{"id":"W2527695466","doi":"10.1109/bigdatacongress.2016.23","title":"QDrill: Query-Based Distributed Consumable Analytics for Big Data","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Analytics; Big data; Scalability; SQL; Database; Data science; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004629,0.002049309,0.001091389,0.001764931,0.0007543338,0.003809509,0.007958991,0.001197781,0.01069266],"category_scores_gemma":[0.01250737,0.001349954,0.00138724,0.002275416,0.001683885,0.006735148,0.008263028,0.002935404,0.00543424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001660377,"about_ca_system_score_gemma":0.002418264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007770457,"about_ca_topic_score_gemma":0.009167301,"domain_scores_codex":[0.9955447,0.0007267625,0.0004380191,0.001132499,0.001843537,0.0003143977],"domain_scores_gemma":[0.9916489,0.002892737,0.0004067359,0.003382888,0.00106729,0.0006014048],"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":[0.003828296,0.001349639,0.01258226,0.002403168,0.0007090888,0.001108943,0.001172423,0.0583518,0.04030417,0.0337364,0.3694837,0.4749701],"study_design_scores_gemma":[0.0009327146,0.0003356557,0.003370954,0.0001123441,0.0001037595,0.0004443371,0.0003111098,0.8328395,0.03239138,0.03917403,0.08977379,0.0002103374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01162339,0.0006685451,0.5564164,0.000817799,0.0001965651,0.0005728502,0.005261913,0.4199761,0.004466528],"genre_scores_gemma":[0.2875732,0.0007039683,0.6368386,0.00242566,0.0001812223,0.001011091,0.03930483,0.02213882,0.009822605],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01069266,"threshold_uncertainty_score":0.03577048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1120343101244934,"score_gpt":0.2994594151444289,"score_spread":0.1874251050199355,"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."}}