{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7KNCGZTPLPQKQOCPSRFL37FH7Y","short_pith_number":"pith:7KNCGZTP","schema_version":"1.0","canonical_sha256":"fa9a23666f5be0a8384f944abdfca7fe292d15e1615833ab49d040907918b1f1","source":{"kind":"arxiv","id":"2304.12085","version":1},"attestation_state":"computed","paper":{"title":"Dangoron: Network Construction on Large-scale Time Series Data across Sliding Windows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"physics.soc-ph","authors_text":"Peizhen Yang, Yunlong Xu, Zhengbin Tao","submitted_at":"2023-04-11T23:54:41Z","abstract_excerpt":"Complex networks represent system dynamics through the interactions of a set of anomalous time series. Consider the problem of computing correlations for highly correlated pairs of time series across sliding windows. Efficiently computing and updating the correlation matrix for user-defined sliding periods and thresholds enables large-scale time series network dynamics analysis. We introduce Dangoron, a framework for effectively identifying highly correlated pairs of time series over sliding windows and computing their exact correlation. By predicting dynamic correlation across sliding windows"},"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":"2304.12085","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.soc-ph","submitted_at":"2023-04-11T23:54:41Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"6df45c835618b5817b47d32a232be5ee6711674d4921034d12e98055a63970d2","abstract_canon_sha256":"a65d0e53327f39788fbf3077b8a15f4f9841a73b890edace8a64552005d4e965"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:03:47.657276Z","signature_b64":"94dQix1AzQc/MjNJ/qyRW9OjN2R6KYXctrcSBeYd2E7/2OPYXOK0fIYsc8Tu4aWZUk24UeT/wjWsSEV7hfOiCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa9a23666f5be0a8384f944abdfca7fe292d15e1615833ab49d040907918b1f1","last_reissued_at":"2026-07-05T06:03:47.656932Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:03:47.656932Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dangoron: Network Construction on Large-scale Time Series Data across Sliding Windows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"physics.soc-ph","authors_text":"Peizhen Yang, Yunlong Xu, Zhengbin Tao","submitted_at":"2023-04-11T23:54:41Z","abstract_excerpt":"Complex networks represent system dynamics through the interactions of a set of anomalous time series. Consider the problem of computing correlations for highly correlated pairs of time series across sliding windows. Efficiently computing and updating the correlation matrix for user-defined sliding periods and thresholds enables large-scale time series network dynamics analysis. We introduce Dangoron, a framework for effectively identifying highly correlated pairs of time series over sliding windows and computing their exact correlation. By predicting dynamic correlation across sliding windows"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.12085","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.12085/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2304.12085","created_at":"2026-07-05T06:03:47.656995+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.12085v1","created_at":"2026-07-05T06:03:47.656995+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.12085","created_at":"2026-07-05T06:03:47.656995+00:00"},{"alias_kind":"pith_short_12","alias_value":"7KNCGZTPLPQK","created_at":"2026-07-05T06:03:47.656995+00:00"},{"alias_kind":"pith_short_16","alias_value":"7KNCGZTPLPQKQOCP","created_at":"2026-07-05T06:03:47.656995+00:00"},{"alias_kind":"pith_short_8","alias_value":"7KNCGZTP","created_at":"2026-07-05T06:03:47.656995+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/7KNCGZTPLPQKQOCPSRFL37FH7Y","json":"https://pith.science/pith/7KNCGZTPLPQKQOCPSRFL37FH7Y.json","graph_json":"https://pith.science/api/pith-number/7KNCGZTPLPQKQOCPSRFL37FH7Y/graph.json","events_json":"https://pith.science/api/pith-number/7KNCGZTPLPQKQOCPSRFL37FH7Y/events.json","paper":"https://pith.science/paper/7KNCGZTP"},"agent_actions":{"view_html":"https://pith.science/pith/7KNCGZTPLPQKQOCPSRFL37FH7Y","download_json":"https://pith.science/pith/7KNCGZTPLPQKQOCPSRFL37FH7Y.json","view_paper":"https://pith.science/paper/7KNCGZTP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.12085&json=true","fetch_graph":"https://pith.science/api/pith-number/7KNCGZTPLPQKQOCPSRFL37FH7Y/graph.json","fetch_events":"https://pith.science/api/pith-number/7KNCGZTPLPQKQOCPSRFL37FH7Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7KNCGZTPLPQKQOCPSRFL37FH7Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7KNCGZTPLPQKQOCPSRFL37FH7Y/action/storage_attestation","attest_author":"https://pith.science/pith/7KNCGZTPLPQKQOCPSRFL37FH7Y/action/author_attestation","sign_citation":"https://pith.science/pith/7KNCGZTPLPQKQOCPSRFL37FH7Y/action/citation_signature","submit_replication":"https://pith.science/pith/7KNCGZTPLPQKQOCPSRFL37FH7Y/action/replication_record"}},"created_at":"2026-07-05T06:03:47.656995+00:00","updated_at":"2026-07-05T06:03:47.656995+00:00"}