AN OPEN SERVICE ARCHITECTURE FOR THE HYPERION PEER DATABASE SYSTEM
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".