{"id":"W1681014725","doi":"","title":"A multi-dimensional histogram for selectivity estimation and fast approximate query answering","year":2003,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Histogram; Computer science; Online analytical processing; Data mining; Query optimization; Oracle; Histogram matching; Set (abstract data type); SQL; Database; Information retrieval; Artificial intelligence; Data warehouse; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002008965,0.00065242,0.001145434,0.003221023,0.0008520989,0.002040561,0.002051077,0.0009546176,0.003656732],"category_scores_gemma":[0.01540453,0.0005077851,0.0006230272,0.005944497,0.0006870869,0.005218986,0.001555803,0.001466206,0.001264898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009892385,"about_ca_system_score_gemma":0.001385374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003772925,"about_ca_topic_score_gemma":0.002663977,"domain_scores_codex":[0.9972562,0.0006395974,0.0001792177,0.0004071413,0.001307155,0.0002106308],"domain_scores_gemma":[0.9908708,0.003931103,0.0005690723,0.002180025,0.002185082,0.0002638866],"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":[0.0009011786,0.0002079407,0.007920323,0.0003348653,0.00009340898,0.0001436256,0.0002876088,0.06590272,0.02645119,0.0375699,0.01617535,0.844012],"study_design_scores_gemma":[0.0001023615,0.0002497986,0.002858698,0.00004110801,0.00004903936,0.0006915567,0.0002022504,0.9205394,0.0291008,0.02861506,0.01739832,0.0001516159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007220343,0.000330665,0.9886435,0.00009704167,0.00005301979,0.00006651555,0.0003788769,0.002526215,0.0006838597],"genre_scores_gemma":[0.2620549,0.0006041104,0.7336556,0.000159684,0.0001807179,0.0002876835,0.001680118,0.0003785918,0.0009986059],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003772925,"threshold_uncertainty_score":0.01223302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1053180068242907,"score_gpt":0.3934269964964237,"score_spread":0.288108989672133,"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."}}