{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DJB3XPCZLDWK3HRD6JM67XRH6J","short_pith_number":"pith:DJB3XPCZ","schema_version":"1.0","canonical_sha256":"1a43bbbc5958ecad9e23f259efde27f26f1f2618fdb06febd3c19503646719ca","source":{"kind":"arxiv","id":"2404.06043","version":1},"attestation_state":"computed","paper":{"title":"Automatic Configuration Tuning on Cloud Database: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Limeng Zhang, M. Ali Babar","submitted_at":"2024-04-09T06:07:03Z","abstract_excerpt":"Faced with the challenges of big data, modern cloud database management systems are designed to efficiently store, organize, and retrieve data, supporting optimal performance, scalability, and reliability for complex data processing and analysis. However, achieving good performance in modern databases is non-trivial as they are notorious for having dozens of configurable knobs, such as hardware setup, software setup, database physical and logical design, etc., that control runtime behaviors and impact database performance. To find the optimal configuration for achieving optimal performance, ex"},"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":"2404.06043","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2024-04-09T06:07:03Z","cross_cats_sorted":[],"title_canon_sha256":"2a46ff46a371174dd8c90d51bd27ff5fbe9ac9ba067e78a74e27117a46378737","abstract_canon_sha256":"b0e74fd1398e0c7fa054b286a37d58b77e7a6ec37f3725ac33b58cf84f847902"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:06:02.963117Z","signature_b64":"C313S6Xg7797BV73IHwvybr/9PAWpfrizggUkFw4i3R2fnB7tuANkKt6wUBkhSQnkJSAIiLJwx30Ipq/aadFCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a43bbbc5958ecad9e23f259efde27f26f1f2618fdb06febd3c19503646719ca","last_reissued_at":"2026-07-05T08:06:02.962661Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:06:02.962661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Configuration Tuning on Cloud Database: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Limeng Zhang, M. Ali Babar","submitted_at":"2024-04-09T06:07:03Z","abstract_excerpt":"Faced with the challenges of big data, modern cloud database management systems are designed to efficiently store, organize, and retrieve data, supporting optimal performance, scalability, and reliability for complex data processing and analysis. However, achieving good performance in modern databases is non-trivial as they are notorious for having dozens of configurable knobs, such as hardware setup, software setup, database physical and logical design, etc., that control runtime behaviors and impact database performance. To find the optimal configuration for achieving optimal performance, ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.06043","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/2404.06043/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":"2404.06043","created_at":"2026-07-05T08:06:02.962716+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.06043v1","created_at":"2026-07-05T08:06:02.962716+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.06043","created_at":"2026-07-05T08:06:02.962716+00:00"},{"alias_kind":"pith_short_12","alias_value":"DJB3XPCZLDWK","created_at":"2026-07-05T08:06:02.962716+00:00"},{"alias_kind":"pith_short_16","alias_value":"DJB3XPCZLDWK3HRD","created_at":"2026-07-05T08:06:02.962716+00:00"},{"alias_kind":"pith_short_8","alias_value":"DJB3XPCZ","created_at":"2026-07-05T08:06:02.962716+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20318","citing_title":"AgenticDB: Self-Evolving Reconfiguration Framework for Database Workloads","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20318","citing_title":"AgenticDB: Self-Evolving Reconfiguration Framework for Database Workloads","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DJB3XPCZLDWK3HRD6JM67XRH6J","json":"https://pith.science/pith/DJB3XPCZLDWK3HRD6JM67XRH6J.json","graph_json":"https://pith.science/api/pith-number/DJB3XPCZLDWK3HRD6JM67XRH6J/graph.json","events_json":"https://pith.science/api/pith-number/DJB3XPCZLDWK3HRD6JM67XRH6J/events.json","paper":"https://pith.science/paper/DJB3XPCZ"},"agent_actions":{"view_html":"https://pith.science/pith/DJB3XPCZLDWK3HRD6JM67XRH6J","download_json":"https://pith.science/pith/DJB3XPCZLDWK3HRD6JM67XRH6J.json","view_paper":"https://pith.science/paper/DJB3XPCZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.06043&json=true","fetch_graph":"https://pith.science/api/pith-number/DJB3XPCZLDWK3HRD6JM67XRH6J/graph.json","fetch_events":"https://pith.science/api/pith-number/DJB3XPCZLDWK3HRD6JM67XRH6J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DJB3XPCZLDWK3HRD6JM67XRH6J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DJB3XPCZLDWK3HRD6JM67XRH6J/action/storage_attestation","attest_author":"https://pith.science/pith/DJB3XPCZLDWK3HRD6JM67XRH6J/action/author_attestation","sign_citation":"https://pith.science/pith/DJB3XPCZLDWK3HRD6JM67XRH6J/action/citation_signature","submit_replication":"https://pith.science/pith/DJB3XPCZLDWK3HRD6JM67XRH6J/action/replication_record"}},"created_at":"2026-07-05T08:06:02.962716+00:00","updated_at":"2026-07-05T08:06:02.962716+00:00"}