{"id":"W2973341182","doi":"10.3390/e21090902","title":"Optimization of Big Data Scheduling in Social Networks","year":2019,"lang":"en","type":"article","venue":"Entropy","topic":"AI and Multimedia in Education","field":"Computer Science","cited_by":230,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"Natural Science Foundation of Inner Mongolia","keywords":"Computer science; Big data; Scheduling (production processes); Fair-share scheduling; Dynamic priority scheduling; Entropy (arrow of time); Two-level scheduling; Rate-monotonic scheduling; Distributed computing; Mathematical optimization; Data mining; Quality of service; Computer network; Mathematics","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":[0.001312365,0.0010146,0.000768818,0.0005807799,0.0007007722,0.000911811,0.000819141,0.0006134771,0.001301122],"category_scores_gemma":[0.002931217,0.0004662592,0.0005551682,0.0007779537,0.0006318514,0.001455141,0.0007879332,0.0005720676,0.000127503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001833549,"about_ca_system_score_gemma":0.001639455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005722978,"about_ca_topic_score_gemma":0.005041127,"domain_scores_codex":[0.9991075,0.000341388,0.00003670858,0.0001681527,0.0001740976,0.0001721182],"domain_scores_gemma":[0.9986765,0.0007946541,0.0001918436,0.00006453211,0.0001430184,0.0001295389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005283425,0.00002407213,0.0004486853,0.0000370313,0.00002428141,0.00003120656,0.0000262458,0.9857401,0.0007985777,0.005976119,0.0004410163,0.006399802],"study_design_scores_gemma":[0.00000461052,0.00001038409,0.0001092373,0.000001391089,0.000003052513,0.000004265555,0.00001423465,0.996254,0.0001578959,0.003266829,0.0001719728,0.000002026209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1228947,0.0005178756,0.8708358,0.0005258607,0.0001121343,0.000131757,0.0001948762,0.0002233086,0.004563771],"genre_scores_gemma":[0.9314638,0.0002561812,0.06598798,0.00007528482,0.00004413888,0.0001399058,0.0001252222,0.00008116029,0.001826289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005722978,"threshold_uncertainty_score":0.01330334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03703419125600126,"score_gpt":0.2813361211476854,"score_spread":0.2443019298916841,"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."}}