ENGINEERING REPRODUCIBLE DATA FOUNDATIONS FOR  TRUSTWORTHY ENTERPRISE ANALYTICS: A VERSION-AWARE  FRAMEWORK FOR ANALYTICAL STATE RECONSTRUCTION,  VALIDATION, AND AUTONOMOUS RECOVERY

Chalapathi Koneni ,Venkata Ratna Kumar Bonagiri

ENGINEERING REPRODUCIBLE DATA FOUNDATIONS FOR TRUSTWORTHY ENTERPRISE ANALYTICS: A VERSION-AWARE FRAMEWORK FOR ANALYTICAL STATE RECONSTRUCTION, VALIDATION, AND AUTONOMOUS RECOVERY

Authors

Chalapathi Koneni ,Venkata Ratna Kumar Bonagiri

Keywords:

Reproducible Analytics; Analytical State Management; Version-Aware Data Foundations; Enterprise Data Platforms; Data Lineage; Data Versioning; Analytical State Reconstruction; Autonomous Recovery; DataOps; Machine Learning-Based Recovery; Data Governance; Lakehouse Architecture

Abstract

Reproduction of enterprise analytics is problematicdue to the constantly changing data sources, logic of transformation, semantic definitions and executionenvironments. In this study, an analytical frameworkbased on version awareness is suggested, whichrepresents analytical states by an identity of thesources, versions of a particular transformation,dataset releases

References

[1]Kavuluri, H.V.R., 2021. Distributed Feature Stores for Machine Learning Pipelines with Versioned Synchronization. Cross-Disciplinary Knowledge Systems, 8, pp.9-16.

[2]Kunadi, S.K., 2021. Establishing robust data foundations: Early-stage architecture for scalable data warehousing and analytics systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(3), pp.3078-3088

Downloads

Published

2024-10-15

How to Cite

Chalapathi Koneni ,Venkata Ratna Kumar Bonagiri. (2024). ENGINEERING REPRODUCIBLE DATA FOUNDATIONS FOR TRUSTWORTHY ENTERPRISE ANALYTICS: A VERSION-AWARE FRAMEWORK FOR ANALYTICAL STATE RECONSTRUCTION, VALIDATION, AND AUTONOMOUS RECOVERY . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 9661–9674. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5817