Abstract
In this short paper, we outline the idea of applying the concept of a learned index structure to approximate nearest neighbor query processing. We discuss different data partitioning approaches and show how the task of identifying the disc pages of potential hits for a given query can be solved by a predictive machine learning model. In a preliminary experimental case study we evaluate and discuss the general applicability of different partitioning approaches as well as of different predictive models.
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Hünemörder, M., Kröger, P., Renz, M. (2021). Towards a Learned Index Structure for Approximate Nearest Neighbor Search Query Processing. In: Reyes, N., et al. Similarity Search and Applications. SISAP 2021. Lecture Notes in Computer Science(), vol 13058. Springer, Cham. https://doi.org/10.1007/978-3-030-89657-7_8
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