{"id":"W1486245092","doi":"10.1109/bigdataservice.2015.23","title":"Index Selection on MapReduce Relational-Databases","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Index selection; Index (typography); Relational database; Task (project management); Selection (genetic algorithm); Set (abstract data type); Data mining; Process (computing); Database; Database design; Relational database management system; Factor (programming language); Artificial intelligence; Engineering","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":[0.001475805,0.0006134295,0.0007016546,0.0009848512,0.0009656262,0.001948772,0.002023816,0.0003543457,0.001374678],"category_scores_gemma":[0.00339222,0.000411189,0.0006006077,0.001634025,0.0003517223,0.001423498,0.0009793409,0.0005052752,0.000617942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006887512,"about_ca_system_score_gemma":0.00124347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004084253,"about_ca_topic_score_gemma":0.004326076,"domain_scores_codex":[0.998434,0.0003349472,0.0001200273,0.000217273,0.0007797364,0.00011405],"domain_scores_gemma":[0.9987836,0.0003593653,0.00007403608,0.000307593,0.0003831826,0.0000922324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001274306,0.0006505761,0.01079716,0.0008340277,0.0003555116,0.0008364344,0.0006840615,0.2068942,0.05965281,0.02715058,0.02847759,0.6623927],"study_design_scores_gemma":[0.00008930146,0.0001978068,0.002923798,0.00003038341,0.00006380663,0.0003629397,0.000316243,0.9092274,0.04365737,0.02362949,0.01944589,0.00005562857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.128921,0.001767639,0.8363966,0.0004500441,0.0001481466,0.0005686492,0.001314177,0.01999898,0.01043484],"genre_scores_gemma":[0.4922803,0.0005715687,0.5011647,0.0001519008,0.0000634753,0.0002711818,0.001660533,0.0005325882,0.003303759],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004084253,"threshold_uncertainty_score":0.008120954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08623181636501051,"score_gpt":0.2996648312292766,"score_spread":0.2134330148642661,"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."}}