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Record W1518603076 · doi:10.5277/e-informatica

A user-centered approach to modeling BPEL business processes using SUCD use cases

2007· article· en· W1518603076 on OpenAlexaff
Mohamed El‐Attar, James Miller

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBusiness Process Execution LanguageComputer scienceBusiness processBusiness Process Model and NotationOrchestrationUse Case DiagramActivity diagramBusiness process modelingWeb serviceUnified Modeling LanguageArtifact-centric business process modelSoftware engineeringProcess modelingSet (abstract data type)Business process managementProcess managementService-oriented architectureWorld Wide WebClass diagramProgramming languageWork in process

Abstract

fetched live from OpenAlex

BPEL is being widely used to specify business processes through the orchestration, composition and coordination of web services. It is now common practice to begin the process of modeling the work ows within a set of BPEL business processes using UML Activity Diagrams since they can be automatically mapped down onto BPEL code. However activity diagrams were not intended to explicitly model user goals and interactions with external systems o ering web services. However, since the chief purpose of BPEL business processes is to rst and foremost provide services to their users, using activity diagram modeling alone will not allow an E-commerce analyst to explicitly capture and model the users ' goals. In this paper we propose an approach to solve this issue; initially model BPEL business processes using Use Cases to capture users ' perspective, and to systematically develop activity diagrams from Use Case models. A Travel Agency system case study is presented illustrates the feasibility of the proposed approach. 1

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.

How this classification was reachedexpand

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.330
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.064
GPT teacher head0.275
Teacher spread0.211 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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