{"id":"W2143682328","doi":"10.1007/s00778-004-0128-2","title":"Supporting top-k join queries in relational databases","year":2004,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":387,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Joins; Computer science; Join (topology); Sort-merge join; Query optimization; Ranking (information retrieval); Hash join; Database; Relational database; Heuristic; Query language; Sargable; Information retrieval; Rank (graph theory); Heuristics; Relational algebra; Query expansion; Theoretical computer science; Web search query; Search engine; Programming language; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005228457,0.001087648,0.002600929,0.001444186,0.00243184,0.008435749,0.005038479,0.002322149,0.005461701],"category_scores_gemma":[0.024543,0.001496007,0.001209621,0.003480641,0.002048505,0.01740723,0.006402438,0.002378959,0.003628027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009942695,"about_ca_system_score_gemma":0.002183656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003360765,"about_ca_topic_score_gemma":0.004614528,"domain_scores_codex":[0.9910232,0.001505855,0.001461646,0.001354981,0.003473944,0.001180373],"domain_scores_gemma":[0.9746523,0.01198386,0.00128786,0.008777367,0.002372137,0.0009265894],"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":[0.009171318,0.001244991,0.03337821,0.002790584,0.0007308449,0.00222956,0.004358225,0.08303259,0.07917921,0.1042169,0.05763362,0.622034],"study_design_scores_gemma":[0.0005810113,0.0006774622,0.003055695,0.0001885187,0.0005458107,0.002211432,0.001933504,0.6167038,0.0685297,0.2791406,0.02617003,0.0002623973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2319163,0.003700186,0.7254238,0.00212552,0.000427445,0.0002326098,0.002511929,0.02190468,0.01175753],"genre_scores_gemma":[0.7734467,0.001022039,0.2171907,0.0003451017,0.0002487903,0.0000756262,0.002636575,0.001055536,0.00397895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008435749,"threshold_uncertainty_score":0.02765107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04003804845750331,"score_gpt":0.2958259358899701,"score_spread":0.2557878874324668,"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."}}