{"id":"W2996780607","doi":"10.4018/jdm.2020010104","title":"A Service Architecture Using Machine Learning to Contextualize Anomaly Detection","year":2019,"lang":"en","type":"article","venue":"Journal of Database Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"University of Ontario Institute of Technology","keywords":"Computer science; Anomaly detection; Context (archaeology); Outlier; Service (business); Set (abstract data type); Dashboard; Feature (linguistics); Architecture; Data mining; Artificial intelligence; Anomaly (physics); Machine learning; Data science","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":[0.002192963,0.000996116,0.0009143919,0.002072899,0.001190756,0.003038751,0.002122186,0.001370309,0.002984342],"category_scores_gemma":[0.005667938,0.0004677592,0.0008785939,0.001800785,0.001035466,0.003291991,0.002684247,0.001523873,0.001170262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001643958,"about_ca_system_score_gemma":0.0019687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01761795,"about_ca_topic_score_gemma":0.01341671,"domain_scores_codex":[0.9983949,0.0003743347,0.000138895,0.0004228425,0.0005104843,0.0001585036],"domain_scores_gemma":[0.9971768,0.0007699439,0.0002020597,0.0007010142,0.0008956664,0.000254467],"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":[0.001296535,0.0008339778,0.02004934,0.0004296972,0.0003513775,0.001305338,0.00185894,0.2714894,0.02792753,0.08513191,0.0237075,0.5656185],"study_design_scores_gemma":[0.00002272637,0.00009443161,0.0006812073,0.00003050903,0.00004709962,0.0001520684,0.0001419632,0.9496184,0.007519822,0.02958449,0.01205623,0.00005104576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02187447,0.000215746,0.9453064,0.001002518,0.0001121793,0.0001653876,0.0003094731,0.02833828,0.002675591],"genre_scores_gemma":[0.4215544,0.0002781039,0.5728768,0.0004242989,0.00009965083,0.0001771023,0.001179655,0.0006826381,0.002727194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01761795,"threshold_uncertainty_score":0.03503078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540857362089905,"score_gpt":0.2594359076302085,"score_spread":0.2440273340093095,"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."}}