{"id":"W1574590432","doi":"10.4018/978-1-59140-053-0.ch006","title":"Time in Multidimensional Databases","year":2003,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Online analytical processing; Computer science; Data warehouse; Database; Schema (genetic algorithms); Temporal database; Software versioning; Schema evolution; Abstraction; Query language; Data mining; Information retrieval; Database schema; Programming language; Database design; Software","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":[0.002023739,0.0009779955,0.0009413119,0.002429021,0.001239305,0.006858803,0.001663436,0.001397022,0.008544213],"category_scores_gemma":[0.00477081,0.0007079522,0.001105672,0.008233851,0.002322549,0.01195622,0.003222806,0.002837977,0.004001101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00193286,"about_ca_system_score_gemma":0.001053672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001862008,"about_ca_topic_score_gemma":0.001203673,"domain_scores_codex":[0.9977707,0.000556361,0.0002457936,0.0003676827,0.0009482386,0.0001113677],"domain_scores_gemma":[0.9982501,0.0008555886,0.0000878217,0.000497336,0.0002271771,0.00008196186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002428986,0.00001815873,0.000229671,0.0004246483,0.00001977513,0.0001853779,0.0006455001,0.002132814,0.0006211913,0.8692499,0.03218912,0.0942596],"study_design_scores_gemma":[0.00001067561,0.00001742323,0.0001603029,0.0002183967,0.00001895919,0.0004651722,0.0001908285,0.007961149,0.0006378569,0.4631403,0.5271563,0.00002258268],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.005335233,0.1285918,0.7276617,0.007293755,0.003845442,0.0001653321,0.001230709,0.001776929,0.1240991],"genre_scores_gemma":[0.1192832,0.1235461,0.6459219,0.004052099,0.005073642,0.0006623688,0.003187647,0.001240254,0.09703276],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.008544213,"threshold_uncertainty_score":0.02858323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193184685117388,"score_gpt":0.2543738975964139,"score_spread":0.23244205074524,"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."}}