New Developments in Practice II: Enterprise Application Integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The term enterprise application integration (EAI) refers to the plans, methods, and tools aimed at modernizing, consolidating, integrating and coordinating the computer applications within an enterprise. The need to integrate across applications is being driven by customer demand for access to information and the desire of the business for a single point of contact with their customer base. The challenges are significant because of the variety of technologies in need of integration and because integration cuts across lines of business. This paper distinguishes among four different (but related) targets of EAI: Data-level integration Application-level integration Process-level integration Inter-organizational-level integration The paper then discusses the technologies that assist with this integration (the "EAI toolkit") under the following categories: Asynchronous Event/Message Transport Transformation Engines Integration Brokers Business Process Management Frameworks The paper concludes by outlining six key strategies for managing EAI suggested by a group of senior IT managers from leading-edge firms.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it