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Record W2088920297 · doi:10.1109/ccece.2014.6901137

Hybrid threshold-based distributed discovery service for the EPCglobal network

2014· article· en· W2088920297 on OpenAlexaff
Mazen G. Khair, Burak Kantarcı, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBlocking (statistics)Computer scienceComputer networkTable (database)Default gatewayService (business)Distributed computingData mining

Abstract

fetched live from OpenAlex

In this paper, we present a new threshold-based discovery service for the EPCglobal network to be used by the Electronic Product Code-Border Gateway Protocol (EPC-BGP)- based discovery service. Our proposal employs hybrid threshold-based triggering and enables status updates in the routing tables based on either elapsed time or number of changes in the status of the EPC whereas the term threshold denotes the upper bound on the ratio of changes in the EPC status table of a supply chain. We evaluate our hybrid technique with respect to update blocking probability and define three types of blocking, namely the Justified Update Blocking (JUB), Unjustified Update Acceptance (UUA), and Unjustified Update Blocking (UUB). We investigate the impact of the frequency of update advertisements on the blocking probability. Through numerical results we show that the hybrid threshold-based update outperforms its counterparts in terms of all types of blocking probability. We further show that the proposed hybrid threshold-based update technique can control blocking probability under frequent product recall arrivals. Moreover, numerical results confirm that our proposal can adapt to traffic change and reduce the blocking probability significantly compared to its counterparts.

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 categoriesnone
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 score0.974
Threshold uncertainty score0.409

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 science0.0010.000
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 head0.015
GPT teacher head0.218
Teacher spread0.203 · 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
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
Published2014
Admission routes1
Has abstractyes

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