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@article{189997,
        author = {NIMISHA A. MODI},
        title = {The Evolution of Tree-Based Indexing: From Static Structures to Adaptive Traversal},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {2551-2556},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189997},
        abstract = {Tree-based index structures such as B-Trees and B+ Trees serve as the backbone of database indexing due to their robustness and predictable performance, yet their root-to-leaf traversal strategies remain largely static despite increasing workload skew and evolving hardware architectures. Recent advances in compression-aware indexing, persistent memory optimization, and learned index models have improved space efficiency and access latency, but they either focus on node-level optimizations or attempt to replace traditional structures entirely, often at the cost of system reliability and adaptability. This paper presents a comprehensive survey of these developments and identifies static traversal logic as an underexplored performance bottleneck in modern database systems. To address this limitation, this paper conceptually proposes an adaptive traversal model that augments a traditional B+ Tree with a lightweight, workload-aware learning component to predict likely leaf destinations. The model employs confidence-based routing with safe fallback to standard traversal. The study highlights how such an approach can reduce CPU overhead and tail latency, offering a promising direction for intelligent indexing.},
        keywords = {Adaptive Traversal, B -Tree, B+ Tree, Database Indexing, Learned Indexes},
        month = {February},
        }