{"id":"W2964255032","doi":"10.1109/access.2019.2931005","title":"Scalable Distributed kNN Processing on Clustered Data Streams","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Science Foundation of Shandong Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Shandong University","keywords":"Computer science; Scalability; Partition (number theory); Data stream mining; Bounded function; Data mining; Sliding window protocol; Set (abstract data type); Process (computing); Index (typography); Distributed computing; Theoretical computer science; Window (computing); Database; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0002377882,0.0001448839,0.0001502112,0.00007943622,0.0001025059,0.001462077,0.005895524,0.0000378655,0.00004151649],"category_scores_gemma":[0.00001550393,0.0001235722,0.00002015135,0.0005377452,0.00002005563,0.005293335,0.001870492,0.0001110269,0.0004631681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002502127,"about_ca_system_score_gemma":0.00003784297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006501753,"about_ca_topic_score_gemma":0.000009994711,"domain_scores_codex":[0.9984545,0.00002727535,0.0001793116,0.0006777524,0.000340987,0.0003201933],"domain_scores_gemma":[0.9977573,0.00003487662,0.0001041616,0.001991825,0.00004294822,0.00006893117],"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":[0.00002387041,0.0003255928,0.005629447,0.0001785287,0.00004810365,0.00004431753,0.00008926008,0.0006776392,0.0001692311,0.002411934,0.08087345,0.9095286],"study_design_scores_gemma":[0.00102847,0.0000857807,0.005449074,0.0001586164,0.00001665603,0.000003215896,0.00002937187,0.9515796,0.002220953,0.0009958916,0.03794339,0.0004890018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1180793,0.00006661239,0.8660907,0.001633889,0.002442468,0.000714152,0.0003772597,0.0006159151,0.009979719],"genre_scores_gemma":[0.9944262,0.000009640382,0.003240598,0.0005665019,0.0001694998,0.000008654565,0.0004807548,0.00001419105,0.001083948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9509019,"threshold_uncertainty_score":0.9995745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0616474869040738,"score_gpt":0.3196342269281706,"score_spread":0.2579867400240968,"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."}}