{"id":"W2131164945","doi":"10.14778/2367502.2367512","title":"Solving big data challenges for enterprise application performance management","year":2012,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":232,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scalability; Big data; Data science; Context (archaeology); Analytics; Instrumentation (computer programming); Data management; Enterprise system; System monitoring; Database; Data mining; Operating system","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":[0.0008809931,0.0001428866,0.0001352056,0.00007748249,0.0001846294,0.00005755069,0.002611291,0.00002620164,4.464295e-7],"category_scores_gemma":[0.0000153469,0.0001012193,0.00006350873,0.0001803034,0.0000296357,0.0001113316,0.003052684,0.00006653109,0.000005970372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005730093,"about_ca_system_score_gemma":0.000004858753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004316537,"about_ca_topic_score_gemma":3.160828e-7,"domain_scores_codex":[0.9986486,0.000004967377,0.0002523644,0.0003754897,0.0003473158,0.0003712794],"domain_scores_gemma":[0.9989534,0.0000296101,0.0002397541,0.0006539021,0.0000636092,0.00005970939],"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":[0.00001463677,0.0003582774,0.002619779,0.0008979289,0.000117672,4.889231e-8,0.001931207,0.0001315,0.0008722499,0.05681726,0.00227077,0.9339687],"study_design_scores_gemma":[0.00250801,0.0003141684,0.03949105,0.0009266238,0.0002833625,0.0000233279,0.001932313,0.474215,0.03387995,0.004166326,0.4411833,0.001076529],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.655544,0.008390632,0.2229202,0.01774422,0.0053523,0.007983917,0.00001634658,0.0009174457,0.08113093],"genre_scores_gemma":[0.9845365,0.0002106015,0.01436515,0.000108363,0.000286751,0.0001456365,0.000001048362,0.00001133142,0.0003346382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9328921,"threshold_uncertainty_score":0.4852472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0504586435494311,"score_gpt":0.2445988120777852,"score_spread":0.1941401685283541,"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."}}