{"ID":2876802,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.21682","arxiv_id":"2508.21682","title":"Hilbert Forest in the SISAP 2025 Indexing Challenge","abstract":"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.","short_abstract":"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 appr...","url_abs":"https://arxiv.org/abs/2508.21682","url_pdf":"https://arxiv.org/pdf/2508.21682v1","authors":"[\"Yasunobu Imamura\",\"Takeshi Shinohara\",\"Naoya Higuchi\",\"Kouichi Hirata\",\"Tetsuji Kuboyama\"]","published":"2025-08-29T14:51:13Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.DS\"]","methods":"[]","has_code":false}