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Record W2118312746 · doi:10.1109/mcom.2007.382658

A business model for dynamic composition of telecommunication web services

2007· article· en· W2118312746 on OpenAlexafffund
Rajesh Karunamurthy, Ferhat Khendek, Roch Glitho

Bibliographic record

VenueIEEE Communications Magazine · 2007
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsConcordia University
FundersConcordia University
KeywordsComputer scienceWeb serviceBusiness Process Execution LanguageWS-PolicyWorld Wide WebProvisioningWeb modelingService (business)Service-oriented architectureWS-I Basic ProfileServices computingDatabaseWeb developmentWeb application securityComputer networkBusiness

Abstract

fetched live from OpenAlex

Web service composition is a mechanism for creating new web services from existing Web services. Web service composition enables rapid service creation by reusing existing services. Dynamic composition is composition at runtime. A business model defines the different parties involved in service provisioning and their relationships. However, the existing business models are not suitable for Web service composition. This article proposes a novel business model for dynamic web service composition that is an extension of the standard web service business model. The proposed model is demand-driven, where services can be dynamically composed based on the demand for them. We introduce new business roles and new interactions. We have provided a UDDI-based implementation of our new model by proposing extensions to the subscription API of UDDI. We have developed a proof-of-concept prototype, and have made some preliminary performance measurements. The performance analysis shows that the UDDI extensions incur acceptable performance penalization.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0040.001
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 head0.018
GPT teacher head0.285
Teacher spread0.267 · 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
GenreMethods

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

Citations18
Published2007
Admission routes2
Has abstractyes

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