In any sufficiently complex system, such as any vast financial system with any practical user base, ensuring data integrity across K data points is crucial.Furthermore, to satisfy correctness during usage, we should ensure that kn data points are verified and corrected for consumption. At the same time, K is correct as a whole, without any discrepancies in expected values.Values falling out of valid ranges can cause distress among consumers of this data, leading to questions directed at platform designers, resulting in wasted time trying to recover original data points used, mitigating data loss, and validating inputs across I indicators where I am the number of total indicators used across the platform. (An indicator is a set of kn con-figured in a way that an operation produces a resultconsumed by the end-consumer.)To address these issues, we propose implementing an algorithm that relies on the classical Lagrange Interpolation to help maintain correctness in our set of by using polynomials through known data points.
Available on ResearchGate as well.