learning

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.

System and method for splitting data elements for data communication based on transformation types implemented on the data elements at different devices

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.

Multicomputer system with machine learning engine for query optimization and dynamic data reorganization

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.

Electronic system for monitoring and automatically controlling batch processing

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.

Correction, synchronization, and migration of databases

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.

System and method for determining the shortest data transfer path in data communication

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.

A method includes receiving a plurality of data processing requests and generating a primary processing stack indicating a queue for processing the first data. The primary processing stack comprises a plurality of layers. Each layer comprises a plurality of slices, wherein each slice represents a portion of the first data of at least one data processing request. The plurality of slices are arranged within each layer based at least on the priority indicator corresponding to the first data that each slice represents. The method further includes receiving resource information about a plurality of servers, assigning each slice of the primary processing stack to one of the servers, and sending processing instructions comprising an identification of each slice of the primary processing stack assigned to the respective server.

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Frisco, TX, United States of America

Rama Venkata S Kavali