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Record W2112162877 · doi:10.1109/mascot.2002.1167096

WebTraff: a GUI for Web proxy cache workload modeling and analysis

2003· article· en· W2112162877 on OpenAlexafffund
Nayden Markatchev, Carey Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceCacheWorkloadLocalityProxy (statistics)Web pageWeb applicationWeb serverOperating systemDatabaseWorld Wide WebThe InternetMachine learning

Abstract

fetched live from OpenAlex

The paper describes an interactive graphical user interface (GUI) that can be used for the modeling and analysis of Web proxy workloads. The WebTraff GUI has three main components. First, the WebTraff tool provides a visual front-end to ProWGen, a Web proxy workload generation tool developed in prior work (see Busari, M. and Williamson, C., Computer Networks, vol.38, no.6, p.779-94, 2002), which can be used for generating synthetic Web proxy workloads of arbitrary length, with user-specified statistical properties. Second, the WebTraff GUI provides tools for the analysis of Web proxy workload characteristics, including document size distribution, document popularity profile, and temporal locality properties. Third, the GUI provides a front-end to a simple Web proxy cache simulation program, which can be used in studies of Web proxy cache performance and cache filter effects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.038
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0380.015

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 head0.025
GPT teacher head0.238
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations42
Published2003
Admission routes2
Has abstractyes

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