{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:UMOGZMQ2FLU6MGTSKTMYC5OOQR","short_pith_number":"pith:UMOGZMQ2","schema_version":"1.0","canonical_sha256":"a31c6cb21a2ae9e61a7254d98175ce847407758dfb258ababc4ea1dccf250263","source":{"kind":"arxiv","id":"1610.02455","version":1},"attestation_state":"computed","paper":{"title":"Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Wei Wang, Wenjie Zhang, Wen Li, Xuemin Lin, Yifang Sun, Ying Zhang","submitted_at":"2016-10-08T00:40:14Z","abstract_excerpt":"Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision. Although many algorithms have been continuously proposed in the literature in the above domains each year, there is no comprehensive evaluation and analysis of their performances.\n  In this paper, we conduct a comprehensive experimental evaluation of many state-of-the-art methods for approximate nearest neighbor search. Our study (1) is cross-disciplinary (i.e., including 16 algorithms in different domains, an"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1610.02455","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2016-10-08T00:40:14Z","cross_cats_sorted":[],"title_canon_sha256":"224c4b962b532bd3243af949d913def9da05e5452bcee517d6ae5cd51b05ae4b","abstract_canon_sha256":"82157c5c397ede9224cc61c53694eeab29a74258cf8db0dcbff5032bb75004fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:02:51.459192Z","signature_b64":"bj0ZnW6xzVNUpy1w1lYAKAPh7ft+ZrP0qSmlWlZbODHTsfkWGIZocLsRktrpcx8Qu80ogB2P3AJXIAfCuucsAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a31c6cb21a2ae9e61a7254d98175ce847407758dfb258ababc4ea1dccf250263","last_reissued_at":"2026-05-18T01:02:51.458783Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:02:51.458783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Wei Wang, Wenjie Zhang, Wen Li, Xuemin Lin, Yifang Sun, Ying Zhang","submitted_at":"2016-10-08T00:40:14Z","abstract_excerpt":"Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision. Although many algorithms have been continuously proposed in the literature in the above domains each year, there is no comprehensive evaluation and analysis of their performances.\n  In this paper, we conduct a comprehensive experimental evaluation of many state-of-the-art methods for approximate nearest neighbor search. Our study (1) is cross-disciplinary (i.e., including 16 algorithms in different domains, an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.02455","kind":"arxiv","version":1},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1610.02455","created_at":"2026-05-18T01:02:51.458845+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.02455v1","created_at":"2026-05-18T01:02:51.458845+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.02455","created_at":"2026-05-18T01:02:51.458845+00:00"},{"alias_kind":"pith_short_12","alias_value":"UMOGZMQ2FLU6","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_16","alias_value":"UMOGZMQ2FLU6MGTS","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_8","alias_value":"UMOGZMQ2","created_at":"2026-05-18T12:30:46.583412+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.07387","citing_title":"The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UMOGZMQ2FLU6MGTSKTMYC5OOQR","json":"https://pith.science/pith/UMOGZMQ2FLU6MGTSKTMYC5OOQR.json","graph_json":"https://pith.science/api/pith-number/UMOGZMQ2FLU6MGTSKTMYC5OOQR/graph.json","events_json":"https://pith.science/api/pith-number/UMOGZMQ2FLU6MGTSKTMYC5OOQR/events.json","paper":"https://pith.science/paper/UMOGZMQ2"},"agent_actions":{"view_html":"https://pith.science/pith/UMOGZMQ2FLU6MGTSKTMYC5OOQR","download_json":"https://pith.science/pith/UMOGZMQ2FLU6MGTSKTMYC5OOQR.json","view_paper":"https://pith.science/paper/UMOGZMQ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.02455&json=true","fetch_graph":"https://pith.science/api/pith-number/UMOGZMQ2FLU6MGTSKTMYC5OOQR/graph.json","fetch_events":"https://pith.science/api/pith-number/UMOGZMQ2FLU6MGTSKTMYC5OOQR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UMOGZMQ2FLU6MGTSKTMYC5OOQR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UMOGZMQ2FLU6MGTSKTMYC5OOQR/action/storage_attestation","attest_author":"https://pith.science/pith/UMOGZMQ2FLU6MGTSKTMYC5OOQR/action/author_attestation","sign_citation":"https://pith.science/pith/UMOGZMQ2FLU6MGTSKTMYC5OOQR/action/citation_signature","submit_replication":"https://pith.science/pith/UMOGZMQ2FLU6MGTSKTMYC5OOQR/action/replication_record"}},"created_at":"2026-05-18T01:02:51.458845+00:00","updated_at":"2026-05-18T01:02:51.458845+00:00"}