{"id":"W2080555007","doi":"10.1145/1806907.1806909","title":"Continuous online index tuning in moving object databases","year":2010,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Media Development Authority - Singapore","keywords":"Computer science; Granularity; B-tree; Overhead (engineering); Search engine indexing; Tree (set theory); Grid; Data mining; Workload; Set (abstract data type); Object (grammar); Database; Binary tree; Information retrieval; Algorithm; Artificial intelligence; Mathematics","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":[0.002764337,0.0008555252,0.001457661,0.001739928,0.001170492,0.0029372,0.003988688,0.0009649663,0.001234371],"category_scores_gemma":[0.01169054,0.0008134115,0.0004406682,0.003571848,0.0008167914,0.005784246,0.003230914,0.001010173,0.0007534404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079001,"about_ca_system_score_gemma":0.001329181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004184304,"about_ca_topic_score_gemma":0.003741119,"domain_scores_codex":[0.9971294,0.0004205107,0.0003096644,0.0006414478,0.001208231,0.000290712],"domain_scores_gemma":[0.995137,0.001529926,0.0004266973,0.001780939,0.0007201069,0.0004053336],"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":[0.001861351,0.0006953197,0.01551937,0.0005334949,0.0001937813,0.0006266456,0.0008018925,0.1586429,0.05557637,0.0150023,0.02525389,0.7252927],"study_design_scores_gemma":[0.000125762,0.0002449724,0.00293917,0.00003152365,0.00004676721,0.0003772349,0.0002107677,0.9523668,0.01697745,0.01369774,0.01291677,0.00006496484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2483349,0.006258551,0.7087886,0.0005011486,0.0003841905,0.0004225628,0.00100842,0.02613952,0.008162039],"genre_scores_gemma":[0.7060131,0.0009675279,0.2880599,0.0002683095,0.0001781021,0.000216054,0.001605979,0.0008411545,0.001849918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004184304,"threshold_uncertainty_score":0.01461935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02813362435550357,"score_gpt":0.2745241931610565,"score_spread":0.2463905688055529,"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."}}