{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ZNQ7OJN2NIUU3RYUIKTXKQAFHG","short_pith_number":"pith:ZNQ7OJN2","schema_version":"1.0","canonical_sha256":"cb61f725ba6a294dc71442a775400539b605c6c58867e0b67e488ec90a4f24e2","source":{"kind":"arxiv","id":"2104.15052","version":2},"attestation_state":"computed","paper":{"title":"DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Fengyuan Yu, Guansong Pang, Hongzuo Xu, Songlei Jian, Yijie Wang, Yongjun Wang, Zhiyue Wu","submitted_at":"2021-04-30T15:20:22Z","abstract_excerpt":"DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been done on DRAM failure prediction mainly due to the lack of public available datasets. This paper presents a comprehensive empirical evaluation of diverse machine learning techniques for DRAM failure prediction using a large-scale multi-source dataset, including more than three millions of records of kernel, address, and mcelog data, provided by Alibaba Cloud through PAKDD 2021 competition. Particularly, we first formul"},"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":"2104.15052","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-04-30T15:20:22Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"b5787c54bd4c064f744b1f5b35d62273148efecf9358249019535fbb8476417b","abstract_canon_sha256":"93058a95dbef7677110e9895d752272bb6cbc6fc769bf16948cf826a281891b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:37:12.930171Z","signature_b64":"vVkGw3B0vCfK6wgaBFsrgMBi3OHFKLtXuN3Gz5EhBjCWRLBkEtGhQB8KvtbEeGZ55LiNOP9NkqQF4Dmls0B7BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb61f725ba6a294dc71442a775400539b605c6c58867e0b67e488ec90a4f24e2","last_reissued_at":"2026-07-05T02:37:12.929727Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:37:12.929727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Fengyuan Yu, Guansong Pang, Hongzuo Xu, Songlei Jian, Yijie Wang, Yongjun Wang, Zhiyue Wu","submitted_at":"2021-04-30T15:20:22Z","abstract_excerpt":"DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been done on DRAM failure prediction mainly due to the lack of public available datasets. This paper presents a comprehensive empirical evaluation of diverse machine learning techniques for DRAM failure prediction using a large-scale multi-source dataset, including more than three millions of records of kernel, address, and mcelog data, provided by Alibaba Cloud through PAKDD 2021 competition. Particularly, we first formul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.15052","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/2104.15052/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":"2104.15052","created_at":"2026-07-05T02:37:12.929790+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.15052v2","created_at":"2026-07-05T02:37:12.929790+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.15052","created_at":"2026-07-05T02:37:12.929790+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZNQ7OJN2NIUU","created_at":"2026-07-05T02:37:12.929790+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZNQ7OJN2NIUU3RYU","created_at":"2026-07-05T02:37:12.929790+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZNQ7OJN2","created_at":"2026-07-05T02:37:12.929790+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/ZNQ7OJN2NIUU3RYUIKTXKQAFHG","json":"https://pith.science/pith/ZNQ7OJN2NIUU3RYUIKTXKQAFHG.json","graph_json":"https://pith.science/api/pith-number/ZNQ7OJN2NIUU3RYUIKTXKQAFHG/graph.json","events_json":"https://pith.science/api/pith-number/ZNQ7OJN2NIUU3RYUIKTXKQAFHG/events.json","paper":"https://pith.science/paper/ZNQ7OJN2"},"agent_actions":{"view_html":"https://pith.science/pith/ZNQ7OJN2NIUU3RYUIKTXKQAFHG","download_json":"https://pith.science/pith/ZNQ7OJN2NIUU3RYUIKTXKQAFHG.json","view_paper":"https://pith.science/paper/ZNQ7OJN2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.15052&json=true","fetch_graph":"https://pith.science/api/pith-number/ZNQ7OJN2NIUU3RYUIKTXKQAFHG/graph.json","fetch_events":"https://pith.science/api/pith-number/ZNQ7OJN2NIUU3RYUIKTXKQAFHG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZNQ7OJN2NIUU3RYUIKTXKQAFHG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZNQ7OJN2NIUU3RYUIKTXKQAFHG/action/storage_attestation","attest_author":"https://pith.science/pith/ZNQ7OJN2NIUU3RYUIKTXKQAFHG/action/author_attestation","sign_citation":"https://pith.science/pith/ZNQ7OJN2NIUU3RYUIKTXKQAFHG/action/citation_signature","submit_replication":"https://pith.science/pith/ZNQ7OJN2NIUU3RYUIKTXKQAFHG/action/replication_record"}},"created_at":"2026-07-05T02:37:12.929790+00:00","updated_at":"2026-07-05T02:37:12.929790+00:00"}