{"id":"W3033337213","doi":"10.1145/3401071.3401657","title":"Research challenges in deep reinforcement learning-based join query optimization","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Query optimization; Computer science; Sargable; Join (topology); Reinforcement learning; Query plan; Query expansion; Web query classification; Web search query; Set (abstract data type); Theoretical computer science; Data mining; Information retrieval; Artificial intelligence; Search engine; Programming language; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007603198,0.00007345602,0.0000848984,0.0001875988,0.00007018873,0.000171103,0.000654899,0.00002850732,0.0001167443],"category_scores_gemma":[0.00008307735,0.00006812962,0.00001932482,0.0006066874,0.00001995388,0.0005774079,0.0004277679,0.0001943178,0.0001254805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003168716,"about_ca_system_score_gemma":0.00002966577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002452012,"about_ca_topic_score_gemma":0.00002134368,"domain_scores_codex":[0.9986911,0.0001348278,0.0001617785,0.0003407657,0.000410879,0.0002605918],"domain_scores_gemma":[0.9995145,0.00006500912,0.0000279972,0.0002620973,0.00005474444,0.00007571783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004332574,0.00002095339,0.00003850618,0.00002320572,0.000002909393,0.0000137109,0.0004294941,0.9551786,0.000003371142,0.01982058,0.0007231954,0.0237411],"study_design_scores_gemma":[0.000287849,0.0001371128,0.00008621511,0.000009862727,5.509426e-7,9.132816e-8,0.0003030953,0.9914608,0.0001129118,0.00007488273,0.007440711,0.00008591401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00003587942,0.0001334387,0.9572331,0.01946202,0.00004222928,0.0001615158,5.513813e-8,0.0001403602,0.02279142],"genre_scores_gemma":[0.8243152,0.001027306,0.1705885,0.002114588,0.0001633394,0.00007153473,0.00005372316,0.00002150236,0.001644315],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8242793,"threshold_uncertainty_score":0.2778245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1240569034594771,"score_gpt":0.3115986065888434,"score_spread":0.1875417031293664,"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."}}