{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003973938,0.0001299432,0.0001911866,0.0001769878,0.00007170608,0.000431914,0.002013834,0.00003156466,0.00003598],"category_scores_gemma":[0.0001601365,0.0001151382,0.00003482439,0.00009652906,0.00002830014,0.002314019,0.001136386,0.0001637155,0.000009441925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003485958,"about_ca_system_score_gemma":0.0001218711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004581173,"about_ca_topic_score_gemma":0.00004280454,"domain_scores_codex":[0.9985904,0.00004238245,0.0004393947,0.0003586324,0.0004147584,0.0001544262],"domain_scores_gemma":[0.9983802,0.0003354617,0.0003889865,0.0005741559,0.0002314956,0.00008972843],"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":[0.0000378892,0.0001202735,0.04487076,0.000008865843,0.0003155466,0.0001551482,0.001909057,0.000122435,0.004186158,0.0003068561,0.003596639,0.9443704],"study_design_scores_gemma":[0.003267956,0.0001779529,0.01045758,0.001671199,0.00009626555,0.0008922803,0.003292632,0.8013502,0.002381547,0.0001411341,0.1755742,0.0006969728],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.593199,0.0001598666,0.4044153,0.0003331324,0.0009870235,0.00003283124,0.0007950754,0.00002825507,0.00004953701],"genre_scores_gemma":[0.640685,0.00006748219,0.3580942,0.0001590622,0.000314157,6.858752e-7,0.0006383354,0.00001043033,0.00003057209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9436734,"threshold_uncertainty_score":0.4695202,"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."}}