{"id":"W4381329253","doi":"10.1145/3589298","title":"Computing the Difference of Conjunctive Queries Efficiently","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ACM on Management of Data","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Rewriting; Computer science; Query optimization; Heuristics; Operator (biology); Spatial query; Benchmark (surveying); Boolean conjunctive query; Set (abstract data type); Class (philosophy); Conjunctive query; SQL; Theoretical computer science; Algorithm; Sargable; Relational database; Search engine; Database; Information retrieval; Web search query","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":[0.003904338,0.001499114,0.00254915,0.001879831,0.001074226,0.004431486,0.004809613,0.001095627,0.006335379],"category_scores_gemma":[0.01875646,0.001285256,0.002298489,0.003207238,0.001956907,0.01254608,0.004223195,0.003002884,0.001743643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002686167,"about_ca_system_score_gemma":0.002971568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007076325,"about_ca_topic_score_gemma":0.00864323,"domain_scores_codex":[0.9888194,0.001160105,0.001044368,0.002558505,0.005366215,0.001051363],"domain_scores_gemma":[0.9839484,0.009363238,0.0008428504,0.00349149,0.002043106,0.0003109139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003098646,0.001108451,0.01603581,0.001701703,0.0008155484,0.001097064,0.001431762,0.1371795,0.0947395,0.1280542,0.02071015,0.5940276],"study_design_scores_gemma":[0.0002866439,0.0004769873,0.002261965,0.00004086325,0.0002381241,0.0006232569,0.0004665975,0.7633927,0.05571991,0.168871,0.007519176,0.0001027857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1787084,0.0009691645,0.7968478,0.00113762,0.0002396522,0.0003942574,0.001575864,0.013154,0.006973244],"genre_scores_gemma":[0.4765908,0.0002503703,0.5156232,0.0005904895,0.0001587988,0.0001527021,0.00338564,0.001177794,0.002070216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007076325,"threshold_uncertainty_score":0.02119392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06453656104710064,"score_gpt":0.3010111735680607,"score_spread":0.23647461252096,"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."}}