{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:Y4WFKWLHOUNCF2XBURKSCOHOR2","short_pith_number":"pith:Y4WFKWLH","schema_version":"1.0","canonical_sha256":"c72c555967751a22eae1a4552138ee8e828f7ded92b5d383b81082777853eba3","source":{"kind":"arxiv","id":"1711.01960","version":4},"attestation_state":"computed","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"},"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":"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"},"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"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1711.01960","created_at":"2026-05-17T23:55:59.881547+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.01960v4","created_at":"2026-05-17T23:55:59.881547+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01960","created_at":"2026-05-17T23:55:59.881547+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y4WFKWLHOUNC","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y4WFKWLHOUNCF2XB","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y4WFKWLH","created_at":"2026-05-18T12:31:56.362134+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2","json":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2.json","graph_json":"https://pith.science/api/pith-number/Y4WFKWLHOUNCF2XBURKSCOHOR2/graph.json","events_json":"https://pith.science/api/pith-number/Y4WFKWLHOUNCF2XBURKSCOHOR2/events.json","paper":"https://pith.science/paper/Y4WFKWLH"},"agent_actions":{"view_html":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2","download_json":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2.json","view_paper":"https://pith.science/paper/Y4WFKWLH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.01960&json=true","fetch_graph":"https://pith.science/api/pith-number/Y4WFKWLHOUNCF2XBURKSCOHOR2/graph.json","fetch_events":"https://pith.science/api/pith-number/Y4WFKWLHOUNCF2XBURKSCOHOR2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/action/storage_attestation","attest_author":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/action/author_attestation","sign_citation":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/action/citation_signature","submit_replication":"https://pith.science/pith/Y4WFKWLHOUNCF2XBURKSCOHOR2/action/replication_record"}},"created_at":"2026-05-17T23:55:59.881547+00:00","updated_at":"2026-05-17T23:55:59.881547+00:00"}