{"id":"W2923407516","doi":"10.1145/3299869.3319861","title":"Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Computer science; Search engine indexing; Overhead (engineering); Exploit; Online analytical processing; Outlier; Column (typography); Key (lock); Data mining; Access method; Data structure; Database; Information retrieval; Data warehouse; Computer network; Artificial intelligence","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":[0.0003713799,0.0001233442,0.0001577814,0.00006362594,0.0002266204,0.00009034042,0.0002640391,0.00004843282,0.000216507],"category_scores_gemma":[0.00003676681,0.0001197523,0.00003824731,0.0002194154,0.00001205685,0.001662516,0.0002415094,0.0001627981,0.0002654073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003839318,"about_ca_system_score_gemma":0.00006891564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008081647,"about_ca_topic_score_gemma":0.00001636736,"domain_scores_codex":[0.9988694,0.00006526495,0.0002449802,0.000356319,0.0001909678,0.0002730284],"domain_scores_gemma":[0.9991213,0.0002133709,0.0001078581,0.000428999,0.00005192163,0.00007656502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002656613,0.0000168874,0.001080344,0.00002583053,0.00001507828,0.000007828332,0.0005999213,0.0001768075,0.03853995,0.9505573,0.003611128,0.005366282],"study_design_scores_gemma":[0.003746005,0.0005849979,0.001394312,0.0006882311,0.00002582989,0.0002968296,0.00890406,0.3513713,0.2646621,0.02717763,0.3379392,0.003209432],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008715794,0.00006689665,0.9753454,0.0001056439,0.0004749515,0.0002158004,0.00001080138,0.0002906249,0.01477411],"genre_scores_gemma":[0.5838376,0.000003678448,0.4092589,0.0004782438,0.00004832479,0.00002531284,0.00002497811,0.00001730581,0.006305615],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9233797,"threshold_uncertainty_score":0.4883355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00951753844807349,"score_gpt":0.2141737136724857,"score_spread":0.2046561752244122,"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."}}