Exploring Context Sensing in the Goal-Driven Design of Business Processes
Why this work is in the frame
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Bibliographic record
Abstract
As more and more business processes execute in increasingly rich digital environments, there is great opportunity for these processes to make use of the context data to better achieve business goals. Selecting what context data to employ and how to incorporate context sensing into a business process design is therefore of great interest. In this paper, we propose an approach that allows organizations to proactively identify and explore the space of context information that can be sensed and utilized in a business process, with the aim of selecting such context information that can deliver important business benefits. Then, given the selected context information, the approach derives BP design constraints that determine at which points in a business process the selected context information can be sensed and used. The approach builds upon earlier work on goal-driven design of context-aware business processes. Examples from the passenger transportation domain are used for illustration.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it