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Record W2017975200 · doi:10.1109/cloudcom.2013.60

Innesto: A Searchable Key/Value Store for Highly Dimensional Data

2013· article· en· W2017975200 on OpenAlexaff
Mahdi Tayarani Najaran, Norman C. Hutchinson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceAssociative arrayKey (lock)ScalabilityCloud computingDatabaseConsistency (knowledge bases)NoSQLSet (abstract data type)Value (mathematics)Component (thermodynamics)Operating system

Abstract

fetched live from OpenAlex

Key/value data storage systems serve as the fundamental component of scalable cloud-based services. However, the scalability of existing key/value data stores comes at the cost of a narrow data access API. We present Innesto, a distributed key/value data store that provides search as part of its API. Search allows data items to be retrieved based on constraints on multiple different attributes. Innesto's strong consistency data model and its transactional interface bring much of the power of traditional relational databases to cloud-scale performance. Our evaluation of Innesto shows it offers these extra features with competitive performance compared to an industrial key/value data store such as Cassandra which offers an inferior feature set.

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)0.001
metaresearch head score (Gemma)0.007
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: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0050.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.038
GPT teacher head0.277
Teacher spread0.238 · 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
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

Citations7
Published2013
Admission routes1
Has abstractyes

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