Bank of America Corporation

Ravindra Dabbiru

Venugopala Rao Randhi

Rama Venkata S Kavali

Damodarrao Thakkalapelli

Manohar Aedma

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

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.

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Hyderabad Telangana, India

Ravindra Dabbiru