{"id":"W2732621197","doi":"10.1109/tpds.2017.2720174","title":"Combining Vertex-Centric Graph Processing with SPARQL for Large-Scale RDF Data Analytics","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"SPARQL; Named graph; Computer science; RDF; Analytics; RDF Schema; Graph; Graph database; Linked data; Theoretical computer science; PageRank; Information retrieval; Database; Semantic 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004217848,0.0002267755,0.0003208949,0.0001139703,0.001753194,0.0008751891,0.001175121,0.00009281411,0.000001527814],"category_scores_gemma":[0.000007651618,0.0001827983,0.00006440844,0.0002543957,0.00009599673,0.0009120394,0.00001416639,0.0001761352,0.000004595771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001826607,"about_ca_system_score_gemma":0.00006302304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003144237,"about_ca_topic_score_gemma":0.00004586472,"domain_scores_codex":[0.9983976,0.00005551005,0.0002822457,0.0006025385,0.0002418598,0.0004202048],"domain_scores_gemma":[0.9980755,0.00009337427,0.0002230695,0.001306751,0.0001180316,0.0001833139],"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":[0.001942624,0.006657136,0.004545533,0.002720733,0.002056369,0.0003057203,0.004222367,0.816183,0.0004163875,0.07407738,0.005466666,0.08140609],"study_design_scores_gemma":[0.002384642,0.0001827228,0.0004008893,0.0001901246,0.0001007224,0.00006004693,0.0003290993,0.9935406,0.00005096206,0.0009557193,0.001455672,0.0003488371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002454575,0.0001847022,0.9949893,0.0002230155,0.0005532744,0.0004021085,0.0009867029,0.0001257884,0.0000805136],"genre_scores_gemma":[0.9955786,0.00003748251,0.004011918,0.0000280844,0.00002727717,0.00005856665,0.00009277018,0.00001342521,0.0001518258],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9931241,"threshold_uncertainty_score":0.9995464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04261578362629414,"score_gpt":0.2699335214137163,"score_spread":0.2273177377874221,"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."}}