{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002479594,0.001147099,0.001423616,0.002832868,0.0009408686,0.002982806,0.002970197,0.0007064992,0.003004049],"category_scores_gemma":[0.01206428,0.0008367881,0.0008216513,0.004762108,0.001087228,0.006700736,0.00371762,0.001405307,0.002115679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009391414,"about_ca_system_score_gemma":0.002872812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002040598,"about_ca_topic_score_gemma":0.002509042,"domain_scores_codex":[0.9967113,0.0004129523,0.0004514926,0.0005299731,0.001588335,0.0003059342],"domain_scores_gemma":[0.9893268,0.001852902,0.00137957,0.004162854,0.002842552,0.0004353098],"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":[0.001789964,0.0007377033,0.02588785,0.0007210317,0.0002473437,0.0007340232,0.001260477,0.06241489,0.1698736,0.04909525,0.0286575,0.6585804],"study_design_scores_gemma":[0.0001560361,0.0008388713,0.003452873,0.00007878996,0.0001487068,0.001050474,0.0002978499,0.7878232,0.160133,0.01827971,0.02754532,0.0001951358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04966715,0.0005983131,0.9314129,0.0002781682,0.0001035929,0.0002430225,0.001011546,0.01484365,0.001841741],"genre_scores_gemma":[0.3628564,0.0004842396,0.6281764,0.0003476821,0.0001630215,0.0003021371,0.003842315,0.001092659,0.002735202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003004049,"threshold_uncertainty_score":0.0131135,"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."}}