{"id":"W2998594312","doi":"10.1109/edoc.2019.00029","title":"Predictive Analytics for Event Stream Processing","year":2019,"lang":"en","type":"article","venue":"","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Complex event processing; Computer science; Stream processing; Event (particle physics); Workflow; Process mining; Process (computing); Business process; Analytics; Business process discovery; Business process management; Data mining; Computation; Business process modeling; Work in process; Distributed computing; Database; Programming language; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000189611,0.0001556986,0.0002196978,0.0002119093,0.0001278621,0.0002661387,0.000177365,0.00005864486,0.0001591325],"category_scores_gemma":[0.00004673602,0.0001257928,0.0001229407,0.0005147746,0.00001740684,0.0009165962,0.00006346877,0.0000637863,0.0001726034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001957359,"about_ca_system_score_gemma":0.00003307559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001017288,"about_ca_topic_score_gemma":0.0000190337,"domain_scores_codex":[0.9989846,0.000001456118,0.0002358641,0.0003177632,0.0002083134,0.0002519476],"domain_scores_gemma":[0.999155,0.00001803431,0.0001656738,0.0001715909,0.0004803544,0.000009368477],"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":[0.0005751004,0.001242421,0.179281,0.007185807,0.0007243223,0.000005563664,0.000229224,0.3272246,0.001185493,0.08073129,0.01240854,0.3892066],"study_design_scores_gemma":[0.0004441272,0.000009277318,0.0006980918,0.0000690075,0.0001816935,2.447894e-7,0.0003045771,0.9809641,0.00004709115,0.006163527,0.01090941,0.0002088946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2493745,0.0002219275,0.709263,0.001874906,0.0003658564,0.000699712,0.000006918976,0.000551833,0.03764141],"genre_scores_gemma":[0.9945945,0.0000035872,0.0007191119,0.000874873,0.0006371534,0.00002962331,0.0000469114,0.00002755641,0.003066754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7452199,"threshold_uncertainty_score":0.5129681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657984723476217,"score_gpt":0.2417042614681306,"score_spread":0.2251244142333685,"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."}}