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Record W2137309678 · doi:10.1109/mcetech.2008.18

Assessing the Applicability of Use Case Maps for Business Process and Workflow Description

2008· article· en· W2137309678 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

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWorkflowComputer scienceSemantics (computer science)Business processAbstractionSoftware engineeringNotationBusiness Process Model and NotationSet (abstract data type)Programming languageProcess (computing)Business process modelingDatabaseWork in processEngineeringLinguistics

Abstract

fetched live from OpenAlex

Use Case Maps (UCMs) have already been used to describe business processes and workflows at a high level of abstraction. The semantics of UCMs, however, require further clarification and enhancement. An initial assessment based on 27 workflow and communication patterns (a) highlighted some of the semantic variation points of UCMs, (b) introduced small extensions to the UCM language in order to more precisely define scenarios, high-level business processes, and workflows, and (c) compared UCMs with other business process and workflow languages. This short paper summarizes the continuation of the assessment with a larger set of workflow patterns recently made available. The assessment concludes that the UCM notation including the proposed extensions is a competitive language to describe high-level business processes and workflows, while providing additional benefits over the other languages.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.437

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 studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.282
Teacher spread0.206 · 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

Quick stats

Citations14
Published2008
Admission routes1
Has abstractyes

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