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Record W2133682093 · doi:10.1109/dcs.1988.12556

On the communication cost of distributed database processing

2003· article· en· W2133682093 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
TopicAdvanced Data Storage Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceSortingDistributed databaseTestbedEthernetHash functionJoin (topology)WorkstationDistributed computingDistributed memoryTransfer (computing)Parallel computingComputer networkShared memoryOperating systemAlgorithm

Abstract

fetched live from OpenAlex

Various communication aspects of locally distributed database processing are studied, using some distributed sorting and distributed hash-based join algorithms as examples. The algorithms are implemented on diskless workstations connected by an Ethernet network to simulate a distributed main memory system environment. This experimental testbed is described. Raw communication performance data (i.e. memory-to-memory data transfer timing) are presented. The effects of the underlying distributed operating system and the speed of the processor on the communication performance are shown. Two distributed sorting algorithms are used as examples to study the issue of concurrent transmissions of messages. Distributed hash join is used as a case study for communication/local-processing tradeoff. The idea of load sharing among a number of sites to speed up the join operation is introduced.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.036
GPT teacher head0.284
Teacher spread0.248 · 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

Citations4
Published2003
Admission routes1
Has abstractyes

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