{"id":"W4406890523","doi":"10.1109/access.2025.3535693","title":"Supporting Efficient Family Joins for Big Data Tables via Multiple Freedom Family Index","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"IBM Canada","keywords":"Joins; Computer science; Index (typography); Big data; Theoretical computer science; Data mining; World Wide Web; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002041133,0.0005045549,0.0007602137,0.001477505,0.001731191,0.002640828,0.001495891,0.0005717754,0.002584319],"category_scores_gemma":[0.006145721,0.0004254009,0.0008605837,0.002521549,0.000667008,0.005814571,0.00269779,0.0009411824,0.0009352869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005994544,"about_ca_system_score_gemma":0.001614871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002025156,"about_ca_topic_score_gemma":0.002751721,"domain_scores_codex":[0.9979293,0.0002666642,0.0002074287,0.000349044,0.001065393,0.0001820199],"domain_scores_gemma":[0.996362,0.00113144,0.0003131977,0.001310659,0.0006036521,0.0002790515],"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":[0.001136716,0.0005483514,0.01653326,0.000515218,0.000204332,0.0006918194,0.002663756,0.05297403,0.06174052,0.1349147,0.02880304,0.6992742],"study_design_scores_gemma":[0.0001310226,0.000482234,0.003475696,0.00006687176,0.00008092958,0.001395201,0.001223008,0.7348267,0.07372512,0.1111512,0.07327605,0.0001660024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05451855,0.0004852852,0.9337524,0.0002007844,0.0001074207,0.0002362464,0.0008672221,0.004660493,0.005171626],"genre_scores_gemma":[0.3347804,0.0003604457,0.6580715,0.0001318765,0.0001088176,0.0002101429,0.00286589,0.0004706504,0.003000302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002640828,"threshold_uncertainty_score":0.01079464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08972171192065728,"score_gpt":0.3328539176895632,"score_spread":0.2431322057689059,"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."}}