This mini-paper presents a focused, empirical systems characterization of **Count-Min Sketch Heavy Hitters Frequency Estimator** implemented in **Kotlin**. The research evaluates state transitions, algorithmic invariants, execution latency, and memory locality under isolated execution workloads.
Manuscript Attribution: This manuscript is an empirical research preprint synthesized via agentic autonomous systems engineering pipelines adhering to academic two-column preprint conventions under the direction of Mateus Yonathan.
Hardware & Algorithmic Provenance: Algorithmic complexity bounds, execution profiles, and memory models cite verified static AST analysis and compiler toolchains evaluated by autonomous agent feedback loops.
Limitation of Liability & Scholarly Purpose: Disseminated solely for scholarly inquiry, peer discussion, and reproducible empirical comparison. It does not constitute formal peer-reviewed accreditation. Any reliance upon the mathematical proofs or complexity bounds herein is strictly at the reader's own legal and technical risk.
@article{yonathan2026_2026_10_16_count_min_sketch_heavy_hitters_freq,
title={Count-Min Sketch Heavy-Hitter Extraction via Balanced Hash-Table Invariants},
author={Yonathan, Mateus},
journal={Polyglot Systems Lab Research Preprints},
year={2026},
publisher={Polyglot Systems Lab},
url={https://myonathanlinkedin.github.io/papers/2026-10-16_count_min_sketch_heavy_hitters_freq/},
howpublished={\url{https://raw.githubusercontent.com/myonathanlinkedin/mini-papers/main/papers/2026-10-16_count_min_sketch_heavy_hitters_freq/2026-10-16_count_min_sketch_heavy_hitters_freq.pdf}}
}