MétaCan
Menu
Back to cohort
Record W2802495635 · doi:10.18559/soep.2017.12.1

Benchmarking of Databases for Big Data Exploration in the Social Graph Analysis

2017· article· en· W2802495635 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

VenueStudia Oeconomica Posnaniensia · 2017
Typearticle
Languageen
FieldComputer Science
TopicGraph Theory and Algorithms
Canadian institutionsBausch Health (Canada)
Fundersnot available
KeywordsBenchmarkingComputer scienceBig dataGraph databaseDatabaseData scienceGraphWorld Wide WebInformation retrievalData miningTheoretical computer scienceBusiness

Abstract

fetched live from OpenAlex

In this article we present the results of the benchmarking of various data and knowledge-based systems for use while analysing a big social graph. MySQL, Neo4J, Titan and Virtuoso have been tested on data describing communication events among ca. 7 million users. We have proposed queries regarding the real-world scenarios derived from the requirements analysis of the data owner. Execution time has been measured for basic, aggregation, and networking types of queries. While MySQL was good at queries requiring calculations, Virtuoso outperformed it in graph-related queries.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.164
GPT teacher head0.343
Teacher spread0.179 · 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