{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FCENPQDIAWXCPNTLUXRBEDYP6W","short_pith_number":"pith:FCENPQDI","schema_version":"1.0","canonical_sha256":"2888d7c06805ae27b66ba5e2120f0ff5b25932465f34c83eb4ed691f41bc5848","source":{"kind":"arxiv","id":"2412.07986","version":1},"attestation_state":"computed","paper":{"title":"Provenance Analysis and Semiring Semantics for First-Order Logic","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.LO","authors_text":"Erich Gr\\\"adel, Val Tannen","submitted_at":"2024-12-10T23:59:12Z","abstract_excerpt":"A provenance analysis for a query evaluation or a model checking computation extracts information on how its result depends on the atomic facts of the model or database. Traditional work on data provenance was, to a large extent, restricted to positive query languages or the negation-free fragment of first-order logic and showed how provenance abstractions can be usefully described as elements of commutative semirings -- most generally as multivariate polynomials with positive integer coefficients. We describe and evaluate here a provenance approach for dealing with negation, based on quotient"},"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":"2412.07986","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LO","submitted_at":"2024-12-10T23:59:12Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"d2842ffbb75f519ab39648b1264a50d68ceaebaaa81775287a0dfb67df976189","abstract_canon_sha256":"ee016c3b8b0dd15b2b04ad5ff573a93ea88651da5677cbef5d2b41be47b862fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:47:42.616795Z","signature_b64":"/Zech2Wd3Qo57Jmq5BUSqsiDuTbjvTykMSRlY2j5GQ5nZqs7H6OoiGQ17SG/UCPg0rwNkm6bQt9NpnZEZTxsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2888d7c06805ae27b66ba5e2120f0ff5b25932465f34c83eb4ed691f41bc5848","last_reissued_at":"2026-07-05T09:47:42.616150Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:47:42.616150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Provenance Analysis and Semiring Semantics for First-Order Logic","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.LO","authors_text":"Erich Gr\\\"adel, Val Tannen","submitted_at":"2024-12-10T23:59:12Z","abstract_excerpt":"A provenance analysis for a query evaluation or a model checking computation extracts information on how its result depends on the atomic facts of the model or database. Traditional work on data provenance was, to a large extent, restricted to positive query languages or the negation-free fragment of first-order logic and showed how provenance abstractions can be usefully described as elements of commutative semirings -- most generally as multivariate polynomials with positive integer coefficients. We describe and evaluate here a provenance approach for dealing with negation, based on quotient"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.07986","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/2412.07986/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":"2412.07986","created_at":"2026-07-05T09:47:42.616227+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.07986v1","created_at":"2026-07-05T09:47:42.616227+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.07986","created_at":"2026-07-05T09:47:42.616227+00:00"},{"alias_kind":"pith_short_12","alias_value":"FCENPQDIAWXC","created_at":"2026-07-05T09:47:42.616227+00:00"},{"alias_kind":"pith_short_16","alias_value":"FCENPQDIAWXCPNTL","created_at":"2026-07-05T09:47:42.616227+00:00"},{"alias_kind":"pith_short_8","alias_value":"FCENPQDI","created_at":"2026-07-05T09:47:42.616227+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06240","citing_title":"TOKI: A Bitemporal Operator Algebra for Contradiction Resolution in LLM-Agent Persistent Memory","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FCENPQDIAWXCPNTLUXRBEDYP6W","json":"https://pith.science/pith/FCENPQDIAWXCPNTLUXRBEDYP6W.json","graph_json":"https://pith.science/api/pith-number/FCENPQDIAWXCPNTLUXRBEDYP6W/graph.json","events_json":"https://pith.science/api/pith-number/FCENPQDIAWXCPNTLUXRBEDYP6W/events.json","paper":"https://pith.science/paper/FCENPQDI"},"agent_actions":{"view_html":"https://pith.science/pith/FCENPQDIAWXCPNTLUXRBEDYP6W","download_json":"https://pith.science/pith/FCENPQDIAWXCPNTLUXRBEDYP6W.json","view_paper":"https://pith.science/paper/FCENPQDI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.07986&json=true","fetch_graph":"https://pith.science/api/pith-number/FCENPQDIAWXCPNTLUXRBEDYP6W/graph.json","fetch_events":"https://pith.science/api/pith-number/FCENPQDIAWXCPNTLUXRBEDYP6W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FCENPQDIAWXCPNTLUXRBEDYP6W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FCENPQDIAWXCPNTLUXRBEDYP6W/action/storage_attestation","attest_author":"https://pith.science/pith/FCENPQDIAWXCPNTLUXRBEDYP6W/action/author_attestation","sign_citation":"https://pith.science/pith/FCENPQDIAWXCPNTLUXRBEDYP6W/action/citation_signature","submit_replication":"https://pith.science/pith/FCENPQDIAWXCPNTLUXRBEDYP6W/action/replication_record"}},"created_at":"2026-07-05T09:47:42.616227+00:00","updated_at":"2026-07-05T09:47:42.616227+00:00"}