A Process Mining Method for Inter-organizational Business Process Integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Business process integration (BPI) allows organizations to connect and automate their business processes in order to deliver the right economic resources at the right time, place, and price. BPI requires the integration of business processes and their supporting systems across multiple autonomous organizations. However, such integration is complex and can face coordination complexities that occur during the resource exchanges between the partners’ processes. This article proposes a new method called Process Mining for Business Process Integration (PM4BPI) that helps process designers to perform BPI by creating new process models that cross the boundaries of multiple organizations from a collection of process event logs. PM4BPI uses federated process mining techniques to detect incompatibilities before the integration of the partners’ processes. Then, it applies process adaptation patterns to solve detected incompatibilities. Finally, organizations’ processes are merged to build a collaborative process model that crosses the organizations’ boundaries. Adapt WF_Net , an extension of a Petri net, is used to design inter-organizational business processes and adaptation patterns. An integrated care pathway is used as a case study to assess the applicability and effectiveness of the proposed method.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 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