{"id":"W2115072326","doi":"10.1109/cse.2009.144","title":"Optimization of Distributed SPARQL Queries Using Edmonds' Algorithm and Prim's Algorithm","year":2009,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"SPARQL; Computer science; Query plan; Named graph; Algorithm; Query optimization; Graph; RDF; Query language; Information retrieval; Sargable; Theoretical computer science; Web search query; Semantic Web; 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":[0.002133971,0.0009312273,0.001111256,0.0009382023,0.0007932425,0.001761119,0.001684088,0.0008373194,0.004130458],"category_scores_gemma":[0.0042052,0.0005496177,0.0007980243,0.001540801,0.0007889512,0.002363712,0.001874233,0.0009829248,0.0007022645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00132724,"about_ca_system_score_gemma":0.001617829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005071065,"about_ca_topic_score_gemma":0.00637078,"domain_scores_codex":[0.9986474,0.0003847373,0.0001041917,0.0002585698,0.0004742773,0.0001308223],"domain_scores_gemma":[0.9987096,0.0006831595,0.00008225985,0.0002709399,0.0002002718,0.00005381316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003485572,0.000217418,0.00154025,0.0001630778,0.00008534954,0.0001870506,0.0001796115,0.6252852,0.003967044,0.1117364,0.009539312,0.2467507],"study_design_scores_gemma":[0.00004792321,0.00002708276,0.00007899514,0.000007402871,0.00000838461,0.0000310113,0.00003526885,0.9655595,0.00210141,0.02917618,0.002919668,0.000007199243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008941815,0.0001441605,0.9861777,0.0001790925,0.00002547398,0.00008661271,0.00009864314,0.001143029,0.003203494],"genre_scores_gemma":[0.1147818,0.0001578046,0.8817485,0.00009761088,0.00001919634,0.0001776022,0.0003437819,0.0003431387,0.002330501],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005071065,"threshold_uncertainty_score":0.01381773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01575432963371913,"score_gpt":0.2482508130551846,"score_spread":0.2324964834214655,"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."}}