{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001549125,0.00008727307,0.0001440909,0.00003501817,0.0002580117,0.00002689661,0.0001240918,0.00002821634,0.00001465546],"category_scores_gemma":[0.00007229019,0.00007321943,0.00004203472,0.0001307333,0.0000173208,0.0006496137,0.00006802977,0.00002750064,0.00001852758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002127445,"about_ca_system_score_gemma":0.00003423256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001692851,"about_ca_topic_score_gemma":0.00005763166,"domain_scores_codex":[0.9992363,0.00002995863,0.000203584,0.0002472326,0.00009102782,0.0001919057],"domain_scores_gemma":[0.9995407,0.0000637066,0.00006548988,0.0002117318,0.00007756118,0.00004083699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000915384,0.0001270073,0.004259611,0.0003759418,0.00003809525,0.00002157476,0.001175517,0.0001247436,0.1181006,0.820493,0.01282269,0.04236966],"study_design_scores_gemma":[0.001749181,0.0001730427,0.0009045851,0.0001189996,0.000004863078,0.0003701795,0.0001050645,0.07478224,0.1015894,0.0008420965,0.8188105,0.0005497874],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01781387,0.00006796869,0.9805661,0.00008623251,0.0003582863,0.0002525907,0.0000103973,0.0002991056,0.0005454569],"genre_scores_gemma":[0.1911722,0.00004480503,0.8070374,0.0002253117,0.0001978323,0.0001121758,0.00001694934,0.00001115576,0.0011821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8196509,"threshold_uncertainty_score":0.2985802,"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."}}