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Record W2162694538 · doi:10.1109/mcom.2010.5673078

RESTful web services for bridging presence service across technologies and domains: an early feasibility prototype

2010· article· en· W2162694538 on OpenAlexaff
Chunyan Fu, Fatna Belqasmi, Roch Glitho

Bibliographic record

VenueIEEE Communications Magazine · 2010
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsConcordia UniversityEricsson (Canada)
Fundersnot available
KeywordsBridging (networking)Computer scienceWeb serviceWorld Wide WebService (business)TelecommunicationsComputer network

Abstract

fetched live from OpenAlex

A presence service enables the discovery and retrieval of, and subscription to changes in, an end user's context information. RESTful web services are now emerging as a lighter alternative to the so-called Big Web services. This article presents an early feasibility prototype of a RESTful web-services-based architecture. The architecture enables the discovery and retrieval of and subscription to changes in context information, independent of the technologies used in the end users' domains. Concretely, it enables end users with multiple presence accounts (e.g., MSN, Yahoo, Gmail) to publish context information related to the account(s) they are using at any given time. It also enables other end users or applications to retrieve this information by subscribing to any of the multiple accounts of the publisher. The project has demonstrated that RESTful web services are quite suitable for bridging services across technologies and domains. It has also demonstrated that a RESTful web services approach has several advantages over a traditional web services (also known as Big Web services) approach. However, more functionality needs to be added to the prototype before market introduction is contemplated. The lessons learned are discussed and the missing functionalities are identified.

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 categoriesMeta-epidemiology (narrow), Open science
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 score0.566
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0060.002
Research integrity0.0000.001
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 head0.028
GPT teacher head0.316
Teacher spread0.288 · 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

Citations25
Published2010
Admission routes1
Has abstractyes

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