{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:JJOFCYD4RXU6TJSYATBX26CKEB","short_pith_number":"pith:JJOFCYD4","schema_version":"1.0","canonical_sha256":"4a5c51607c8de9e9a65804c37d784a20409a2566ae21ee48dcd104b0480afc4c","source":{"kind":"arxiv","id":"1608.08252","version":1},"attestation_state":"computed","paper":{"title":"Business Process Deviance Mining: Review and Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.AI","authors_text":"Fabrizio Maria Maggi, Hoang Nguyen, Marcello La Rosa, Marlon Dumas, Suriadi Suriadi","submitted_at":"2016-08-29T21:14:01Z","abstract_excerpt":"Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to its expected or desirable outcomes. Deviant executions of a business process include those that violate compliance rules, or executions that undershoot or exceed performance targets. Deviance mining is concerned with uncovering the reasons for deviant executions by analyzing business process event logs. This article provides a systematic review and comparative evaluation of deviance mining approaches based on a family of data mining"},"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":"1608.08252","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-08-29T21:14:01Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"8b6e45a2eb5af7ae5b24922b4708126fcfbb752d7d7be43bd769e981e00cb520","abstract_canon_sha256":"d99bbb177a0ac83daf7310a6919b82429367990494042fa9828619cbd9f61a65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:07:19.836091Z","signature_b64":"P9eaitkUzXhFc7olyIx/HdvkqxeeTj5MF394uiL4ufdg8UVP9OpFtvMMKVK6Rc9t5UF4B3ZPiqrt/baeS4bQDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a5c51607c8de9e9a65804c37d784a20409a2566ae21ee48dcd104b0480afc4c","last_reissued_at":"2026-05-18T01:07:19.835580Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:07:19.835580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Business Process Deviance Mining: Review and Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.AI","authors_text":"Fabrizio Maria Maggi, Hoang Nguyen, Marcello La Rosa, Marlon Dumas, Suriadi Suriadi","submitted_at":"2016-08-29T21:14:01Z","abstract_excerpt":"Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to its expected or desirable outcomes. Deviant executions of a business process include those that violate compliance rules, or executions that undershoot or exceed performance targets. Deviance mining is concerned with uncovering the reasons for deviant executions by analyzing business process event logs. This article provides a systematic review and comparative evaluation of deviance mining approaches based on a family of data mining"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.08252","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":""},"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":"1608.08252","created_at":"2026-05-18T01:07:19.835653+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.08252v1","created_at":"2026-05-18T01:07:19.835653+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.08252","created_at":"2026-05-18T01:07:19.835653+00:00"},{"alias_kind":"pith_short_12","alias_value":"JJOFCYD4RXU6","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_16","alias_value":"JJOFCYD4RXU6TJSY","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_8","alias_value":"JJOFCYD4","created_at":"2026-05-18T12:30:25.849896+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/JJOFCYD4RXU6TJSYATBX26CKEB","json":"https://pith.science/pith/JJOFCYD4RXU6TJSYATBX26CKEB.json","graph_json":"https://pith.science/api/pith-number/JJOFCYD4RXU6TJSYATBX26CKEB/graph.json","events_json":"https://pith.science/api/pith-number/JJOFCYD4RXU6TJSYATBX26CKEB/events.json","paper":"https://pith.science/paper/JJOFCYD4"},"agent_actions":{"view_html":"https://pith.science/pith/JJOFCYD4RXU6TJSYATBX26CKEB","download_json":"https://pith.science/pith/JJOFCYD4RXU6TJSYATBX26CKEB.json","view_paper":"https://pith.science/paper/JJOFCYD4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.08252&json=true","fetch_graph":"https://pith.science/api/pith-number/JJOFCYD4RXU6TJSYATBX26CKEB/graph.json","fetch_events":"https://pith.science/api/pith-number/JJOFCYD4RXU6TJSYATBX26CKEB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JJOFCYD4RXU6TJSYATBX26CKEB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JJOFCYD4RXU6TJSYATBX26CKEB/action/storage_attestation","attest_author":"https://pith.science/pith/JJOFCYD4RXU6TJSYATBX26CKEB/action/author_attestation","sign_citation":"https://pith.science/pith/JJOFCYD4RXU6TJSYATBX26CKEB/action/citation_signature","submit_replication":"https://pith.science/pith/JJOFCYD4RXU6TJSYATBX26CKEB/action/replication_record"}},"created_at":"2026-05-18T01:07:19.835653+00:00","updated_at":"2026-05-18T01:07:19.835653+00:00"}