{"id":"W4410035951","doi":"10.1007/978-3-031-88304-0_69","title":"Enhancing Scalability and Performance in Big Data Query Processing: A Multi-faceted Approach","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hog Administrative Marketing Services (Canada)","funders":"","keywords":"Scalability; Computer science; Big data; Query optimization; Database; Distributed computing; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001249679,0.0004000299,0.0006550306,0.0002594574,0.0001482994,0.0003507029,0.000853719,0.000439108,2.242508e-7],"category_scores_gemma":[0.00005321398,0.0003296595,0.00003235942,0.0002416893,0.00009107508,0.00003014985,0.001471434,0.0008146124,2.889945e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008112541,"about_ca_system_score_gemma":0.00007106902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001399085,"about_ca_topic_score_gemma":0.0002493311,"domain_scores_codex":[0.997461,0.0001005823,0.0006015751,0.001215917,0.0002324414,0.0003884542],"domain_scores_gemma":[0.9983508,0.0002495559,0.0002192563,0.001070338,0.00004227825,0.00006777754],"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":[0.00001802956,0.00005326441,0.00486658,0.002823388,0.00004100065,0.00002056896,0.000857955,0.3178171,0.000001333601,0.0006361526,0.00003209868,0.6728325],"study_design_scores_gemma":[0.0003283118,0.00002946958,0.0009871487,0.002666287,0.00001305056,0.00001569634,0.000009052744,0.9935618,5.377831e-7,0.0001003686,0.001949562,0.0003387124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004763033,0.02896621,0.9586757,0.0001134236,0.0008106299,0.0009151648,0.000003883949,0.0001238325,0.005628161],"genre_scores_gemma":[0.9930347,0.0002095889,0.003436397,0.00009689769,0.000396445,0.00001960966,0.00001915162,0.00002276024,0.002764506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9882716,"threshold_uncertainty_score":0.9999155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03537951724961013,"score_gpt":0.2363369880403675,"score_spread":0.2009574707907574,"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."}}