{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000256583,0.0001158821,0.00009065537,0.00005957313,0.0001802331,0.0001036262,0.0007915164,0.00002656764,0.00004205516],"category_scores_gemma":[0.000008810102,0.00005914528,0.00004105556,0.000214546,0.00003797154,0.0003367032,0.000155587,0.00009731323,0.00006832903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009700097,"about_ca_system_score_gemma":0.00005116226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003698423,"about_ca_topic_score_gemma":0.00007943318,"domain_scores_codex":[0.9988289,0.00008088566,0.0002378138,0.0002553773,0.0004102487,0.0001867918],"domain_scores_gemma":[0.9989817,0.000119013,0.00008409532,0.000621865,0.0001122411,0.00008111578],"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":[0.0001467754,0.002122496,0.005267963,0.0002337109,0.0001803981,0.00000832277,0.01687617,0.1121781,0.02565358,0.7576447,0.002448921,0.07723884],"study_design_scores_gemma":[0.0005889524,0.0005583002,0.01564517,0.0001106574,0.00001221444,0.00001064139,0.001043575,0.8937416,0.0740024,0.001497244,0.01238373,0.0004054793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9569995,0.0001731538,0.03768729,0.003336179,0.0002860771,0.0003528557,0.000004977216,0.0001120629,0.001047891],"genre_scores_gemma":[0.9906363,0.00000334008,0.008660458,0.0004439842,0.00005430641,0.0000305808,0.000009940355,0.000005731178,0.0001553632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7815635,"threshold_uncertainty_score":0.2411875,"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."}}