{"id":"W2084007999","doi":"10.1109/icde.2008.4497496","title":"Multiple Materialized View Selection for XPath Query Rewriting","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Materialized view; Computer science; Rewriting; XPath; Selection (genetic algorithm); Query optimization; Heuristic; Query language; Information retrieval; Set (abstract data type); Theoretical computer science; Scheme (mathematics); View; XML; Programming language; Artificial intelligence; Mathematics; World Wide Web","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":[0.003802313,0.0008067318,0.001092325,0.001155012,0.0007387552,0.002008342,0.001675163,0.001227328,0.001579982],"category_scores_gemma":[0.009595811,0.0004982929,0.001771151,0.00124445,0.001532451,0.003573337,0.001705977,0.001626745,0.0003837792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022903,"about_ca_system_score_gemma":0.0007757047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001876186,"about_ca_topic_score_gemma":0.001491509,"domain_scores_codex":[0.9941723,0.002049726,0.0004526848,0.001000809,0.00200829,0.0003161395],"domain_scores_gemma":[0.9923444,0.004837255,0.0004501588,0.001572804,0.000623412,0.0001719815],"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":[0.0005707044,0.0004173297,0.003950038,0.0007971062,0.0002951121,0.001575107,0.00252982,0.1496644,0.09541833,0.236777,0.004989581,0.5030155],"study_design_scores_gemma":[0.00006526848,0.0003350836,0.0006253414,0.00007172785,0.0001468117,0.0009660966,0.0003217576,0.8047341,0.05969277,0.121023,0.01193479,0.00008317387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01603896,0.0002591051,0.9821222,0.0001208049,0.00001790522,0.00007105291,0.00003832099,0.000529116,0.0008026222],"genre_scores_gemma":[0.242143,0.0003121863,0.7551575,0.000127571,0.0000906073,0.0001359493,0.0003294375,0.0002918306,0.001411873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003802313,"threshold_uncertainty_score":0.02010882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03289630735022392,"score_gpt":0.2594823799693533,"score_spread":0.2265860726191294,"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."}}