On the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Article in Proceedings |
| Conference | Big Data Analytics and Knowledge Discovery 27th International Conference, DaWaK 2025, Bangkok, Thailand, August 25–27, 2025, Proceedings |
| MU Faculty or unit | |
| Citation | |
| web | https://link.springer.com/chapter/10.1007/978-3-032-02215-8_20 |
| Doi | https://doi.org/10.1007/978-3-032-02215-8_20 |
| Keywords | Learned indexing; Dynamization; Dynamic datasets; kNN search ; ANN search |
| Description | One of the main challenges within the growing research area of learned indexing is the lack of adaptability to dynamically expanding datasets . This paper explores the dynamization of a static learned index for complex data through operations such as node splitting and broadening, enabling efficient adaptation to new data. Furthermore, we evaluate the trade-offs between static and dynamic approaches by introducing an amortized cost model to assess query performance in tandem with the build costs of the index structure, enabling experimental determination of when a dynamic learned index outperforms its static counterpart. We apply the dynamization method to a static learned index and demonstrate that its superior scaling quickly surpasses the static implementation in terms of overall costs as the database grows. |
| Related projects: |