{"id":"W2041912543","doi":"10.1109/edocw.2013.37","title":"The Relational Database Engine: An Efficient Validator of Temporal Properties on Event Traces","year":2013,"lang":"en","type":"article","venue":"","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Validator; TRACE (psycholinguistics); Computer science; SQL; Relational database; Temporal logic; Event (particle physics); Database; Process (computing); Data mining; Programming language; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004881242,0.001434857,0.001176074,0.002598837,0.000472432,0.002746474,0.003378355,0.0009816576,0.004516367],"category_scores_gemma":[0.01544908,0.0009247508,0.001066697,0.001654996,0.00073192,0.004606427,0.002053339,0.001274697,0.002991102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007363496,"about_ca_system_score_gemma":0.002320159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0067402,"about_ca_topic_score_gemma":0.005826477,"domain_scores_codex":[0.9952016,0.0009001744,0.0006210726,0.0009649213,0.002105515,0.0002068278],"domain_scores_gemma":[0.9917923,0.003435134,0.0006768332,0.002806823,0.001155586,0.0001332294],"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":[0.00169267,0.0006200828,0.0118074,0.001931328,0.0007901381,0.001200995,0.001460518,0.0284264,0.09265339,0.03510117,0.03039639,0.7939196],"study_design_scores_gemma":[0.0002769048,0.0004647802,0.004457809,0.0002528005,0.0003080659,0.001832465,0.0003437847,0.6775984,0.2359264,0.01872415,0.05954713,0.0002673566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01321977,0.0004740566,0.8806025,0.0001306862,0.00004682967,0.0002643923,0.001857799,0.1019043,0.001499763],"genre_scores_gemma":[0.2508556,0.0007651789,0.727632,0.0002812777,0.00004372714,0.0002861353,0.007113971,0.007741443,0.005280747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0067402,"threshold_uncertainty_score":0.02581483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660247511046356,"score_gpt":0.2245168058495719,"score_spread":0.2079143307391084,"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."}}