{"id":"W4402564288","doi":"10.48550/arxiv.2408.07650","title":"Exact Trajectory Similarity Search With N-tree: An Efficient Metric Index for kNN and Range Queries","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Nearest neighbor search; Similarity (geometry); Index (typography); Metric (unit); Range (aeronautics); Trajectory; Tree (set theory); Mathematics; Computer science; Range query (database); Data mining; Algorithm; Artificial intelligence; Combinatorics; Information retrieval; Search engine; Web search query; Engineering; Physics; World Wide Web; Sargable","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":[0.001551088,0.0009127859,0.002733211,0.004828363,0.001370819,0.002425677,0.002585212,0.001431376,0.003606163],"category_scores_gemma":[0.01310895,0.0003839526,0.0006438571,0.01044489,0.0008954437,0.008329892,0.00379628,0.001024119,0.002838366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001660027,"about_ca_system_score_gemma":0.002792332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004433626,"about_ca_topic_score_gemma":0.005515985,"domain_scores_codex":[0.9967483,0.0005145478,0.0004170581,0.0004424842,0.001633775,0.0002437369],"domain_scores_gemma":[0.9963834,0.0009050033,0.0003789641,0.001344243,0.0007560627,0.00023227],"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":[0.0008484546,0.0004679712,0.004008926,0.0007428513,0.0001275475,0.0003149292,0.0005814492,0.07383368,0.01177023,0.06641325,0.04175297,0.7991377],"study_design_scores_gemma":[0.000165487,0.0005897883,0.00153915,0.0001247954,0.00007940394,0.001510579,0.0004708557,0.804354,0.009501958,0.125955,0.05558993,0.0001191056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03690306,0.005199526,0.9393893,0.0006794414,0.0003033919,0.000448464,0.003211051,0.005858785,0.008006967],"genre_scores_gemma":[0.254917,0.001640313,0.729701,0.000293611,0.0002437836,0.0005238233,0.00822538,0.0005062506,0.00394874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004828363,"threshold_uncertainty_score":0.01206386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06774126754605381,"score_gpt":0.2100978507326794,"score_spread":0.1423565831866256,"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."}}