{"id":"W2019985064","doi":"10.1109/bigdata.congress.2014.121","title":"Towards Efficient KNN Joins on Data Streams","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Joins; Computer science; Snapshot (computer storage); Data mining; Data stream mining; Cluster analysis; Big data; Database; Artificial intelligence","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":[0.006913296,0.001852136,0.003166957,0.003303251,0.002287406,0.004697222,0.003870821,0.001910573,0.001834337],"category_scores_gemma":[0.02457313,0.001426434,0.001379722,0.007255277,0.001572875,0.01070117,0.005794068,0.002513473,0.001772391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001202748,"about_ca_system_score_gemma":0.002660488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004653128,"about_ca_topic_score_gemma":0.005210673,"domain_scores_codex":[0.9902036,0.00180535,0.001049368,0.001547493,0.004703182,0.0006910062],"domain_scores_gemma":[0.9844787,0.006151982,0.001480538,0.003813932,0.003383987,0.0006909989],"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":[0.002668967,0.001071286,0.0101893,0.001068874,0.0004078122,0.00057434,0.00243452,0.3037094,0.02991959,0.05781459,0.03051783,0.5596235],"study_design_scores_gemma":[0.0001352595,0.000239161,0.0005804249,0.0000395324,0.0000408712,0.0002199543,0.0005208267,0.9118701,0.01141017,0.06707196,0.007830693,0.00004112684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04239599,0.00140862,0.9477068,0.0005627626,0.0002122156,0.0002482855,0.0008201839,0.004385978,0.00225906],"genre_scores_gemma":[0.1495922,0.0007176836,0.8431245,0.0002813265,0.0003413939,0.0002583533,0.002889966,0.0005336675,0.002260861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006913296,"threshold_uncertainty_score":0.03656143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03989704760031067,"score_gpt":0.2677724367088479,"score_spread":0.2278753891085372,"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."}}