Enterprise frameworks: issues and research directions
Bibliographic record
Abstract
Abstract Enterprise frameworks are a special class of application frameworks. They are distinguished from other application frameworks in terms of scale and focus. In terms of focus, application frameworks typically cover one particular aspect of an application, either a domain‐dependent aspect (e.g., billing in a web‐based customer‐to‐business ordering system), or a computational infrastructure aspect such as distribution, man‐machine interface, or persistence, etc. Generally, an application framework alone delivers no useful end‐user function. With infrastructure frameworks, we still have to plug in domain functionalities, while with domain frameworks, we need to set‐up the infrastructure. In contrast, enterprise frameworks embody a reference architecture for an entire application, covering both the infrastructure aspects of the application, and much of the domain‐specific functionality. Instantiating an enterprise framework is nothing short of application engineering, where the architecture and many of the components are reusable. While creativity and continual improvement may be the major ingredients for building a good application framework, anything related to enterprise frameworks, be it building, documenting, or instantiating them, is complex and requires careful design and planning. In this paper, we identify the issues involved in building, using, and maintaining enterprise frameworks, both from research and practical perspective. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.037 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.017 | 0.045 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".