{"id":"W2197954473","doi":"","title":"Accelerating analytic queries in OLTP environment using DB2 shadow tables","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Online transaction processing; Online analytical processing; Computer science; Transaction processing; Database; Shadow (psychology); Lag; Transactional leadership; Business intelligence; Data warehouse; Database transaction; Operating system","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":[0.0008854986,0.0001808851,0.0002232569,0.0002631503,0.000245704,0.0002679322,0.0004685572,0.00003224418,0.000001950473],"category_scores_gemma":[0.0001776529,0.0001710113,0.00002239168,0.0005840336,0.0001236483,0.001922635,0.0006547041,0.000118686,0.00000275583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009733027,"about_ca_system_score_gemma":0.00006424957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006194387,"about_ca_topic_score_gemma":0.000007512599,"domain_scores_codex":[0.9983891,0.00002205932,0.0002546018,0.0005517627,0.0003479531,0.0004345425],"domain_scores_gemma":[0.9991899,0.0001297813,0.0000584826,0.0004503955,0.00004307017,0.000128425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000019884,0.0000397787,0.01626449,0.0001821829,0.00001154338,0.00003764592,0.002187168,0.6886582,0.005809695,0.09549985,0.00002077464,0.1912867],"study_design_scores_gemma":[0.0001114095,0.00002464979,0.005253672,0.0001050456,0.000001417308,0.0000262199,0.00001269138,0.991667,0.0005544498,0.0001037378,0.001905216,0.0002345146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1017782,0.0001651472,0.8975433,0.00002624492,0.0002770534,0.00008076592,0.000001335052,0.0001216284,0.000006410199],"genre_scores_gemma":[0.369547,0.00001051287,0.6302886,0.00006034349,0.00007666161,0.000005299829,7.879822e-7,0.000006974057,0.000003829448],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3030088,"threshold_uncertainty_score":0.6973639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378997099188476,"score_gpt":0.2081185444131823,"score_spread":0.1943285734212975,"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."}}