{"id":"W1490413238","doi":"10.1007/978-3-642-12098-5_5","title":"Dynamic Skyline Queries in Large Graphs","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Skyline; Computer science; Pruning; Euclidean space; Graph; Set (abstract data type); Query optimization; Data mining; Spatial query; Metric space; Theoretical computer science; Algorithm; Web search query; Sargable; Information retrieval; Search engine; Mathematics; Discrete mathematics; Combinatorics","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":[0.001158737,0.0007671582,0.00132018,0.001232543,0.001119833,0.002445367,0.001947751,0.001255789,0.008648911],"category_scores_gemma":[0.005338321,0.0007404389,0.0006011465,0.004350399,0.0008023515,0.00865107,0.002125021,0.001830586,0.001962801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001222783,"about_ca_system_score_gemma":0.0006753512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001563594,"about_ca_topic_score_gemma":0.002344465,"domain_scores_codex":[0.998857,0.0002416301,0.00006723361,0.0002617103,0.0004219928,0.000150334],"domain_scores_gemma":[0.9960954,0.002298526,0.0001688807,0.0009214127,0.0003036116,0.000212202],"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":[0.001020203,0.0003522816,0.00318357,0.001488321,0.0001470873,0.0005606394,0.0009033557,0.1218092,0.02061603,0.264999,0.1940472,0.390873],"study_design_scores_gemma":[0.0001965166,0.0001324287,0.001202811,0.00008665269,0.00008379952,0.001108066,0.0004601934,0.4920593,0.01181768,0.4155871,0.0772175,0.00004792829],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.133541,0.007364078,0.7960331,0.004520817,0.0004499034,0.0002892766,0.005721937,0.01036756,0.04171227],"genre_scores_gemma":[0.5841846,0.005251787,0.3626633,0.0006526487,0.000722696,0.0002858971,0.01184726,0.002661797,0.03173001],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008648911,"threshold_uncertainty_score":0.02893347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008112831594243572,"score_gpt":0.2351527862407053,"score_spread":0.2270399546464618,"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."}}