{"paper":{"title":"On Optimal Top-K String Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Cheng Sheng, Jeffrey Scott Vitter, Rahul Shah, Sharma V. Thankachan","submitted_at":"2012-07-11T13:30:06Z","abstract_excerpt":"Let ${\\cal{D}}$ = $\\{d_1, d_2, d_3, ..., d_D\\}$ be a given set of $D$ (string) documents of total length $n$. The top-$k$ document retrieval problem is to index $\\cal{D}$ such that when a pattern $P$ of length $p$, and a parameter $k$ come as a query, the index returns the $k$ most relevant documents to the pattern $P$. Hon et. al. \\cite{HSV09} gave the first linear space framework to solve this problem in $O(p + k\\log k)$ time. This was improved by Navarro and Nekrich \\cite{NN12} to $O(p + k)$. These results are powerful enough to support arbitrary relevance functions like frequency, proximit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.2632","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}