{"id":"W1657379609","doi":"10.1109/ipdpsw.2015.21","title":"Towards a Combined Grouping and Aggregation Algorithm for Fast Query Processing in Columnar Databases with GPUs","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Computer science; Hash function; Parallel computing; Aggregate (composite); Algorithm; sort; Database; Data structure; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003734274,0.0001061269,0.0001293091,0.0001209278,0.00006888468,0.000365706,0.0002791157,0.00001611465,8.848821e-7],"category_scores_gemma":[0.00002257303,0.00008794605,0.000009237471,0.0003446449,0.00003462422,0.00222944,0.0002456814,0.00004371436,0.000001461989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003054304,"about_ca_system_score_gemma":0.00007360632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002741393,"about_ca_topic_score_gemma":0.0002078668,"domain_scores_codex":[0.9991004,0.00001856791,0.0001455172,0.0003344189,0.0001972979,0.0002037622],"domain_scores_gemma":[0.9995508,0.0000184889,0.00006402328,0.0002154887,0.00007754435,0.00007364012],"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":[0.00001061206,0.00005444794,0.0005155812,0.00003727618,0.000004650347,0.00001170431,0.0002902299,0.00001027397,0.000005793496,0.005112904,0.0005860164,0.9933605],"study_design_scores_gemma":[0.001884936,0.0002293792,0.001170214,0.0001171944,0.000007437644,0.000009084024,0.0004460335,0.992229,0.000184339,0.0009880284,0.002519619,0.0002147059],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003558475,0.00009714962,0.9947172,0.0003553193,0.00007905936,0.0003739653,0.000009843716,0.000111808,0.0006972075],"genre_scores_gemma":[0.06353478,0.0000113483,0.9355255,0.0002669464,0.00005418332,0.00007493544,0.00009282136,0.00001089349,0.0004286235],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9931458,"threshold_uncertainty_score":0.3586336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03857750052534887,"score_gpt":0.2682024168819815,"score_spread":0.2296249163566327,"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."}}