{"id":"W2063278540","doi":"10.1145/2390045.2390054","title":"Towards intensional answers to OLAP queries for analytical sessions","year":2012,"lang":"en","type":"preprint","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Online analytical processing; Computer science; Session (web analytics); Information retrieval; Extensional definition; Query language; Data cube; Cube (algebra); Database; Data mining; Data warehouse; World Wide Web","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":[0.01392037,0.001605146,0.001820167,0.003316915,0.001352746,0.008424278,0.002931263,0.002161961,0.002913871],"category_scores_gemma":[0.03874717,0.00114603,0.002374968,0.003564287,0.002683361,0.01644071,0.00696284,0.004490158,0.001051287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001297911,"about_ca_system_score_gemma":0.002022512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001546129,"about_ca_topic_score_gemma":0.001897555,"domain_scores_codex":[0.9861136,0.004693905,0.001554492,0.001715741,0.005231778,0.0006904936],"domain_scores_gemma":[0.9790759,0.009403458,0.002155129,0.004623402,0.004085441,0.0006565469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006401376,0.0004298189,0.003733445,0.0007856568,0.0002826737,0.000610551,0.006334484,0.0747871,0.0224505,0.6987521,0.0079888,0.1832047],"study_design_scores_gemma":[0.00005948985,0.0001447281,0.0004143798,0.0001303359,0.0001145403,0.0003512817,0.001061508,0.4436568,0.01341645,0.5229973,0.01756615,0.00008709844],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01140511,0.0001539202,0.9851373,0.0005428423,0.00002392993,0.0001522374,0.0002390465,0.001190158,0.001155286],"genre_scores_gemma":[0.1214337,0.0003306315,0.8742219,0.0004213281,0.000163243,0.0003170169,0.001162193,0.0002935969,0.0016563],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01392037,"threshold_uncertainty_score":0.07361889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05600656242751541,"score_gpt":0.3422436827572888,"score_spread":0.2862371203297734,"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."}}