Abstract
The present disclosure provides an adaptive orchestration framework for optimizing data transfer between enterprise sourcing systems and financial databases through a middleware integration layer. The framework employs an AI-driven optimization engine that analyzes business metadata from sourcing platforms, system telemetry from middleware, and transaction feedback from the database. A hybrid scoring algorithm computes a PriorityScore used to dynamically reconfigure middleware parameters, including thread concurrency, queue thresholds, and data routing, without interrupting ongoing data flows. The framework maintains consistency between forward and reverse synchronization through a Sync Controller that coordinates bidirectional data flows based on forward-flow completion and load metrics. The framework logs every decision for auditability through an audit logger and self-learns parameters through a continuous monitoring and learning component that refines weightings and thresholds using historical data.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
SINHA, KIRTEE SHREE; DAS, PRASENJIT; and PANDIAN, RAMADURAI, "SYSTEM AND METHOD FOR ADAPTIVE DATA FLOW OPTIMIZATION FOR MIDDLEWARE INTEGRATION", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11328