Abstract

This disclosure presents an innovative system and a method designed to efficiently compare vast datasets while optimizing time and cost considerations. Comprising a Learning Module and an Execution Module, the system dynamically determines optimal execution factors, ensuring resource-efficient performance. Challenges posed by extensive data and execution time are tackled by breaking tasks into stages. The system not only identifies impacted columns but also employs a proprietary Execution SQL to overcome limitations in existing techniques. A machine learning-based algorithm enhances the execution process, efficiently detecting missing and mismatched data. This integrated solution redefines data comparison, blending machine learning, advanced SQL, and algorithmic analysis to offer comprehensive insights into discrepancies.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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