{"id":"W4394766409","doi":"10.1145/3657286","title":"A Systematic Literature Review of Novelty Detection in Data Streams: Challenges and Opportunities","year":2024,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Novelty; Data stream mining; Novelty detection; Data science; Field (mathematics); Taxonomy (biology); Key (lock); STREAMS; Artificial intelligence; Task (project management); Systematic review; Machine learning; Data mining; Systems engineering","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":[0.008984334,0.001268616,0.002761103,0.01402178,0.0007048085,0.002928202,0.00155788,0.001798758,0.004712618],"category_scores_gemma":[0.04983584,0.0007811565,0.002758206,0.01445771,0.0008851013,0.00518132,0.001568233,0.001632881,0.001356189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001667797,"about_ca_system_score_gemma":0.01055151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002533626,"about_ca_topic_score_gemma":0.006384499,"domain_scores_codex":[0.9954999,0.001452849,0.001095306,0.0004997847,0.001310421,0.0001417613],"domain_scores_gemma":[0.9387928,0.05060592,0.003271296,0.0009218326,0.005920445,0.0004877136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001478167,0.00006887529,0.001382486,0.2110223,0.000878935,0.0001455649,0.000324719,0.000623211,0.0004332981,0.00505497,0.02938732,0.7505305],"study_design_scores_gemma":[0.00009167134,0.0003625779,0.005871166,0.4010835,0.00586776,0.001245734,0.0007717326,0.001057482,0.0007093124,0.01285173,0.5699625,0.0001248193],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003311794,0.9959841,0.001329396,0.001164269,0.0003176703,0.00006324709,0.0001902422,0.00002595183,0.0005938946],"genre_scores_gemma":[0.001822159,0.9949371,0.001870585,0.00059701,0.0003106947,0.00009270577,0.000207492,0.000008972057,0.0001532434],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01402178,"threshold_uncertainty_score":0.04751426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2235691234449488,"score_gpt":0.3811554367388975,"score_spread":0.1575863132939487,"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."}}