{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006626886,0.0002460809,0.0002844383,0.0005483517,0.0001997293,0.0003113437,0.001887804,0.00005427772,0.00004220869],"category_scores_gemma":[0.0001151465,0.0002385513,0.00006025504,0.0008076304,0.00004824142,0.002097528,0.0001385292,0.0006470979,0.00008572257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003500802,"about_ca_system_score_gemma":0.00006131239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002611039,"about_ca_topic_score_gemma":0.002281161,"domain_scores_codex":[0.9979141,0.0001053232,0.0004442433,0.0006881533,0.0004162808,0.0004319249],"domain_scores_gemma":[0.9966849,0.0002652427,0.0001163541,0.002760135,0.0000448376,0.0001284982],"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":[0.000131113,0.004838792,0.01082829,0.000864495,0.000425751,0.001712182,0.001355733,0.008771725,0.0320848,0.04095716,0.006051922,0.891978],"study_design_scores_gemma":[0.003738289,0.0002094551,0.005206101,0.001022248,0.00007083258,0.000217102,0.001764723,0.8333502,0.002093875,0.0001466791,0.1503901,0.001790398],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0195941,0.00006175797,0.9753745,0.0002885505,0.00238569,0.0004255073,0.001164809,0.0003261243,0.0003789094],"genre_scores_gemma":[0.9424059,0.00005133276,0.05584538,0.0001727026,0.0001817105,0.00007571498,0.0006269836,0.00002855332,0.0006117696],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9228117,"threshold_uncertainty_score":0.9727841,"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."}}