SISAP 2023 Indexing challenge –⁠ Learned Metric Index: Raw data, analyses, figures

==== For complete code, description, data, and steps to reproduce, visit: https://github.com/LearnedMetricIndex/LearnedMetricIndex/tree/paper-sisap23-indexing-challenge ==== This repository contains the data for our submission to the SISAP 2023 Indexing challenge. We used a strip-down version of the Learned Metric Index (LMI), which is an index for approximate nearest neighbor search on complex data using machine learning and probability-based navigation. **Getting started** Follow the instructions in README.md –⁠ https://github.com/LearnedMetricIndex/LearnedMetricIndex/tree/paper-sisap23-indexing-challenge **Contents** 1. result/ - contains the raw .h5 files of each experiment (with varying hyperparameters), 2088 experiment in total 2. res.csv - contains the evaluation of every experiment (1 row) in terms of recall and query time 3. 02-Analyze-results.ipynb - Jupyter notebook used to analyze the results and plot the figures 4. cat.pdf, nobjects.pdf - figures used in the paper **Related Publications** > M. Antol, J. Ol'ha, T. Slanináková, V. Dohnal: [Learned Metric Index—Proposition of learned indexing for unstructured data](https://www.sciencedirect.com/science/article/pii/S0306437921000326?casa_token=EvG8iaWkqQUAAAAA:xgfbutrsNGcBXnTN-U4MQ65hgmPE3fAyzwqtijzGC-JRrkO1IYNmcN3A8yMsSOT3CCoHpqVtMA). Information Systems, 2021 - Elsevier (2021) > T. Slanináková, M. Antol, J. Ol'ha, V. Kaňa, V. Dohnal: [Learned Metric Index—Proposition of learned indexing for unstructured data](https://link.springer.com/chapter/10.1007/978-3-030-89657-7_7). SISAP 2021 - Similarity Search and Applications pp 81-94 (2021) > J. Ol'ha, T. Slanináková, M. Gendiar, M. Antol, V. Dohnal: [Learned Indexing in Proteins: Extended Work on Substituting Complex Distance Calculations with Embedding and Clustering Techniques](https://arxiv.org/abs/2208.08910), and [Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques](https://link.springer.com/chapter/10.1007/978-3-031-17849-8_22) SISAP 2022 - Similarity Search and Applications pp 274-282 (2022) > T. Slanináková, M. Antol, J. Ol'ha, V. Kaňa, V. Dohnal, S. Ladra, M. A. Martinez-Prieto: [Reproducible experiments with Learned Metric Index Framework](https://www.sciencedirect.com/science/article/pii/S0306437923000911). Information Systems, Volume 118, September 2023, 102255 (2023) **Mendeley dataset**: https://data.mendeley.com/datasets/8wp73zxr47/12 ** Authors** - Terézia Slanináková, Masaryk University - David Procházka, Masaryk University - Jaroslav Oľha, Masaryk University - Matej Antol, Masaryk University - Vlastislav Dohnal, Masaryk University