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Record W2954173133 · doi:10.22215/etd/2018-12991

Distributed Real-Time Analysis of High-Velocity Many-Dimensional Data Sets

2018· dissertation· en· W2954173133 on OpenAlexfundno aff
David Robillard

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicData Management and Algorithms
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceScalabilityDistributed computingAggregate (composite)Cloud computingKey (lock)Set (abstract data type)Isolation (microbiology)DatabaseHigh availabilityData miningReal-time computingOperating system

Abstract

fetched live from OpenAlex

Fast aggregation of data with many hierarchical dimensions is a key component of many data analysis applications. Recent years have seen increased interest in the ability to do so in real-time, allowing live analysis of rapidly changing data sets. At the same time, data sets of interest have increased in size well beyond the capabilities of a single machine to process efficiently. With the widespread availability of virtualized cloud computing environments, organizations increasingly demand systems that can be deployed on commodity platforms.

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: Methods · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.285
Teacher spread0.264 · 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
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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