{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:Y4WFKWLHOUNCF2XBURKSCOHOR2","short_pith_number":"pith:Y4WFKWLH","canonical_record":{"source":{"id":"1711.01960","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-11-06T15:34:35Z","cross_cats_sorted":[],"title_canon_sha256":"be4c48e109e9b7150fc721b1a7ef9023a4f2bba42f549c667977a4ca85297973","abstract_canon_sha256":"c8283bb10b03a36be9bc09d7784636efda8a4139f8abf405f8a658b21b174d8a"},"schema_version":"1.0"},"canonical_sha256":"c72c555967751a22eae1a4552138ee8e828f7ded92b5d383b81082777853eba3","source":{"kind":"arxiv","id":"1711.01960","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.01960","created_at":"2026-05-17T23:55:59Z"},{"alias_kind":"arxiv_version","alias_value":"1711.01960v4","created_at":"2026-05-17T23:55:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01960","created_at":"2026-05-17T23:55:59Z"},{"alias_kind":"pith_short_12","alias_value":"Y4WFKWLHOUNC","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"Y4WFKWLHOUNCF2XB","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"Y4WFKWLH","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:Y4WFKWLHOUNCF2XBURKSCOHOR2","target":"record","payload":{"canonical_record":{"source":{"id":"1711.01960","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-11-06T15:34:35Z","cross_cats_sorted":[],"title_canon_sha256":"be4c48e109e9b7150fc721b1a7ef9023a4f2bba42f549c667977a4ca85297973","abstract_canon_sha256":"c8283bb10b03a36be9bc09d7784636efda8a4139f8abf405f8a658b21b174d8a"},"schema_version":"1.0"},"canonical_sha256":"c72c555967751a22eae1a4552138ee8e828f7ded92b5d383b81082777853eba3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:59.882127Z","signature_b64":"3ulO/c6t8N0f08EPVFIdGWvCnM+6W5rXcWzQuq5vHsUri4Fx8/gz5hCGq7upXppVxWgqDAkyc0egXpCwT1WRAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c72c555967751a22eae1a4552138ee8e828f7ded92b5d383b81082777853eba3","last_reissued_at":"2026-05-17T23:55:59.881424Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:59.881424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.01960","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:55:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JNx+YrSXeuAttf9dF9f4GfWu8lPMCfQ3Cg0eI+B6e6LFIIUEMeUD4P9HovyecLSH9RsX3td/N6bWdDYGG9yCBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T18:15:51.589957Z"},"content_sha256":"ae0ef09f2b68a3f8fc7a242a7a9e785843d24c5edcde2a74c2ae57e97bac82ab","schema_version":"1.0","event_id":"sha256:ae0ef09f2b68a3f8fc7a242a7a9e785843d24c5edcde2a74c2ae57e97bac82ab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:Y4WFKWLHOUNCF2XBURKSCOHOR2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Iterative Scheme for Leverage-based Approximate Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Hongzhi Wang, Jialin Wan, Jianzhong Li, Shanshan Han","submitted_at":"2017-11-06T15:34:35Z","abstract_excerpt":"The current data explosion poses great challenges to the approximate aggregation with an efficiency and accuracy. To address this problem, we propose a novel approach to calculate the aggregation answers with a high accuracy using only a small portion of the data. We introduce leverages to reflect individual differences in the samples from a statistical perspective. Two kinds of estimators, the leverage-based estimator, and the sketch estimator (a \"rough picture\" of the aggregation answer), are in constraint relations and iteratively improved according to the actual conditions until their diff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.01960","kind":"arxiv","version":4},"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":"2026-05-17T23:55:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qSb0gvjhuDn6bEf/kBcUZrYJE3oTc0R9VZSNUfsseSO6LxzL0LyEozQf83+Nr7doMg1VK2WWQ1ZaskL+AFlBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T18:15:51.590661Z"},"content_sha256":"3c2ee4a3c28b6af7c03893afed3751bdc43f633e3d73edd5cff873dd0381c70a","schema_version":"1.0","event_id":"sha256:3c2ee4a3c28b6af7c03893afed3751bdc43f633e3d73edd5cff873dd0381c70a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/bundle.json","state_url":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/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":"2026-05-25T18:15:51Z","links":{"resolver":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2","bundle":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/bundle.json","state":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:Y4WFKWLHOUNCF2XBURKSCOHOR2","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":"c8283bb10b03a36be9bc09d7784636efda8a4139f8abf405f8a658b21b174d8a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-11-06T15:34:35Z","title_canon_sha256":"be4c48e109e9b7150fc721b1a7ef9023a4f2bba42f549c667977a4ca85297973"},"schema_version":"1.0","source":{"id":"1711.01960","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.01960","created_at":"2026-05-17T23:55:59Z"},{"alias_kind":"arxiv_version","alias_value":"1711.01960v4","created_at":"2026-05-17T23:55:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01960","created_at":"2026-05-17T23:55:59Z"},{"alias_kind":"pith_short_12","alias_value":"Y4WFKWLHOUNC","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"Y4WFKWLHOUNCF2XB","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"Y4WFKWLH","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:3c2ee4a3c28b6af7c03893afed3751bdc43f633e3d73edd5cff873dd0381c70a","target":"graph","created_at":"2026-05-17T23:55:59Z","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":"The current data explosion poses great challenges to the approximate aggregation with an efficiency and accuracy. To address this problem, we propose a novel approach to calculate the aggregation answers with a high accuracy using only a small portion of the data. We introduce leverages to reflect individual differences in the samples from a statistical perspective. Two kinds of estimators, the leverage-based estimator, and the sketch estimator (a \"rough picture\" of the aggregation answer), are in constraint relations and iteratively improved according to the actual conditions until their diff","authors_text":"Hongzhi Wang, Jialin Wan, Jianzhong Li, Shanshan Han","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-11-06T15:34:35Z","title":"An Iterative Scheme for Leverage-based Approximate Aggregation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.01960","kind":"arxiv","version":4},"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:ae0ef09f2b68a3f8fc7a242a7a9e785843d24c5edcde2a74c2ae57e97bac82ab","target":"record","created_at":"2026-05-17T23:55:59Z","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":"c8283bb10b03a36be9bc09d7784636efda8a4139f8abf405f8a658b21b174d8a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-11-06T15:34:35Z","title_canon_sha256":"be4c48e109e9b7150fc721b1a7ef9023a4f2bba42f549c667977a4ca85297973"},"schema_version":"1.0","source":{"id":"1711.01960","kind":"arxiv","version":4}},"canonical_sha256":"c72c555967751a22eae1a4552138ee8e828f7ded92b5d383b81082777853eba3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c72c555967751a22eae1a4552138ee8e828f7ded92b5d383b81082777853eba3","first_computed_at":"2026-05-17T23:55:59.881424Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:55:59.881424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3ulO/c6t8N0f08EPVFIdGWvCnM+6W5rXcWzQuq5vHsUri4Fx8/gz5hCGq7upXppVxWgqDAkyc0egXpCwT1WRAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:55:59.882127Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.01960","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae0ef09f2b68a3f8fc7a242a7a9e785843d24c5edcde2a74c2ae57e97bac82ab","sha256:3c2ee4a3c28b6af7c03893afed3751bdc43f633e3d73edd5cff873dd0381c70a"],"state_sha256":"a968cd35494ac62731b64aa91414aa57d17228bc366b19be702c5569138d27c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BitXn1VgYYpTaTz5/XAFerTnyiW2BLJDlE2kiib4ZyHbv8MFsb71fcndY5dqNr3vOkzflwPIONrR42lNpUVRCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T18:15:51.594354Z","bundle_sha256":"e49535e9cd5a1171551b13b742f91d2ea130e6d2e039fac7c52ec9c1092e1946"}}