{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002297307,0.001774781,0.001020055,0.002232522,0.001167879,0.005452775,0.002940947,0.000677932,0.01091298],"category_scores_gemma":[0.007598153,0.001044099,0.0007536871,0.004449649,0.0005110861,0.006646633,0.002960876,0.001476103,0.005155437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001239405,"about_ca_system_score_gemma":0.002796501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01211476,"about_ca_topic_score_gemma":0.008098957,"domain_scores_codex":[0.9968977,0.0004118303,0.0003129197,0.0006673777,0.00133797,0.0003722069],"domain_scores_gemma":[0.994271,0.00154994,0.0002682502,0.002139298,0.001369576,0.0004018982],"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":[0.005043528,0.0008786129,0.02114537,0.001070813,0.0003493548,0.001358645,0.001533963,0.04459948,0.08531756,0.02788515,0.1063175,0.7045001],"study_design_scores_gemma":[0.0006712076,0.0006485119,0.006382342,0.0001148391,0.0002186017,0.0008067826,0.0008862627,0.7491995,0.09886966,0.02397223,0.1180307,0.0001992778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.108014,0.002597866,0.6860771,0.0007911702,0.000680747,0.0004763999,0.006191844,0.1788929,0.01627802],"genre_scores_gemma":[0.4398991,0.001145971,0.5326678,0.000432807,0.0002347034,0.0002782586,0.0120643,0.004357157,0.008919832],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01211476,"threshold_uncertainty_score":0.03650755,"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."}}