{"id":"W3176513991","doi":"10.1109/icde51399.2021.00183","title":"Summarizing Provenance of Aggregate Query Results in Relational Databases","year":2021,"lang":"en","type":"article","venue":"","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Provenance; Automatic summarization; Aggregate (composite); Information retrieval; Relational database; Database; Process (computing); Overhead (engineering)","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":[0.01212065,0.0007190706,0.001058812,0.004403337,0.001262674,0.005372451,0.001404691,0.0008796118,0.0008962183],"category_scores_gemma":[0.0481782,0.0005739467,0.0009358525,0.005740223,0.000832085,0.007007879,0.002236282,0.001009666,0.0004655256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001146599,"about_ca_system_score_gemma":0.001717282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003317278,"about_ca_topic_score_gemma":0.003686398,"domain_scores_codex":[0.9880876,0.004107217,0.001611439,0.001054151,0.004805119,0.0003344943],"domain_scores_gemma":[0.9602928,0.0204134,0.003471895,0.008778778,0.006512424,0.0005306763],"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":[0.001549907,0.0003863101,0.02202296,0.00183386,0.0005206999,0.0008832654,0.007850582,0.1743734,0.02646418,0.0864336,0.01319714,0.6644841],"study_design_scores_gemma":[0.0001914762,0.0007542914,0.007051469,0.0004690909,0.0005870656,0.000923516,0.002456354,0.7246439,0.05032579,0.1699651,0.04244988,0.000182054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08528261,0.003909085,0.9012409,0.0009637183,0.0001011009,0.0004244935,0.002244643,0.003521963,0.0023115],"genre_scores_gemma":[0.5167047,0.002426414,0.4740239,0.0001753134,0.0001776985,0.0002476724,0.004505018,0.0004089305,0.001330503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01212065,"threshold_uncertainty_score":0.06410086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2522167658801537,"score_gpt":0.3989016147330034,"score_spread":0.1466848488528497,"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."}}