{"id":"W2136877435","doi":"10.1109/icws.2010.35","title":"An Approach for Mining Web Service Composition Patterns from Execution Logs","year":2010,"lang":"en","type":"article","venue":"","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Web service; Service (business); Documentation; Differentiated service; Composition (language); Set (abstract data type); Database; Reuse; World Wide Web; Service design; Service composition; Quality of service; Service delivery framework; Computer network; Business; Engineering; Operating system","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":[0.002366537,0.002050504,0.001840327,0.01129344,0.001365549,0.002336256,0.002303127,0.001931003,0.001225414],"category_scores_gemma":[0.0102936,0.0009543184,0.002151025,0.007201348,0.0007697806,0.002859159,0.001115668,0.001527203,0.001484939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009352307,"about_ca_system_score_gemma":0.002949367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008448051,"about_ca_topic_score_gemma":0.009856127,"domain_scores_codex":[0.9965069,0.0005439064,0.0005417244,0.0009250376,0.001319936,0.0001625792],"domain_scores_gemma":[0.9940684,0.00268893,0.0008584294,0.0008715761,0.001324425,0.0001883063],"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":[0.0005013147,0.001641071,0.03918697,0.001379519,0.0007285037,0.001445132,0.001040975,0.04135878,0.03118572,0.007469531,0.0132371,0.8608254],"study_design_scores_gemma":[0.0001399302,0.0002647304,0.01067742,0.0001366509,0.0002453915,0.001802664,0.0005751224,0.9359397,0.01857415,0.01681951,0.01468248,0.0001421609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03805544,0.0005589091,0.939229,0.0005772561,0.00008420693,0.001228422,0.004849938,0.01431295,0.001103879],"genre_scores_gemma":[0.1056531,0.0003232329,0.8837419,0.0001434144,0.00005135879,0.001156267,0.007412196,0.0002086287,0.001309948],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01129344,"threshold_uncertainty_score":0.01679778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01109392833224143,"score_gpt":0.2388559945579404,"score_spread":0.227762066225699,"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."}}