{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DSA5V66PTONZKYFUTVES6GJWC7","short_pith_number":"pith:DSA5V66P","schema_version":"1.0","canonical_sha256":"1c81dafbcf9b9b9560b49d492f193617cab5648058407757f009c34a9d73e28d","source":{"kind":"arxiv","id":"2404.01363","version":1},"attestation_state":"computed","paper":{"title":"AIOps Solutions for Incident Management: Technical Guidelines and A Comprehensive Literature Review","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.SE"],"primary_cat":"cs.OS","authors_text":"Anes Bendimerad, Mehdi Kaytoue, Romain Mathonat, Youcef Remil","submitted_at":"2024-04-01T17:32:22Z","abstract_excerpt":"The management of modern IT systems poses unique challenges, necessitating scalability, reliability, and efficiency in handling extensive data streams. Traditional methods, reliant on manual tasks and rule-based approaches, prove inefficient for the substantial data volumes and alerts generated by IT systems. Artificial Intelligence for Operating Systems (AIOps) has emerged as a solution, leveraging advanced analytics like machine learning and big data to enhance incident management. AIOps detects and predicts incidents, identifies root causes, and automates healing actions, improving quality "},"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":"2404.01363","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.OS","submitted_at":"2024-04-01T17:32:22Z","cross_cats_sorted":["cs.AI","cs.SE"],"title_canon_sha256":"b23a8137c3e2f9e2f34ad5db766b12ba48c52ccc3db592504cef94049ddaa807","abstract_canon_sha256":"b444883297cb69832d9562fa292bb670bad2784206b313264c661fc40be409c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:03:13.246430Z","signature_b64":"uJbt3G/rwYeGUHuSJLCH7shaIre6St236/nI3oogx6zpCancFAThYLz9mTMjtedKUbPHxLQFeKcu95FzYqMADw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c81dafbcf9b9b9560b49d492f193617cab5648058407757f009c34a9d73e28d","last_reissued_at":"2026-07-05T08:03:13.245934Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:03:13.245934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AIOps Solutions for Incident Management: Technical Guidelines and A Comprehensive Literature Review","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.SE"],"primary_cat":"cs.OS","authors_text":"Anes Bendimerad, Mehdi Kaytoue, Romain Mathonat, Youcef Remil","submitted_at":"2024-04-01T17:32:22Z","abstract_excerpt":"The management of modern IT systems poses unique challenges, necessitating scalability, reliability, and efficiency in handling extensive data streams. Traditional methods, reliant on manual tasks and rule-based approaches, prove inefficient for the substantial data volumes and alerts generated by IT systems. Artificial Intelligence for Operating Systems (AIOps) has emerged as a solution, leveraging advanced analytics like machine learning and big data to enhance incident management. AIOps detects and predicts incidents, identifies root causes, and automates healing actions, improving quality "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.01363","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/2404.01363/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":"2404.01363","created_at":"2026-07-05T08:03:13.245993+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.01363v1","created_at":"2026-07-05T08:03:13.245993+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.01363","created_at":"2026-07-05T08:03:13.245993+00:00"},{"alias_kind":"pith_short_12","alias_value":"DSA5V66PTONZ","created_at":"2026-07-05T08:03:13.245993+00:00"},{"alias_kind":"pith_short_16","alias_value":"DSA5V66PTONZKYFU","created_at":"2026-07-05T08:03:13.245993+00:00"},{"alias_kind":"pith_short_8","alias_value":"DSA5V66P","created_at":"2026-07-05T08:03:13.245993+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.11094","citing_title":"E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13462","citing_title":"Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04431","citing_title":"Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DSA5V66PTONZKYFUTVES6GJWC7","json":"https://pith.science/pith/DSA5V66PTONZKYFUTVES6GJWC7.json","graph_json":"https://pith.science/api/pith-number/DSA5V66PTONZKYFUTVES6GJWC7/graph.json","events_json":"https://pith.science/api/pith-number/DSA5V66PTONZKYFUTVES6GJWC7/events.json","paper":"https://pith.science/paper/DSA5V66P"},"agent_actions":{"view_html":"https://pith.science/pith/DSA5V66PTONZKYFUTVES6GJWC7","download_json":"https://pith.science/pith/DSA5V66PTONZKYFUTVES6GJWC7.json","view_paper":"https://pith.science/paper/DSA5V66P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.01363&json=true","fetch_graph":"https://pith.science/api/pith-number/DSA5V66PTONZKYFUTVES6GJWC7/graph.json","fetch_events":"https://pith.science/api/pith-number/DSA5V66PTONZKYFUTVES6GJWC7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DSA5V66PTONZKYFUTVES6GJWC7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DSA5V66PTONZKYFUTVES6GJWC7/action/storage_attestation","attest_author":"https://pith.science/pith/DSA5V66PTONZKYFUTVES6GJWC7/action/author_attestation","sign_citation":"https://pith.science/pith/DSA5V66PTONZKYFUTVES6GJWC7/action/citation_signature","submit_replication":"https://pith.science/pith/DSA5V66PTONZKYFUTVES6GJWC7/action/replication_record"}},"created_at":"2026-07-05T08:03:13.245993+00:00","updated_at":"2026-07-05T08:03:13.245993+00:00"}