{"id":"W1841391047","doi":"10.1007/978-3-540-74553-2_20","title":"Selection and Pruning Algorithms for Bitmap Index Selection Problem Using Data Mining","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Bitmap; Computer science; Data mining; Joins; Pruning; Join (topology); Data warehouse; Key (lock); Tuple; Dimension (graph theory); Search engine indexing; Selection (genetic algorithm); Information retrieval; Mathematics; 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":[0.002080048,0.000931584,0.001991244,0.003339933,0.001039958,0.00161468,0.002520425,0.001380392,0.002547352],"category_scores_gemma":[0.006322154,0.0005212404,0.0008976399,0.005196941,0.0005037491,0.002424624,0.001189328,0.001670119,0.0007523241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006458844,"about_ca_system_score_gemma":0.001287975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002283494,"about_ca_topic_score_gemma":0.00347298,"domain_scores_codex":[0.9986048,0.0003191119,0.0001315561,0.0001767373,0.0006373142,0.0001303957],"domain_scores_gemma":[0.996381,0.001826005,0.0002232637,0.0005532591,0.0008655901,0.0001509424],"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":[0.0003950222,0.0003798827,0.002558311,0.000300751,0.00009680612,0.0001749656,0.0001557903,0.05697712,0.008185099,0.01216069,0.01494827,0.9036673],"study_design_scores_gemma":[0.00006755038,0.0001155089,0.0008594262,0.00004351156,0.00007303367,0.0002894508,0.00006310401,0.9674478,0.006348063,0.02093875,0.003730019,0.00002385383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0311677,0.001791529,0.9624801,0.0004508691,0.0001583091,0.0002013763,0.0003117944,0.001425738,0.002012701],"genre_scores_gemma":[0.1090566,0.0008897802,0.8853201,0.0001543064,0.0001548973,0.0002180789,0.0008848106,0.0001573141,0.003164111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003339933,"threshold_uncertainty_score":0.01100051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07942153423394675,"score_gpt":0.3231942760796316,"score_spread":0.2437727418456848,"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."}}