Enhancing E-Service Collaboration with Enforcement and Relationship Management
Bibliographic record
Abstract
In a business-to-business (B2B) e-service environment, cross-organizational collaboration is important for attaining the interoperability of business processes and their proper enactment. The authors find that B2B collaboration can be divided into multiple layers and perspectives, which has not been adequately addressed in the literature. Besides regular e-service process enactment, robust collaboration requires enforcement, while quality collaboration involves relationship management. These problems are challenging, as they require the enactment of business processes and their monitoring in counter parties outside an organization’s boundary. This paper presents a framework for B2B process collaboration with three layers, namely, collaboration requirements layer, business rule layer, and system implementation layer. The collaboration requirements layer specifies the cross-organizational requirements of e-service processes. In the business rule layer, detailed knowledge of these three types of process collaboration requirements is defined as business rules in a unified Event-Condition-Action (ECA) form. In the system implementation layer, event collaboration interfaces are supported by contemporary Enterprise JavaBeans and Web Services. Based on this architecture, a methodology is presented for the engineering of e-service process collaboration from high-level business requirements down to system implementation details. As a result, B2B process collaboration can be seamlessly defined, enacted, and enforced. Conceptual models of various layers are given in the Unified Modeling Language (UML). We illustrate the applicability of our framework with a running example based on a supply-chain process and evaluate our approach from the perspective of three main stakeholders of e-collaboration, namely users, management, and systems developers.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".