MétaCan
Menu
Back to cohort
Record W2073020719 · doi:10.1109/wmnc.2013.6548964

A comparison of software architectures for data-oriented mobile applications

2013· article· en· W2073020719 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceMiddleware (distributed applications)Mobile computingUsabilityMobile deviceVariety (cybernetics)Mobile WebSoftwareMobile technologyWorld Wide WebDatabaseComputer networkHuman–computer interactionOperating system

Abstract

fetched live from OpenAlex

Mobile applications have empowered and extended the usability of mobile devices far beyond merely supporting voice communication. Mobile applications which rely on remote data sources and databases are particularly challenging given the need to engage in complex business logic and transmit data through wireless media. Previous research has looked at a variety of approaches to address these problems. Two software architectural approaches have emerged as the primary ways to develop mobile applications accessing remote data sources: client-agent-server and client-intercept-server. Both make use of middleware with the main difference being in the way agent components are used. In this work, we compare the performance of the two approaches under different scenarios. Statistical analysis shows that the client-agent-approach performs better. The results of this research provide useful guidelines for the development of mobile applications needing to connect to remote databases or data sources.

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.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.806
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.324
Teacher spread0.289 · 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

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicMobile Agent-Based Network ManagementFrench-language works237,207