{"id":"W4312235560","doi":"10.1007/978-3-031-21541-4_13","title":"Magic Sets in Interpolation-Based Rule Driven Query Optimization","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Query optimization; MAGIC (telescope); Transformation (genetics); Query language; Sargable; Interpolation (computer graphics); Query expansion; Set (abstract data type); Theoretical computer science; Extension (predicate logic); RDF query language; Algorithm; Web search query; Web query classification; Data mining; Information retrieval; Programming language; Artificial intelligence; Search engine","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":[0.003550314,0.0007992903,0.002483031,0.001569363,0.0007642152,0.001951319,0.003481162,0.001247994,0.006318629],"category_scores_gemma":[0.009745836,0.001172504,0.001510277,0.002996384,0.001766707,0.003847768,0.003723693,0.003346137,0.001234117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011683,"about_ca_system_score_gemma":0.001354333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003754151,"about_ca_topic_score_gemma":0.003088815,"domain_scores_codex":[0.9961604,0.001153052,0.0002944988,0.0004549088,0.001670094,0.0002670239],"domain_scores_gemma":[0.9950055,0.003588286,0.0001286601,0.0007732776,0.0004158194,0.0000883926],"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":[0.0009461164,0.0002375452,0.0006836792,0.0003532056,0.0001250881,0.0001532665,0.0002313529,0.3139436,0.006118107,0.2564538,0.00780379,0.4129505],"study_design_scores_gemma":[0.00002779059,0.0000706046,0.00007101518,0.00002542995,0.00002355336,0.00004814558,0.00002305253,0.8793576,0.002342549,0.1155384,0.002453423,0.00001844913],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007350672,0.0006752861,0.9882068,0.0001273385,0.0000540203,0.00006645504,0.00009166249,0.0007718469,0.002655966],"genre_scores_gemma":[0.1953504,0.0006317977,0.7969186,0.0002577658,0.0001374894,0.0002277165,0.0005573033,0.0005192879,0.00539971],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006318629,"threshold_uncertainty_score":0.02113789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240035297799508,"score_gpt":0.2426685078798835,"score_spread":0.2302681549018884,"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."}}