Bibliographic record
Abstract
In a world of dynamic and discontinuous change, systems constantly need to adapt to new conditions so that they can survive and flourish in their environment. Autonomic computing emerged as a research field that takes up this challenge and aims to build systems that are capable of adapting automatically to dynamically changing environments (Self-configuring), discovering, diagnosing and reacting to disruptions (Self-healing), monitoring and tuning resources automatically (Self-optimizing) and anticipating, detecting, identifying and protecting themselves from attacks (Self-protecting) [3]. A major application area for autonomic computing is intended to be system administration, aiming to free system administrators from the details of system operation and maintenance [8], improving robustness of systems and decreasing total cost of ownership. However, the vision of autonomic computing does not need to be restricted to the area of system administration. For example, much research has been done in the area of process-aware information systems [2] such as Workflow Management, Enterprise Resource Planning, Business-to-Business and Customer Relationship systems to effectively and efficiently deal with change on different levels and scales. Frequent questions in these domains include: How can changes to workflows be accommodated? How can flexibility and adaptability of running workflow instances be increased? How can workflow management systems themselves optimize workflow definitions? The type of questions raised here seems to address issues that are similarly addressed by research in autonomic computing, where dealing with change represents a major concern. However, little research has been done on the intersection between these two domains [4]. Based on this observation, this contribution aims to tackle the question: "Can the principles of autonomic computing be applied to workflow management - and if so, how?"
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".