Semantic Consistency in Distributed Analytics Systems: A Design Science Approach to Cross-Engine Query Equivalence

Authors

  • Muthupalaniappan Ramanathan

Keywords:

Semantic Consistency, Distributed Analytics, Query Engines, Canonical Representation, Design Science Research

Abstract

Modern analytics systems use multiple, heterogeneous query engines, balancing different requirementsfor performance, scalability, and workload specialization. While the engines share a similar syntax and support dialects of structured query language, they are not semantically equivalent

References

Neoklis Polyzotis et al., "Data Management Challenges in Production Machine Learning," SIGMOD

'17: Proceedings of the 2017 ACM International Conference on Management of Data (May 2017) [Online]. Available: https://dl.acm.org/doi/pdf/10.1145/3035918.3054782

Michael Armbrust et al., "Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics," 11th Annual Conference on Innovative Data Systems Research (CIDR ’21), January 11–15, 2021. [Online]. Available: https://15721.courses.cs.cmu.edu/spring2023/papers/02-modern/armbrust-cidr21.pdf

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Published

2026-04-16

How to Cite

Muthupalaniappan Ramanathan. (2026). Semantic Consistency in Distributed Analytics Systems: A Design Science Approach to Cross-Engine Query Equivalence. Journal of Computational Analysis and Applications (JoCAAA), 35(4), 188–201. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5341

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Section

Articles