{"id":"W2997878718","doi":"10.1109/iotsms48152.2019.8939171","title":"Analytics Everywhere for Streaming IoT Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Analytics; Cloud computing; Internet of Things; Stream processing; Architecture; Data stream mining; Edge computing; Data analysis; Big data; Enhanced Data Rates for GSM Evolution; Data science; Fog computing; Message broker; Streaming data; Edge device; Distributed computing; World Wide Web; Data mining; Computer network; Artificial intelligence; 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":[0.0002260143,0.00007451075,0.0001159364,0.00005841494,0.00001754037,0.00004136979,0.0003840009,0.00004672707,0.0002299749],"category_scores_gemma":[0.00004016317,0.00006887697,0.00002195549,0.0001063967,0.000005362926,0.00007465704,0.00009035303,0.00006897538,0.0002892855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004350551,"about_ca_system_score_gemma":0.00001647757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000345402,"about_ca_topic_score_gemma":0.00005814897,"domain_scores_codex":[0.9993244,0.000007223662,0.0001238824,0.0001650595,0.0001500137,0.0002294256],"domain_scores_gemma":[0.9990054,0.0001422482,0.000008266324,0.0007722217,0.0000290141,0.00004282112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003303161,0.0000688383,0.1725401,0.002926405,0.0008661922,0.00001261138,0.0003121861,0.1324335,0.03023519,0.002907024,0.6064714,0.05119353],"study_design_scores_gemma":[0.000216404,0.00001472083,0.001182035,0.00002838121,0.000005787244,0.000002051053,0.00008012992,0.8372026,0.0008910194,0.00001598577,0.1602461,0.0001147439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7782827,0.0005515539,0.0830553,0.0001287401,0.001876112,0.001570503,0.0002203106,0.001101295,0.1332135],"genre_scores_gemma":[0.9869355,0.00000456644,0.002959457,0.000008373625,0.0001510279,0.000008148141,0.00004937007,0.00003486253,0.009848721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7047691,"threshold_uncertainty_score":0.3718276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06504593016136516,"score_gpt":0.3061124885251686,"score_spread":0.2410665583638034,"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."}}