BPMapping and UMM in business process modeling for e-government processes
Bibliographic record
Abstract
Governments worldwide are moving through information and communications technology (ICT) to the e-Government. In Quebec, the e-Government has reached its four phase of development that is the phase of integration necessitating an essential two-level transformation at the front and back offices. At the front-office, the transformation deals with the integration of services offered to citizens, and in the back-office it aims to migrate from systems developed in silos to interoperable and interconnected systems. To achieve this integration, there is a need to redesign business processes to offer integrated services. For this purpose, a modeling of the business processes according to international norms and standards to provide interoperability and interconnectivity is hence necessary. This paper presents a new business process modeling approach. It is based on Business Process Mapping (BPMapping) and the UN/CEFACT Modeling Methodology (UMM). The BPMapping provides an overall view of the business processes showing their inputs, outputs and interdependencies. The details of these business processes are described using UMM and different levels of decomposition. Each level provides diagrams and forms to describe the business and functional requirements and information exchanged between process partners. The approach is applied to the modeling of the Integrated Records Management (IRM) process of the Quebec Government. The results show that the proposed approach identifies, models and documents business processes according to international norms and standards while providing an overall structural and dynamic view of the business process.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| 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".