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Record W2151867707 · doi:10.5555/2433508.2433598

Managing simulation workflow patterns using dynamic service-oriented compositions

2010· article· en· W2151867707 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWinter Simulation Conference · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsWorkflowWorkflow technologyComputer scienceWorkflow engineWorkflow management systemInteroperabilityMiddleware (distributed applications)Windows Workflow FoundationSoftware engineeringWeb serviceComponent (thermodynamics)Workflow Management CoalitionBusiness processBusiness process managementServerXPDLDatabaseWorld Wide WebEngineeringWork in process

Abstract

fetched live from OpenAlex

Distributed simulation usage in industry has been limited due to its high cost in comparison to its returned benefits. A number of surveys of experts from different background suggested the need of distributed simulation features to overcome its challenges and cost. The RESTful Interoperability Simulation Environment (RISE) middleware, based on RESTful Web-services, deals with these issues. However, simulation assets also need to be part of a formal Business Process Management (BPM) to allow practical across-enterprise collaboration. The Workflow mechanism introduced here promises to help with this situation. Further, these workflows provide automation, repeatable and reusable simulation experiments. We present the design of a workflow component that is capable of managing and executing different workflow patterns across various simulation RISE servers. We further present in detail a number of simulation workflow patterns executed by the workflow component.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.542
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.285
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it