{"id":"W2529060894","doi":"10.1109/bigdatacongress.2016.19","title":"Enhanced State History Tree (eSHT): A Stateful Data Structure for Analysis of Highly Parallel System Traces","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Stateful firewall; TRACE (psycholinguistics); Scalability; Data structure; Thread (computing); Heap (data structure); Debugging; Tree (set theory); Theoretical computer science; Distributed computing; Parallel computing; Programming language; Database","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":[0.001402502,0.0009005612,0.000705416,0.002649133,0.0007522596,0.001836184,0.001686666,0.0007805471,0.004028251],"category_scores_gemma":[0.007936496,0.0005885639,0.000897858,0.002995407,0.0008475677,0.004154044,0.001550745,0.001096348,0.001100136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008654122,"about_ca_system_score_gemma":0.001950286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00533234,"about_ca_topic_score_gemma":0.006386444,"domain_scores_codex":[0.9987149,0.0002024952,0.0002164141,0.0002347364,0.0005389695,0.00009245228],"domain_scores_gemma":[0.9941706,0.002231886,0.0006338979,0.001966368,0.000773342,0.0002239095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001633249,0.0004167405,0.03023554,0.001348528,0.000304104,0.0006302028,0.002167705,0.1186449,0.03519946,0.1010479,0.05189239,0.6564792],"study_design_scores_gemma":[0.0001286344,0.0004212107,0.005155914,0.0001336969,0.00008220518,0.0004040138,0.0003233207,0.8210695,0.04454057,0.07332832,0.0542518,0.0001609011],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0284519,0.0003623471,0.9137442,0.0003618688,0.00008879482,0.0002344077,0.009820156,0.04487438,0.002061993],"genre_scores_gemma":[0.3877899,0.0005022672,0.5822259,0.0002337757,0.00008044022,0.0006570993,0.02275856,0.002812889,0.002939284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00533234,"threshold_uncertainty_score":0.01347589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03647089403056068,"score_gpt":0.2544424261053332,"score_spread":0.2179715320747725,"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."}}