{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UHCGIWTILNB3KD4Y3Q7RLLHXZA","short_pith_number":"pith:UHCGIWTI","schema_version":"1.0","canonical_sha256":"a1c4645a685b43b50f98dc3f15acf7c8075a8cd1a6d02438864805eed2a8c364","source":{"kind":"arxiv","id":"2403.00775","version":1},"attestation_state":"computed","paper":{"title":"Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.DB","cs.LG"],"primary_cat":"q-fin.ST","authors_text":"Alessandro Niro, Michael Werner","submitted_at":"2024-02-14T14:17:56Z","abstract_excerpt":"Detecting anomalies is important for identifying inefficiencies, errors, or fraud in business processes. Traditional process mining approaches focus on analyzing 'flattened', sequential, event logs based on a single case notion. However, many real-world process executions exhibit a graph-like structure, where events can be associated with multiple cases. Flattening event logs requires selecting a single case identifier which creates a gap with the real event data and artificially introduces anomalies in the event logs. Object-centric process mining avoids these limitations by allowing events t"},"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":"2403.00775","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-fin.ST","submitted_at":"2024-02-14T14:17:56Z","cross_cats_sorted":["cs.DB","cs.LG"],"title_canon_sha256":"ef5d7a97a66c50bed37470ec512205293f3e125ca66874b48b4f2e8d081513f2","abstract_canon_sha256":"1d9c151db362ce944edf0d134216cdd95369fcf6c594646fd3f10083942d9194"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:51:11.558091Z","signature_b64":"UpLW1JuJpLbsaDM8paCPRq6cet5zuDbDT8C/63si+4E/ciqf4169JR7KvQgYyyOqNtd5UHhbG1pdTKuATD0bDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1c4645a685b43b50f98dc3f15acf7c8075a8cd1a6d02438864805eed2a8c364","last_reissued_at":"2026-07-05T07:51:11.557671Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:51:11.557671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.DB","cs.LG"],"primary_cat":"q-fin.ST","authors_text":"Alessandro Niro, Michael Werner","submitted_at":"2024-02-14T14:17:56Z","abstract_excerpt":"Detecting anomalies is important for identifying inefficiencies, errors, or fraud in business processes. Traditional process mining approaches focus on analyzing 'flattened', sequential, event logs based on a single case notion. However, many real-world process executions exhibit a graph-like structure, where events can be associated with multiple cases. Flattening event logs requires selecting a single case identifier which creates a gap with the real event data and artificially introduces anomalies in the event logs. Object-centric process mining avoids these limitations by allowing events t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.00775","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/2403.00775/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":"2403.00775","created_at":"2026-07-05T07:51:11.557731+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.00775v1","created_at":"2026-07-05T07:51:11.557731+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.00775","created_at":"2026-07-05T07:51:11.557731+00:00"},{"alias_kind":"pith_short_12","alias_value":"UHCGIWTILNB3","created_at":"2026-07-05T07:51:11.557731+00:00"},{"alias_kind":"pith_short_16","alias_value":"UHCGIWTILNB3KD4Y","created_at":"2026-07-05T07:51:11.557731+00:00"},{"alias_kind":"pith_short_8","alias_value":"UHCGIWTI","created_at":"2026-07-05T07:51:11.557731+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.06918","citing_title":"Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UHCGIWTILNB3KD4Y3Q7RLLHXZA","json":"https://pith.science/pith/UHCGIWTILNB3KD4Y3Q7RLLHXZA.json","graph_json":"https://pith.science/api/pith-number/UHCGIWTILNB3KD4Y3Q7RLLHXZA/graph.json","events_json":"https://pith.science/api/pith-number/UHCGIWTILNB3KD4Y3Q7RLLHXZA/events.json","paper":"https://pith.science/paper/UHCGIWTI"},"agent_actions":{"view_html":"https://pith.science/pith/UHCGIWTILNB3KD4Y3Q7RLLHXZA","download_json":"https://pith.science/pith/UHCGIWTILNB3KD4Y3Q7RLLHXZA.json","view_paper":"https://pith.science/paper/UHCGIWTI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.00775&json=true","fetch_graph":"https://pith.science/api/pith-number/UHCGIWTILNB3KD4Y3Q7RLLHXZA/graph.json","fetch_events":"https://pith.science/api/pith-number/UHCGIWTILNB3KD4Y3Q7RLLHXZA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UHCGIWTILNB3KD4Y3Q7RLLHXZA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UHCGIWTILNB3KD4Y3Q7RLLHXZA/action/storage_attestation","attest_author":"https://pith.science/pith/UHCGIWTILNB3KD4Y3Q7RLLHXZA/action/author_attestation","sign_citation":"https://pith.science/pith/UHCGIWTILNB3KD4Y3Q7RLLHXZA/action/citation_signature","submit_replication":"https://pith.science/pith/UHCGIWTILNB3KD4Y3Q7RLLHXZA/action/replication_record"}},"created_at":"2026-07-05T07:51:11.557731+00:00","updated_at":"2026-07-05T07:51:11.557731+00:00"}