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Record W2924912183 · doi:10.5539/cis.v12n2p14

Service Packaging: A Pattern Based Approach Towards Service Delivery

2019· article· en· W2924912183 on OpenAlexvenueno aff
Muhammad Adeel Talib, Muhammad Nabeel Talib, Madiha Akhtar

Bibliographic record

VenueComputer and Information Science · 2019
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsnot available
FundersSwinburne University of Technology
KeywordsService delivery frameworkComputer scienceService (business)Service designMiddleware (distributed applications)Differentiated serviceService level objectiveDomain (mathematical analysis)Service providerSoftware engineeringDatabaseBusiness

Abstract

fetched live from OpenAlex

Authentication, authorization, billing and monitoring are all common service delivery functions that are generally required to be added on to core business services in order for them to be delivered online commercially. Extending core services with these service delivery functions requires considerable effort if implemented ground-up and can be subject to limitations if outsourced to a service broker or a conventional middleware platform. Because of the ubiquitous nature of these service delivery functions, we see them as reusable patterns for service delivery. In this paper we have introduce an approach to implementing and applying these patterns in business to consumer e-commerce. We name the approach Service Packaging. Through the approach, generic implementations (or service packages) of the various service delivery patterns can be incrementally applied to core services, thus enabling flexible and systematic service delivery. A core service, regardless of its business domain does not require any structural or behavioral modifications in order to conform to a specific service delivery requirement and hence can be used out of the box. We also present a prototype middleware platform for the design-time modeling and implementation of service packages as well as their runtime execution.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0010.011
Open science0.0020.001
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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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