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Record W2134702703 · doi:10.1145/1982185.1982298

Storage and retrieval of system log events using a structured schema based on message type transformation

2011· article· en· W2134702703 on OpenAlexafffund
Adetokunbo Makanju, A. Nur Zincir‐Heywood, Evangelos Milios

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsDalhousie University
FundersNational Institute for Materials ScienceNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsComputer scienceSchema (genetic algorithms)Transformation (genetics)Information retrievalSchema evolutionDatabase schemaDatabase design

Abstract

fetched live from OpenAlex

Message types are semantic groupings of the free form messages in system log events. The message types that exist in a log file, if known, can be used in several log management and analysis tasks. In this work, we explore the use of message types as a schema definition for the storage and retrieval of messages in event logs. We show how message types can be used to impose structure on the unstructured content of event logs and how this structured representation can provide a usable index for searching the contents of the log file. As a side benefit, the structured representation that message types impose also leads to the removal of redundant information in the event logs that leads to space savings on disk.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0050.008
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.028
GPT teacher head0.242
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

Citations7
Published2011
Admission routes2
Has abstractyes

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