{"id":"W4306316908","doi":"10.1145/3511808.3557117","title":"BLUTune: Query-informed Multi-stage IBM Db2 Tuning via ML","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); Ontario Tech University; York University","funders":"","keywords":"Computer science; IBM; Reinforcement learning; Embedding; Process (computing); Artificial intelligence; Resource (disambiguation); Machine learning; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013759,0.001090691,0.000801422,0.0003635865,0.0003840059,0.0009085905,0.002222434,0.0007920722,0.002655541],"category_scores_gemma":[0.004349858,0.0005449298,0.0003558825,0.0004081987,0.0006589593,0.001521506,0.001200874,0.001769798,0.0007578239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004202,"about_ca_system_score_gemma":0.001476575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01080696,"about_ca_topic_score_gemma":0.01101,"domain_scores_codex":[0.9992061,0.0001825759,0.00003312189,0.0002444951,0.0002197271,0.0001138965],"domain_scores_gemma":[0.9988062,0.0005889706,0.00008059211,0.0002446467,0.0001837895,0.00009574666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000611719,0.0004651772,0.003953616,0.00008537928,0.00009905577,0.0001185912,0.0001145277,0.7926912,0.01690445,0.002523329,0.009508924,0.1729239],"study_design_scores_gemma":[0.00002115179,0.00002966919,0.0001347818,0.000001323286,0.000002657119,0.000006756657,0.000006368854,0.9973117,0.001533792,0.000601856,0.0003454337,0.000004492396],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2076701,0.0009730284,0.7293915,0.0006524285,0.0001747483,0.0002213605,0.0005233042,0.05270953,0.007684053],"genre_scores_gemma":[0.8939593,0.0000813177,0.102015,0.0003235951,0.00002997822,0.0001210674,0.0005418322,0.0007576466,0.002170331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01080696,"threshold_uncertainty_score":0.02148813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07434386009228877,"score_gpt":0.3228954959479354,"score_spread":0.2485516358556466,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}