{"id":"W2573482191","doi":"10.1109/ictai.2016.0151","title":"An Efficient Approach for Mining Frequent Patterns over Uncertain Data Streams","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Data stream mining; Uncertain data; Sliding window protocol; Data mining; Probabilistic logic; Big data; Data stream; Tree (set theory); Task (project management); Data science; Artificial intelligence; Window (computing); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003193352,0.0001083144,0.00009629113,0.00003947412,0.0001107433,0.00016673,0.002124053,0.00003213903,0.00002139743],"category_scores_gemma":[0.00001967313,0.00006664999,0.00002289347,0.0001138528,0.00002396728,0.0004326497,0.0004338225,0.00002515635,0.000008834399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000271203,"about_ca_system_score_gemma":0.00004244707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001197816,"about_ca_topic_score_gemma":0.00001145803,"domain_scores_codex":[0.9986919,0.00001818611,0.0001668894,0.0006924136,0.000171109,0.0002595492],"domain_scores_gemma":[0.9976,0.0001013081,0.0000554626,0.00209592,0.00003606625,0.0001112791],"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":[0.000002388342,0.000397267,0.001201531,0.00001210151,0.00001973799,0.000001148986,0.0002551096,0.0002968297,0.0008809816,0.04549164,0.008738242,0.942703],"study_design_scores_gemma":[0.0003108915,0.00005578141,0.0008613947,0.00001259219,0.000004900569,0.000002709328,0.00008071374,0.9932809,0.0003514634,0.0001074309,0.004775034,0.000156143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01699217,0.000007677091,0.9811146,0.000396556,0.00006970922,0.0002168989,0.0005930453,0.0001420744,0.000467295],"genre_scores_gemma":[0.2504572,0.000002459152,0.7485662,0.0001714275,0.00009440954,0.00008592263,0.0003066875,0.000009601044,0.0003061758],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9929841,"threshold_uncertainty_score":0.3947054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0658649504474581,"score_gpt":0.3203842682322821,"score_spread":0.254519317784824,"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."}}