{"id":"W3174680429","doi":"10.1145/3448016.3452794","title":"Klink: Progress-Aware Scheduling for Streaming Data Systems","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Sapienza Università di Roma","keywords":"Computer science; Timestamp; Scheduling (production processes); Leverage (statistics); Real-time computing; Stream processing; Latency (audio); Distributed computing; Query plan; Sliding window protocol; Data stream; Window (computing); Operating system; Artificial intelligence; Sargable; Telecommunications; Search engine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002507221,0.0001198262,0.0001862876,0.00003059558,0.0001764884,0.0002039826,0.0006779647,0.00004110372,0.000004752957],"category_scores_gemma":[0.00009208156,0.0001011014,0.00003099562,0.0002080091,0.0000251863,0.001334195,0.0009464023,0.00006208283,0.00001283816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002024837,"about_ca_system_score_gemma":0.0001542352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002796754,"about_ca_topic_score_gemma":0.00002922195,"domain_scores_codex":[0.9986768,0.00003162805,0.0002241179,0.0006089159,0.0001838421,0.000274637],"domain_scores_gemma":[0.9978275,0.0001062335,0.00008511521,0.001706049,0.0001988286,0.00007623842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003858142,0.00007946565,0.0006355989,0.0004656138,0.00005375691,0.00008244195,0.0001562921,0.0006450731,0.0003340052,0.9270223,0.001801659,0.06871989],"study_design_scores_gemma":[0.0003930932,0.00003077674,0.00005619703,0.0002677336,0.000009389171,0.00009161019,0.001070031,0.7317172,0.002161281,0.0001804816,0.2636892,0.0003329903],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005394993,0.001676536,0.995669,0.0003768765,0.0007476773,0.0002570047,0.0001874627,0.0002341216,0.0003117991],"genre_scores_gemma":[0.07525424,0.00002098987,0.9225237,0.00006615005,0.0003122582,0.00006931806,0.0004605323,0.00001590603,0.001276911],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9268419,"threshold_uncertainty_score":0.4122795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06721397898214153,"score_gpt":0.324778364178664,"score_spread":0.2575643851965224,"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."}}