{"id":"W1998415522","doi":"10.4018/ijdwm.2015010102","title":"Parallel Real-Time OLAP on Multi-Core Processors","year":2015,"lang":"en","type":"article","venue":"International Journal of Data Warehousing and Mining","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Online analytical processing; Computer science; Data warehouse; Data cube; Benchmark (surveying); Database; Xeon Phi; Decision support system; Data mining; Parallel computing","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":[0.0005667725,0.0001002818,0.0001578295,0.0001389977,0.00005688926,0.0001170938,0.000817732,0.00002756451,0.000001908491],"category_scores_gemma":[0.0002820921,0.00007975136,0.00002104046,0.00007197226,0.00003448152,0.001870367,0.0004541315,0.0001072715,0.000006773752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003922109,"about_ca_system_score_gemma":0.0001450789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003153438,"about_ca_topic_score_gemma":0.000006199371,"domain_scores_codex":[0.9988639,0.00002808,0.0003296119,0.0002093047,0.0004481747,0.0001209535],"domain_scores_gemma":[0.9987981,0.00008447774,0.0003519743,0.000296391,0.0003478198,0.0001212785],"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":[0.0009111873,0.0008439221,0.007989847,0.0000943878,0.0008154809,0.003126578,0.02504536,0.011531,0.004342136,0.02510201,0.04090559,0.8792925],"study_design_scores_gemma":[0.01248058,0.00164095,0.003059657,0.004864413,0.00009077101,0.007492066,0.005628699,0.5309762,0.001064913,0.00158664,0.4294519,0.001663228],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1408123,0.000437064,0.8559176,0.0008078585,0.001279164,0.00006202897,0.0001112282,0.00006094034,0.0005117557],"genre_scores_gemma":[0.1670862,0.0001097559,0.8318975,0.0001887097,0.0004582411,8.723484e-7,0.00005773778,0.00001361527,0.0001873339],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8776293,"threshold_uncertainty_score":0.3252166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1509179829619319,"score_gpt":0.365363937577586,"score_spread":0.2144459546156541,"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."}}