{"id":"W1999968379","doi":"10.1007/s00778-010-0180-z","title":"Streaming multiple aggregations using phantoms","year":2010,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Granularity; Data stream mining; Aggregate (composite); Petabyte; Computation; Volume (thermodynamics); Distributed computing; Set (abstract data type); Data mining; The Internet; Service (business); Computer network; Real-time computing; Big data; Algorithm","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":[0.004304619,0.001252595,0.002254617,0.002089137,0.001619651,0.003337681,0.002995435,0.001473573,0.01048976],"category_scores_gemma":[0.01893803,0.001539362,0.0009351251,0.003397919,0.001238235,0.006386018,0.004475502,0.002232794,0.002208334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001360616,"about_ca_system_score_gemma":0.00173306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002426693,"about_ca_topic_score_gemma":0.002368277,"domain_scores_codex":[0.9966425,0.0007818069,0.0003015049,0.0005887366,0.001338811,0.0003465448],"domain_scores_gemma":[0.9837992,0.00557074,0.000496161,0.006867094,0.00231849,0.0009482407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.008996285,0.001010461,0.005625395,0.00050151,0.0003904451,0.001451145,0.001286327,0.1032499,0.0805121,0.09944651,0.05910219,0.6384276],"study_design_scores_gemma":[0.0003454161,0.0002825224,0.0005153343,0.00004502426,0.00007915028,0.000390483,0.0001257551,0.9110613,0.02549686,0.04392191,0.01765995,0.00007632569],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05224688,0.0007239908,0.9024526,0.000766878,0.0006949221,0.0002043744,0.0006936922,0.03383715,0.008379376],"genre_scores_gemma":[0.4219685,0.0003451949,0.5657052,0.0003026213,0.0004156815,0.0002221389,0.001879816,0.00186809,0.00729268],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01048976,"threshold_uncertainty_score":0.03509176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017665443236114,"score_gpt":0.2689327796126706,"score_spread":0.2487561251803095,"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."}}