Bibliographic record
Abstract
SOA enables integration of applications and resources flexibly, representing every application or resource as a service. Its purpose is to facilitate reuse and interoperability of applications, which are regarded as quality attributes of a system. It is very easy to talk about the benefits of SOA (reuse, etc.). But, there are no precise specifications to define this concept as the architectural style. SOA has another shortcoming; it is a problem of performance related to the creation of services that affect the total processing time of the system. This paper provides a basic specification of SOA and identifies architectural tactics to improve performance. The tactics adopted for the performance are then validated by a case study. A solution for the development of tactics is to use the ADD method. This is a method to meet the architectural requirements or qualities expected from a system. Three architectural models have been well integrated into SOA. Validation of the case study determined that the tactics are working and it is interesting to use in SOA architecture. However, an interesting point that arises from the test is that the decomposition model of service can be used with caution. Two contributions emerge from this paper: a basic specification and a design of SOA-based integration models (architectural) to improve performance. The main recommendation arising from this test is the addition of tactical or creating tools to automate the architecture chosen and thus improve performance.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".