{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:ESQDENUITOHOBS54542NOAAAZ6","short_pith_number":"pith:ESQDENUI","schema_version":"1.0","canonical_sha256":"24a03236889b8ee0cbbcef34d70000cfbecf42ea27128ddae9a742eda1a3ca51","source":{"kind":"arxiv","id":"2003.11268","version":2},"attestation_state":"computed","paper":{"title":"Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Farbod Taymouri, Ilya Verenich, Marcello La Rosa, Sarah Erfani, Zahra Dasht Bozorgi","submitted_at":"2020-03-25T08:31:28Z","abstract_excerpt":"Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the problem of next event prediction. However, due to insufficient training data or sub-optimal network configuration and architecture, these approaches do not generalize well the problem at hand. This paper proposes a novel adversarial training framework to address this shortcoming, b"},"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":"2003.11268","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-25T08:31:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"bdd0748e462424b58c662a2e32bdd49a406409f63bbf4fc0666c4e05832cf9a7","abstract_canon_sha256":"6629d966ba8be0b2b0b3c9b8d110a47bfc9e647e08db62b3c0d6dc227fff5d38"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:52:08.099511Z","signature_b64":"t3XnYYdPyfH9GhcETVKTu5sZWCnNgRyF5cnsbrxcKLaREh4g2UJNOcecGl4l4m5trBwAPggwb+OwCzosxAewCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24a03236889b8ee0cbbcef34d70000cfbecf42ea27128ddae9a742eda1a3ca51","last_reissued_at":"2026-07-05T00:52:08.099102Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:52:08.099102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Farbod Taymouri, Ilya Verenich, Marcello La Rosa, Sarah Erfani, Zahra Dasht Bozorgi","submitted_at":"2020-03-25T08:31:28Z","abstract_excerpt":"Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the problem of next event prediction. However, due to insufficient training data or sub-optimal network configuration and architecture, these approaches do not generalize well the problem at hand. This paper proposes a novel adversarial training framework to address this shortcoming, b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.11268","kind":"arxiv","version":2},"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/2003.11268/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":"2003.11268","created_at":"2026-07-05T00:52:08.099163+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.11268v2","created_at":"2026-07-05T00:52:08.099163+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.11268","created_at":"2026-07-05T00:52:08.099163+00:00"},{"alias_kind":"pith_short_12","alias_value":"ESQDENUITOHO","created_at":"2026-07-05T00:52:08.099163+00:00"},{"alias_kind":"pith_short_16","alias_value":"ESQDENUITOHOBS54","created_at":"2026-07-05T00:52:08.099163+00:00"},{"alias_kind":"pith_short_8","alias_value":"ESQDENUI","created_at":"2026-07-05T00:52:08.099163+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/ESQDENUITOHOBS54542NOAAAZ6","json":"https://pith.science/pith/ESQDENUITOHOBS54542NOAAAZ6.json","graph_json":"https://pith.science/api/pith-number/ESQDENUITOHOBS54542NOAAAZ6/graph.json","events_json":"https://pith.science/api/pith-number/ESQDENUITOHOBS54542NOAAAZ6/events.json","paper":"https://pith.science/paper/ESQDENUI"},"agent_actions":{"view_html":"https://pith.science/pith/ESQDENUITOHOBS54542NOAAAZ6","download_json":"https://pith.science/pith/ESQDENUITOHOBS54542NOAAAZ6.json","view_paper":"https://pith.science/paper/ESQDENUI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.11268&json=true","fetch_graph":"https://pith.science/api/pith-number/ESQDENUITOHOBS54542NOAAAZ6/graph.json","fetch_events":"https://pith.science/api/pith-number/ESQDENUITOHOBS54542NOAAAZ6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ESQDENUITOHOBS54542NOAAAZ6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ESQDENUITOHOBS54542NOAAAZ6/action/storage_attestation","attest_author":"https://pith.science/pith/ESQDENUITOHOBS54542NOAAAZ6/action/author_attestation","sign_citation":"https://pith.science/pith/ESQDENUITOHOBS54542NOAAAZ6/action/citation_signature","submit_replication":"https://pith.science/pith/ESQDENUITOHOBS54542NOAAAZ6/action/replication_record"}},"created_at":"2026-07-05T00:52:08.099163+00:00","updated_at":"2026-07-05T00:52:08.099163+00:00"}