{"id":"W2102390718","doi":"10.1109/icdew.2007.4401031","title":"Poster Session: Adapting Mixed Workloads to Meet SLOs in Autonomic DBMSs","year":2007,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); Queen's University","funders":"","keywords":"Online transaction processing; Workload; Computer science; Online analytical processing; Adaptation (eye); IBM; Session (web analytics); Database; Malleability; Distributed computing; Operating system; Transaction processing; Database transaction; 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":[0.002375397,0.0007771031,0.000577405,0.0002716859,0.0008507173,0.001667281,0.001217234,0.00122715,0.01925792],"category_scores_gemma":[0.001992676,0.0003412835,0.0004866985,0.0004113939,0.0003159697,0.001771644,0.001298587,0.00171,0.005153144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004044358,"about_ca_system_score_gemma":0.0004322821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000525341,"about_ca_topic_score_gemma":0.0007073279,"domain_scores_codex":[0.9994103,0.0001792776,0.00003705651,0.0001538383,0.0001429407,0.00007663824],"domain_scores_gemma":[0.9988742,0.0002959114,0.00003315146,0.0001649229,0.0003601062,0.000271692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002343936,0.001514226,0.002902143,0.0004995942,0.000143518,0.0007225103,0.001019945,0.02179622,0.1037157,0.007389578,0.3379171,0.5200354],"study_design_scores_gemma":[0.001575988,0.005289318,0.01155269,0.0002592876,0.0002829695,0.002357899,0.001312813,0.3113332,0.08471606,0.02098894,0.5599751,0.0003557071],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1727877,0.005548678,0.6868511,0.01954803,0.01663079,0.002158925,0.001065295,0.01435082,0.08105868],"genre_scores_gemma":[0.4936209,0.00362798,0.3005678,0.004516921,0.01023547,0.001002723,0.002386491,0.001860332,0.1821813],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01925792,"threshold_uncertainty_score":0.06442422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01610701949689363,"score_gpt":0.248493400339947,"score_spread":0.2323863808430534,"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."}}