Author Identifier (ORCID)
Abstract
Learned indexes leverage machine learning models to approximate data distributions and predict key positions, offering better performance than traditional index structures such as B+Trees. As data in real-world applications evolve rapidly, the timely and efficient updating of learned indexes has become an increasingly important research problem, attracting growing attention in recent studies. However, under update-intensive workloads with frequent insertions and deletions, existing learned indexes cannot update the model in a timely manner. Moreover, they ignore the impact of deletions on model accuracy. These limitations lead to degraded prediction accuracy and increased query latency, undermining the core advantage of learned indexes. To fill in this gap, in this paper, we design a dynamic learned index (denoted as DynaMind) that is able to timely update the model with the frequent change of data. Specifically, we propose a novel score function to determine the appropriate timing at which a learned index should initiate an update by measuring the influence of updated data on the model accuracy. To enable efficient model updates, we devise a timely learned index update algorithm that implements both lightweight incremental learning for insertions and machine unlearning for deletions together, ensuring the model continuously evolves without full retraining. Extensive experiments on real-world and synthetic datasets show that DynaMind achieves competitive throughput compared to state-of-the-art works while improving the prediction accuracy. The proportion of keys with zero prediction error increases by more than 10% after updates.
Keywords
dynamic learned index, frequent update, incremental learning, unlearning
Document Type
Journal Article
Date of Publication
8-3-2026
Article Number
116357
ISSN
09507051
Volume
348
Publication Title
Knowledge-Based Systems
Publisher
Elsevier
School
School of Business and Law
Funding Information
Supported by the Fundamental Research Funds for the Central Universities, CHD under grant 300102405105.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Cheng, J., Wang, Y., Zhu, T., Yang, X., Cui, N., & Li, J. (2026). DynaMind: A dynamic learned index for update-intensive workloads. Knowledge-Based Systems, 348, 116357. https://doi.org/10.1016/j.knosys.2026.116357