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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.21682 |
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| _version_ | 1866915470364377088 |
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| author | Imamura, Yasunobu Shinohara, Takeshi Higuchi, Naoya Hirata, Kouichi Kuboyama, Tetsuji |
| author_facet | Imamura, Yasunobu Shinohara, Takeshi Higuchi, Naoya Hirata, Kouichi Kuboyama, Tetsuji |
| contents | We report our participation in the SISAP 2025 Indexing Challenge using a novel indexing technique called the Hilbert forest. The method is based on the fast Hilbert sort algorithm, which efficiently orders high-dimensional points along a Hilbert space-filling curve, and constructs multiple Hilbert trees to support approximate nearest neighbor search. We submitted implementations to both Task 1 (approximate search on the PUBMED23 dataset) and Task 2 (k-nearest neighbor graph construction on the GOOAQ dataset) under the official resource constraints of 16 GB RAM and 8 CPU cores. The Hilbert forest demonstrated competitive performance in Task 1 and achieved the fastest construction time in Task 2 while satisfying the required recall levels. These results highlight the practical effectiveness of Hilbert order-based indexing under strict memory limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21682 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hilbert Forest in the SISAP 2025 Indexing Challenge Imamura, Yasunobu Shinohara, Takeshi Higuchi, Naoya Hirata, Kouichi Kuboyama, Tetsuji Databases Data Structures and Algorithms We report our participation in the SISAP 2025 Indexing Challenge using a novel indexing technique called the Hilbert forest. The method is based on the fast Hilbert sort algorithm, which efficiently orders high-dimensional points along a Hilbert space-filling curve, and constructs multiple Hilbert trees to support approximate nearest neighbor search. We submitted implementations to both Task 1 (approximate search on the PUBMED23 dataset) and Task 2 (k-nearest neighbor graph construction on the GOOAQ dataset) under the official resource constraints of 16 GB RAM and 8 CPU cores. The Hilbert forest demonstrated competitive performance in Task 1 and achieved the fastest construction time in Task 2 while satisfying the required recall levels. These results highlight the practical effectiveness of Hilbert order-based indexing under strict memory limitations. |
| title | Hilbert Forest in the SISAP 2025 Indexing Challenge |
| topic | Databases Data Structures and Algorithms |
| url | https://arxiv.org/abs/2508.21682 |