{"id":"W2911692497","doi":"10.17762/itii.v7i1.63","title":"Order Preserving Stream Processing in Fog Computing Architectures","year":2021,"lang":"en","type":"article","venue":"INFORMATION TECHNOLOGY IN INDUSTRY","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computation; Cloud computing; Distributed computing; Stream processing; Quality of service; Enhanced Data Rates for GSM Evolution; Data processing; Process (computing); Edge computing; Node (physics); Architecture; Real-time computing; Computer network; Database; Operating system; Engineering; Algorithm; Artificial intelligence","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":[0.0006137821,0.0003830516,0.0004654379,0.0004774025,0.0008412957,0.001704635,0.0009233425,0.0005069639,0.001189053],"category_scores_gemma":[0.001185418,0.0002212568,0.0004399119,0.0007835295,0.0007426236,0.001645484,0.0008272554,0.0008341153,0.0002702585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00118288,"about_ca_system_score_gemma":0.001375296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007054454,"about_ca_topic_score_gemma":0.007837562,"domain_scores_codex":[0.9996372,0.00006089762,0.00003120876,0.00006431137,0.0001284622,0.00007780905],"domain_scores_gemma":[0.9995747,0.0001021658,0.00003874047,0.0001136342,0.0001327659,0.00003804667],"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":[0.0006554186,0.0001806714,0.002386568,0.0002883569,0.0001290586,0.0005698244,0.0005342183,0.4748268,0.02799388,0.2957385,0.01258183,0.1841148],"study_design_scores_gemma":[0.00002849054,0.00006725302,0.0003515297,0.00001784784,0.00002168704,0.00009928854,0.00008222781,0.8779575,0.008974269,0.1048101,0.007570699,0.00001914692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06524248,0.001663014,0.9186828,0.0006910895,0.0002470735,0.0001387533,0.00023781,0.001101086,0.01199594],"genre_scores_gemma":[0.809543,0.001294566,0.1825725,0.0003156659,0.0001236937,0.00007803791,0.0002911593,0.00009518318,0.005686144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007054454,"threshold_uncertainty_score":0.01402676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01023863708930144,"score_gpt":0.2527749421944566,"score_spread":0.2425363051051552,"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."}}