{"id":"W2098416578","doi":"10.1145/335191.335419","title":"Efficient and extensible algorithms for multi query optimization","year":2000,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":409,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Query optimization; Heuristics; Benchmark (surveying); Overhead (engineering); Greedy algorithm; Heuristic; Algorithm; Data mining; Artificial intelligence","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":[0.0002295013,0.0001314743,0.0001364876,0.00008989274,0.0001522858,0.0002120198,0.00067268,0.00004419554,0.00005851686],"category_scores_gemma":[0.00005787947,0.0001207758,0.00004288409,0.0002313686,0.00002944756,0.0003262915,0.000262134,0.00005367472,0.00003121945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001555317,"about_ca_system_score_gemma":0.00001430133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003722021,"about_ca_topic_score_gemma":0.000004304424,"domain_scores_codex":[0.9989354,0.00002295325,0.0001789507,0.0004649151,0.0001330828,0.0002647201],"domain_scores_gemma":[0.9990844,0.00008123619,0.00004796709,0.0006637301,0.00004995728,0.0000727315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008155907,0.00007509506,0.00005151922,0.00001321532,0.0000129061,0.000004346927,0.00008119923,0.01583252,0.00001669655,0.0006743433,0.001985737,0.9812443],"study_design_scores_gemma":[0.0005906847,0.00008373343,0.0002380324,0.00001304947,0.000008778405,0.000003559371,0.00001068818,0.9842018,0.00007027005,0.00041765,0.01418564,0.0001761105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005635933,0.0001240355,0.992231,0.0006004897,0.0003947254,0.0003865989,0.00000934215,0.0001718424,0.0004460661],"genre_scores_gemma":[0.003481713,0.0001408554,0.9923778,0.0003289561,0.0001115072,0.00004865535,0.00002483227,0.00001428488,0.003471451],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9810681,"threshold_uncertainty_score":0.4925096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04290867931345283,"score_gpt":0.2807725920124819,"score_spread":0.2378639126990291,"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."}}