{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:YFKWMJQXV7KLSZ5SHCSMMSSNT3","short_pith_number":"pith:YFKWMJQX","canonical_record":{"source":{"id":"1509.06957","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-23T13:10:36Z","cross_cats_sorted":["cs.DS","cs.LG"],"title_canon_sha256":"8d01776f08848c5abecfa0c55cecbdb72c39d244814025305115e428aa4771be","abstract_canon_sha256":"8313e18fea6c84fd0425917681f55fb53a85ee5de7603cb422a8b00ea6928d64"},"schema_version":"1.0"},"canonical_sha256":"c155662617afd4b967b238a4c64a4d9efb3cf210089649ad62f4d40986cb80b4","source":{"kind":"arxiv","id":"1509.06957","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.06957","created_at":"2026-05-17T23:48:02Z"},{"alias_kind":"arxiv_version","alias_value":"1509.06957v2","created_at":"2026-05-17T23:48:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.06957","created_at":"2026-05-17T23:48:02Z"},{"alias_kind":"pith_short_12","alias_value":"YFKWMJQXV7KL","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"YFKWMJQXV7KLSZ5S","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"YFKWMJQX","created_at":"2026-05-18T12:29:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:YFKWMJQXV7KLSZ5SHCSMMSSNT3","target":"record","payload":{"canonical_record":{"source":{"id":"1509.06957","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-23T13:10:36Z","cross_cats_sorted":["cs.DS","cs.LG"],"title_canon_sha256":"8d01776f08848c5abecfa0c55cecbdb72c39d244814025305115e428aa4771be","abstract_canon_sha256":"8313e18fea6c84fd0425917681f55fb53a85ee5de7603cb422a8b00ea6928d64"},"schema_version":"1.0"},"canonical_sha256":"c155662617afd4b967b238a4c64a4d9efb3cf210089649ad62f4d40986cb80b4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:02.585601Z","signature_b64":"9iYCQLt2guuQMOJQQSyXOfmrSx22xaCxkBKm0sxKoTUJv+Eyp7Chvg2UbQLB06IhVSlBr4q8r+WlX5RmafwUAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c155662617afd4b967b238a4c64a4d9efb3cf210089649ad62f4d40986cb80b4","last_reissued_at":"2026-05-17T23:48:02.585077Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:02.585077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1509.06957","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gGbkkqLGXIYeq/mtMpeE7ENvP74T8/rvEtsB8Zqk1UeI2edZNlqSHZZ4qOZFX4GwqCtkGEiCQ0EKNI2qyeEkBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T22:08:22.971579Z"},"content_sha256":"f595304f906af5d8e51677eb59b1c5d8ffc9e4bd1a2c9b66d396773eba21432b","schema_version":"1.0","event_id":"sha256:f595304f906af5d8e51677eb59b1c5d8ffc9e4bd1a2c9b66d396773eba21432b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:YFKWMJQXV7KLSZ5SHCSMMSSNT3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast k-NN search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","cs.LG"],"primary_cat":"stat.ML","authors_text":"Elias J\\\"a\\\"asaari, Jukka Corander, Liang Wang, Risto Tuomainen, Sotiris Tasoulis, Teemu Pitk\\\"anen, Teemu Roos, Ville Hyv\\\"onen","submitted_at":"2015-09-23T13:10:36Z","abstract_excerpt":"Efficient index structures for fast approximate nearest neighbor queries are required in many applications such as recommendation systems. In high-dimensional spaces, many conventional methods suffer from excessive usage of memory and slow response times. We propose a method where multiple random projection trees are combined by a novel voting scheme. The key idea is to exploit the redundancy in a large number of candidate sets obtained by independently generated random projections in order to reduce the number of expensive exact distance evaluations. The method is straightforward to implement"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.06957","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9eqDAPlb5m9hSbkHE+GW3C6wkC2hVFl8JwQcibKjPuueQEMIlnqWYqbKk86M50RKkCFVtMiB8a5dG8TNh7kCCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T22:08:22.972267Z"},"content_sha256":"c45fd4903bad4b4abc8dd081a8d89d656dc0196a70e9d2e7e0dba15a2b5eeb75","schema_version":"1.0","event_id":"sha256:c45fd4903bad4b4abc8dd081a8d89d656dc0196a70e9d2e7e0dba15a2b5eeb75"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YFKWMJQXV7KLSZ5SHCSMMSSNT3/bundle.json","state_url":"https://pith.science/pith/YFKWMJQXV7KLSZ5SHCSMMSSNT3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YFKWMJQXV7KLSZ5SHCSMMSSNT3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T22:08:22Z","links":{"resolver":"https://pith.science/pith/YFKWMJQXV7KLSZ5SHCSMMSSNT3","bundle":"https://pith.science/pith/YFKWMJQXV7KLSZ5SHCSMMSSNT3/bundle.json","state":"https://pith.science/pith/YFKWMJQXV7KLSZ5SHCSMMSSNT3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YFKWMJQXV7KLSZ5SHCSMMSSNT3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:YFKWMJQXV7KLSZ5SHCSMMSSNT3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8313e18fea6c84fd0425917681f55fb53a85ee5de7603cb422a8b00ea6928d64","cross_cats_sorted":["cs.DS","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-23T13:10:36Z","title_canon_sha256":"8d01776f08848c5abecfa0c55cecbdb72c39d244814025305115e428aa4771be"},"schema_version":"1.0","source":{"id":"1509.06957","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.06957","created_at":"2026-05-17T23:48:02Z"},{"alias_kind":"arxiv_version","alias_value":"1509.06957v2","created_at":"2026-05-17T23:48:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.06957","created_at":"2026-05-17T23:48:02Z"},{"alias_kind":"pith_short_12","alias_value":"YFKWMJQXV7KL","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"YFKWMJQXV7KLSZ5S","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"YFKWMJQX","created_at":"2026-05-18T12:29:50Z"}],"graph_snapshots":[{"event_id":"sha256:c45fd4903bad4b4abc8dd081a8d89d656dc0196a70e9d2e7e0dba15a2b5eeb75","target":"graph","created_at":"2026-05-17T23:48:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Efficient index structures for fast approximate nearest neighbor queries are required in many applications such as recommendation systems. In high-dimensional spaces, many conventional methods suffer from excessive usage of memory and slow response times. We propose a method where multiple random projection trees are combined by a novel voting scheme. The key idea is to exploit the redundancy in a large number of candidate sets obtained by independently generated random projections in order to reduce the number of expensive exact distance evaluations. The method is straightforward to implement","authors_text":"Elias J\\\"a\\\"asaari, Jukka Corander, Liang Wang, Risto Tuomainen, Sotiris Tasoulis, Teemu Pitk\\\"anen, Teemu Roos, Ville Hyv\\\"onen","cross_cats":["cs.DS","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-23T13:10:36Z","title":"Fast k-NN search"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.06957","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f595304f906af5d8e51677eb59b1c5d8ffc9e4bd1a2c9b66d396773eba21432b","target":"record","created_at":"2026-05-17T23:48:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8313e18fea6c84fd0425917681f55fb53a85ee5de7603cb422a8b00ea6928d64","cross_cats_sorted":["cs.DS","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-23T13:10:36Z","title_canon_sha256":"8d01776f08848c5abecfa0c55cecbdb72c39d244814025305115e428aa4771be"},"schema_version":"1.0","source":{"id":"1509.06957","kind":"arxiv","version":2}},"canonical_sha256":"c155662617afd4b967b238a4c64a4d9efb3cf210089649ad62f4d40986cb80b4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c155662617afd4b967b238a4c64a4d9efb3cf210089649ad62f4d40986cb80b4","first_computed_at":"2026-05-17T23:48:02.585077Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:02.585077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9iYCQLt2guuQMOJQQSyXOfmrSx22xaCxkBKm0sxKoTUJv+Eyp7Chvg2UbQLB06IhVSlBr4q8r+WlX5RmafwUAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:02.585601Z","signed_message":"canonical_sha256_bytes"},"source_id":"1509.06957","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f595304f906af5d8e51677eb59b1c5d8ffc9e4bd1a2c9b66d396773eba21432b","sha256:c45fd4903bad4b4abc8dd081a8d89d656dc0196a70e9d2e7e0dba15a2b5eeb75"],"state_sha256":"eee3c10a9ec3a6cc9eaf812e81c487283228eac144a5cdd76203942dcab5bfdb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wnor0Ii16xOPOfZoPbCdxCybd8SF3CDcKOdc80G2IYH5548CRt+Uslu0E6tcB/LtFLpkR+nOqCqU1tjI87vPBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T22:08:22.976560Z","bundle_sha256":"ef24800c51c9e3f93999b21b2c2ad5c25f194b051c4a4f821bc6c20d54ad163a"}}