{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:CNJH6ECM7CVE7UGAD2OJ4N3YQR","short_pith_number":"pith:CNJH6ECM","schema_version":"1.0","canonical_sha256":"13527f104cf8aa4fd0c01e9c9e37788461c6bc1d1fb75a9a90aa97f6ad4ea1c8","source":{"kind":"arxiv","id":"2208.03938","version":1},"attestation_state":"computed","paper":{"title":"Constructing Large-Scale Real-World Benchmark Datasets for AIOps","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.SE","authors_text":"Dan Pei, Minghua Ma, Nengwen Zhao, Pengfei Chen, Shenglin Zhang, Xidao Wen, Yongqian Sun, Zeyan Li","submitted_at":"2022-08-08T07:06:54Z","abstract_excerpt":"Recently, AIOps (Artificial Intelligence for IT Operations) has been well studied in academia and industry to enable automated and effective software service management. Plenty of efforts have been dedicated to AIOps, including anomaly detection, root cause localization, incident management, etc. However, most existing works are evaluated on private datasets, so their generality and real performance cannot be guaranteed. The lack of public large-scale real-world datasets has prevented researchers and engineers from enhancing the development of AIOps. To tackle this dilemma, in this work, we in"},"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":"2208.03938","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2022-08-08T07:06:54Z","cross_cats_sorted":["cs.PF"],"title_canon_sha256":"d09f0970a6efb5ab105071d55d1c270429684d4b57c1422f53f33eb0ed7416f4","abstract_canon_sha256":"67bb0f5c86d066c89fdbafaafd7d64aa88d5f5d49068d324a4c949190f22dbaf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:46:46.157567Z","signature_b64":"/Csis1k46hiQ5AoGgT5eqZ/lq8L95feEqkWvC5UFIZ5MApwnbeGscXxZQtLKyz38Z+dbi+gFQpe47FB6SNRTDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13527f104cf8aa4fd0c01e9c9e37788461c6bc1d1fb75a9a90aa97f6ad4ea1c8","last_reissued_at":"2026-07-05T04:46:46.157156Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:46:46.157156Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Constructing Large-Scale Real-World Benchmark Datasets for AIOps","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.SE","authors_text":"Dan Pei, Minghua Ma, Nengwen Zhao, Pengfei Chen, Shenglin Zhang, Xidao Wen, Yongqian Sun, Zeyan Li","submitted_at":"2022-08-08T07:06:54Z","abstract_excerpt":"Recently, AIOps (Artificial Intelligence for IT Operations) has been well studied in academia and industry to enable automated and effective software service management. Plenty of efforts have been dedicated to AIOps, including anomaly detection, root cause localization, incident management, etc. However, most existing works are evaluated on private datasets, so their generality and real performance cannot be guaranteed. The lack of public large-scale real-world datasets has prevented researchers and engineers from enhancing the development of AIOps. To tackle this dilemma, in this work, we in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.03938","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/2208.03938/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":"2208.03938","created_at":"2026-07-05T04:46:46.157210+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.03938v1","created_at":"2026-07-05T04:46:46.157210+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.03938","created_at":"2026-07-05T04:46:46.157210+00:00"},{"alias_kind":"pith_short_12","alias_value":"CNJH6ECM7CVE","created_at":"2026-07-05T04:46:46.157210+00:00"},{"alias_kind":"pith_short_16","alias_value":"CNJH6ECM7CVE7UGA","created_at":"2026-07-05T04:46:46.157210+00:00"},{"alias_kind":"pith_short_8","alias_value":"CNJH6ECM","created_at":"2026-07-05T04:46:46.157210+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27154","citing_title":"OpenRCA 2.0: From Outcome Labels to Causal Process Supervision","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27154","citing_title":"OpenRCA 2.0: From Outcome Labels to Causal Process Supervision","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12729","citing_title":"Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety","ref_index":127,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12729","citing_title":"Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety","ref_index":127,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05725","citing_title":"Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CNJH6ECM7CVE7UGAD2OJ4N3YQR","json":"https://pith.science/pith/CNJH6ECM7CVE7UGAD2OJ4N3YQR.json","graph_json":"https://pith.science/api/pith-number/CNJH6ECM7CVE7UGAD2OJ4N3YQR/graph.json","events_json":"https://pith.science/api/pith-number/CNJH6ECM7CVE7UGAD2OJ4N3YQR/events.json","paper":"https://pith.science/paper/CNJH6ECM"},"agent_actions":{"view_html":"https://pith.science/pith/CNJH6ECM7CVE7UGAD2OJ4N3YQR","download_json":"https://pith.science/pith/CNJH6ECM7CVE7UGAD2OJ4N3YQR.json","view_paper":"https://pith.science/paper/CNJH6ECM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.03938&json=true","fetch_graph":"https://pith.science/api/pith-number/CNJH6ECM7CVE7UGAD2OJ4N3YQR/graph.json","fetch_events":"https://pith.science/api/pith-number/CNJH6ECM7CVE7UGAD2OJ4N3YQR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CNJH6ECM7CVE7UGAD2OJ4N3YQR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CNJH6ECM7CVE7UGAD2OJ4N3YQR/action/storage_attestation","attest_author":"https://pith.science/pith/CNJH6ECM7CVE7UGAD2OJ4N3YQR/action/author_attestation","sign_citation":"https://pith.science/pith/CNJH6ECM7CVE7UGAD2OJ4N3YQR/action/citation_signature","submit_replication":"https://pith.science/pith/CNJH6ECM7CVE7UGAD2OJ4N3YQR/action/replication_record"}},"created_at":"2026-07-05T04:46:46.157210+00:00","updated_at":"2026-07-05T04:46:46.157210+00:00"}