Bank of America Corporation

Vijaya Kumar Vegulla

Venugopala Rao Randhi

Rama Venkata S Kavali

Damodarrao Thakkalapelli

Netla Hanumantha Reddy

Nagendra B Grandhye

Rajasekhar Maramreddy

Sandeep D'Souza

Kumar Mahadeva Setty

Anil Kumar Venkata Kalyanam

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

An illustrative computing system for a dynamic user access control management system classifies users and data resources according to their risk and importance by a user management engine with artificial intelligence, machine learning characteristics. The dynamic user access control management system analyzes the log files of data resources to measure system performance characteristics and user access behavior. This system monitors the device and network by which a data access request to a data resource is made. The dynamic user access control management system validates the leave status of a user initiating a data access request. The dynamic user access control management system automatically determines a user access level for a data resource through intelligent analysis of collected information and defers to a user's manager for an access level determination when the determination to grant an access level is outside of the knowledge base of the user management engine.

Dynamic user access control management

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

Aspects described herein may relate to a data processing engine that executes on a computing device in order to store data from one or more feed files, which may be heterogeneous, to a destination data structure on a designated computing device. Because the files may be huge in size, it is important that the files be stored in a manner in order to reduce the time to move the data and to support an efficient mechanism for recovering from errors. A feed file may be dynamically partitioned into groups of contiguous rows based on a dynamic partitioning key, where data chunks are loaded into a plurality of clone tables and subsequently moved into a destination data structure. The data processing engine may determine a row size for the clone files and request for resources from a computing cloud to obtain those resources.

Dynamic server pool data segmentation using dynamic ordinal partition key without locks

Aspects described herein may relate to a data processing engine that executes on a computing device in order to store data from one or more feed files, which may be heterogeneous, to a destination data structure on a designated computing device. Because the files may be huge in size, it is important that the files be stored in a manner in order to reduce the time to move the data and to support am efficient mechanism for recovering from errors. A feed file may be dynamically partitioned into groups of contiguous rows based on a dynamic partitioning key, where data chunks are loaded into a plurality of clone tables and subsequently moved into a destination data structure. The data processing engine may determine a row size for the clone files and request for resources from a computing cloud to obtain those resources.

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Hyderabad Telangana, India

Vijaya Kumar Vegulla