Designing business processes and communication structures for e-business using ontology-based enterprise models with mathematical models
Bibliographic record
Abstract
Organizations are apprehensive about developing e-business systems because the endeavor is novel. If ebusiness is considered as the conduct of business using the Internet—a network of networks—then ebusiness systems design can be represented as a network design problem. This paper outlines an approach for analysis and design of business process and communications structure networks for e-business. Network design alternatives are generated by applying best practices and design principles to business requirements, using ontology-based enterprise models. Alternatives then are modeled mathematically for analysis and comparison. Domains relevant for e-business systems design are described, formally and systematically, using this approach. These formal descriptions are general axioms, used to logically and mathematically infer prescriptions for specific design problems. These descriptions and prescriptions are sharable and re-usable. The mathematical models are developed using known algorithms, heuristics, and formulae. Therefore, fidelity of prescriptions based on these models can be objectively justified. Due to these characteristics, models developed using this technique are especially useful for developing novel ebusiness systems. An example application of this technique is presented, and research questions addressed using the approach are discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".