{"id":"W2989849422","doi":"10.4018/ijdwm.2020010101","title":"Mining Integrated Sequential Patterns From Multiple Databases","year":2019,"lang":"en","type":"article","venue":"International Journal of Data Warehousing and Mining","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Royal Bank of Canada; University of Windsor","funders":"","keywords":"Computer science; Tuple; Sequence (biology); Database transaction; Sequence database; Data mining; GSP Algorithm; Table (database); Database; Position (finance); Association rule learning; Apriori algorithm; Mathematics","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":[0.001908441,0.00118705,0.001629013,0.00831327,0.0008354652,0.002788879,0.001782281,0.0009556513,0.001856881],"category_scores_gemma":[0.01087152,0.0007931681,0.001493345,0.01052714,0.0004238926,0.004301711,0.00195011,0.001009036,0.001037868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005680938,"about_ca_system_score_gemma":0.001397545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00178623,"about_ca_topic_score_gemma":0.002598411,"domain_scores_codex":[0.9954182,0.0004871249,0.0007293042,0.001100849,0.002000024,0.0002644487],"domain_scores_gemma":[0.993854,0.002209499,0.0009300734,0.0009779844,0.0017263,0.0003021171],"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":[0.001129196,0.0005995979,0.06750132,0.001370942,0.0009980274,0.005160055,0.0006464886,0.03373677,0.0196381,0.009590455,0.007489883,0.8521392],"study_design_scores_gemma":[0.0001719548,0.000942892,0.02617582,0.0003799822,0.0009257763,0.007393497,0.001879042,0.812784,0.03985806,0.07219917,0.03715146,0.0001385122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2351026,0.003515486,0.7413842,0.0009546895,0.0002403241,0.0008826099,0.008559629,0.004490048,0.004870418],"genre_scores_gemma":[0.4477171,0.001513199,0.5332083,0.0002368526,0.0001730805,0.0004679426,0.01435191,0.0001702589,0.002161338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00831327,"threshold_uncertainty_score":0.01009291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0751542230104232,"score_gpt":0.3248460621996281,"score_spread":0.2496918391892049,"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."}}