{"id":"W4380875476","doi":"10.1145/3579371.3589082","title":"Profiling Hyperscale Big Data Processing","year":2023,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada); University of Waterloo","funders":"National Science Foundation","keywords":"Big data; Computer science; Profiling (computer programming); Data processing; Data science; 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":[0.0004084693,0.00007171717,0.00007086143,0.00009871201,0.0001702704,0.0002006502,0.001670446,0.00001962101,0.000001643297],"category_scores_gemma":[0.00002443576,0.00005637116,0.00001736751,0.0008534085,0.00001624346,0.00003500269,0.002466356,0.00006514796,0.0001890939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000904522,"about_ca_system_score_gemma":0.00002748431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009961558,"about_ca_topic_score_gemma":0.000001693564,"domain_scores_codex":[0.998974,0.00001825759,0.0001161908,0.0004252223,0.0002180145,0.0002483284],"domain_scores_gemma":[0.9989308,0.00003165746,0.00002907193,0.0009415612,0.00002016948,0.00004670307],"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":[5.701841e-7,0.00001970432,0.000402365,0.00003368867,0.00000663982,0.0000214462,0.0001697823,0.001996726,0.0001741108,0.002828591,0.007971468,0.9863749],"study_design_scores_gemma":[0.00008709946,0.000008150098,0.0003839143,0.00001656568,0.000002135661,0.000003635193,0.0001208101,0.9760276,0.0001958495,0.0002909164,0.02276392,0.00009937456],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1748398,0.0001576808,0.7474504,0.007054506,0.001260493,0.0002646527,0.000002132494,0.004441978,0.06452831],"genre_scores_gemma":[0.938608,0.000003036455,0.05026627,0.0005010403,0.0004470251,0.00000546502,0.00001135092,0.00001304526,0.01014476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9862756,"threshold_uncertainty_score":0.3104132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09851888182743256,"score_gpt":0.2832079028796989,"score_spread":0.1846890210522664,"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."}}