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Record W2024557906 · doi:10.1142/s0218843008001762

AN OPEN SERVICE ARCHITECTURE FOR THE HYPERION PEER DATABASE SYSTEM

2008· article· en· W2024557906 on OpenAlexaff
Tasmeia Yousaf, Iluju Kiringa, Lei Jiang

Bibliographic record

VenueInternational Journal of Cooperative Information Systems · 2008
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceArchitectureWeb serviceLeverage (statistics)Peer-to-peerService-oriented architectureService (business)Applications architectureFlexibility (engineering)DatabaseComputer networkWorld Wide WebDistributed computingSoftware architectureOperating system

Abstract

fetched live from OpenAlex

The need for data sharing across heterogeneous data sources is growing. Peer Database Management Systems (PDBMSs) offer one data sharing approach, which favors a direct and dynamic node-to-node model of communication with no centralized control. Moreover, Service Oriented Architectures (SOA) using Web service technologies allow users to leverage existing assets towards the goal of building new architectures and integrating existing systems that can be componentized. We propose an Open Service Architecture for PDBMSs (OSAP). This architecture offers the main services of a PDBMS as Web services that are invoked via the communication network using a set of well-defined interfaces. This approach provides power and flexibility in terms of development and usage of the system. We have implemented this architecture within the Hyperion PDBMS infrastructure. We provide an analysis of the implementation of the OSAP architecture in terms of its characteristics. We also conduct a performance comparison with both the original Hyperion architecture, and a much simpler architecture that hides all the internal functionalities offered by a PDBMS as private processes that can be used by other peers only through one single web service which acts as peer manager.

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 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 categoriesScholarly communication
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 score0.963
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0010.005
Open science0.0050.000
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 head0.305
Teacher spread0.269 · 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 teacher head, not a consensus.

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

Citations0
Published2008
Admission routes1
Has abstractyes

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