{"id":"W2148682126","doi":"10.1109/ipdps.2012.85","title":"Query Optimization and Execution in a Parallel Analytics DBMS","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Online analytical processing; Computer science; Scalability; Query optimization; Sargable; Data warehouse; View; Database; Search engine indexing; NoSQL; Analytics; Big data; Query expansion; Parallel database; Relational database management system; Relational database; Web search query; Information retrieval; Data mining; Search engine; Database design","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":[0.001655139,0.0007485164,0.0008958414,0.000634324,0.001366488,0.003051134,0.001817413,0.0007757265,0.002534297],"category_scores_gemma":[0.002767148,0.0005458072,0.0007163419,0.001552302,0.0009677745,0.002621763,0.001860144,0.001211291,0.001278988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001147927,"about_ca_system_score_gemma":0.002565205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00725186,"about_ca_topic_score_gemma":0.00535272,"domain_scores_codex":[0.9977489,0.0003138198,0.0002007788,0.0004490441,0.001058647,0.0002287623],"domain_scores_gemma":[0.9989342,0.0002843805,0.00005907672,0.0003438376,0.0002896074,0.00008886533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002674828,0.001376509,0.01348525,0.000405415,0.000297372,0.001690763,0.00158366,0.3577078,0.1429655,0.1175746,0.04273937,0.317499],"study_design_scores_gemma":[0.0001607887,0.0001579696,0.0007562884,0.00001108284,0.00005342286,0.0002102952,0.0002482344,0.9194219,0.02839233,0.03511437,0.01543906,0.00003425316],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1548314,0.0005496116,0.808046,0.001742529,0.0001937889,0.0003398803,0.0007327917,0.01676938,0.01679455],"genre_scores_gemma":[0.5688643,0.0004181692,0.417287,0.000422259,0.0002347786,0.0002113649,0.001746642,0.0009607267,0.00985479],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00725186,"threshold_uncertainty_score":0.01441932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01738522641094388,"score_gpt":0.2463153529283406,"score_spread":0.2289301265173967,"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."}}