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Record W2774636489 · doi:10.1109/iemcon.2017.8117134

Comparing autonomy and collaboration between agent-oriented architecture and service-oriented architecture

2017· article· en· W2774636489 on OpenAlexaff
Parisa Lotfallahtabrizi, Yasser Morgan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsApplications architectureEnterprise architecture frameworkComputer scienceSolution architectureReference architectureArchitectureService-oriented architectureSpace-based architectureEnterprise architecture managementView modelEnterprise architectureData architectureService (business)Distributed computingDatabase-centric architectureSoftware engineeringSoftware architectureWorld Wide WebWeb serviceOperating systemBusiness

Abstract

fetched live from OpenAlex

Over the last decades, information technology in the field of computer networks and Internet is improved rapidly. Hence, in order to achieve high efficiency of information systems in the dispersed environments, it is needed to provide dynamic and distributed applications. There are various kinds of information system architectures to deploy information systems such as Agent-Base Architecture, Enterprise Resource Planning and Service-Oriented Architecture. Agent-oriented architecture and service-oriented architecture possess essential characteristics to develop distributed systems. There are several similarities and differences between agent-oriented architecture and service-oriented architecture. Both architectures have autonomy and collaboration properties. Comparing service-oriented architecture and agent-oriented architecture and representing their differences in autonomy and collaboration are the main purpose of this paper.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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.

Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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