MétaCan
Menu
Back to cohort
Record W1987787762 · doi:10.1109/lcomm.2014.2332491

An Efficient Binary Locally Repairable Code for Hadoop Distributed File System

2014· article· en· W1987787762 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

VenueIEEE Communications Letters · 2014
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceDistributed data storeOverhead (engineering)Code (set theory)Reliability (semiconductor)LocalityBinary numberDistributed computingFile systemReduction (mathematics)Computational complexity theoryDistributed databaseParallel computingOperating systemAlgorithmArithmeticMathematicsSet (abstract data type)

Abstract

fetched live from OpenAlex

In the Hadoop distributed file systems (HDFSs), to lower costly communication traffic for data recovery, the concept of locally repairable codes (LRCs) has been recently proposed. With regard to the immense size of modern energy-hungry HDFS, computational complexity reduction can be attractive. In this letter, to avoid finite field multiplications, which are the major source of complexity, we put forward the idea of designing binary locally repairable codes (BLRCs). More specifically, we design a BLRC with a length of 15, rate of 2/3, and minimum distance of 4, which has the minimum possible locality among its type. We show that our code has lower complexity than most recent non-binary LRC in the literature while meeting other desirable requirements in HDFS such as storage overhead and reliability.

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 categoriesOpen science
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 score0.648
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0060.001
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 head0.025
GPT teacher head0.273
Teacher spread0.247 · 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