Business process monitoring and alignment: An approach based on the user requirements notation and business intelligence tools
Bibliographic record
Abstract
Monitoring business activities using Business Intelligence (BI) tools is a well-established concept. However, online process monitoring is an emerging area which helps organizations not only plan for future improvements but also change and alter their current ongoing processes before problems happen. In this paper, we explore how monitoring process performance can help evolve process goals and requirements. We elaborate an approach that uses the User Requirements Notation (URN) to model the goals and processes of the organization, and to monitor and align processes against their goals. A BI tool exploiting an underlying data warehouse provides the Key Performance Indicators (KPI) used to measure the satisfaction of goals and process requirements. Feeding this information into the URN modeling tool, we can analyze the consequences of current business activities on desired business goals, which can be used for process and business activity alignment thereafter. We illustrate the approach with a case study from the healthcare sector: a hospital discharge 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 | 0.044 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| 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".