Fine-Grained Attribute Level Locking Scheme for Collaborative Scientific Workflow Development
Bibliographic record
Abstract
Scientific Workflow Management Systems are being widely used in recent years for data-intensive analysis tasks or domain-specific discoveries. It often becomes challenging for an individual to effectively analyze the large scale scientific data of relatively higher complexity and dimensions, and requires a collaboration of multiple members of different disciplines. Hence, researchers have focused on designing collaborative workflow management systems. However, consistency management in the face of conflicting concurrent operations of the collaborators is a major challenge in such systems. In this paper, we propose a locking scheme (e.g., collaborator gets write access to non-conflicting components of the workflow at a given time) to facilitate consistency management in collaborative scientific workflow management systems. The proposed method allows locking workflow components at a granular level in addition to supporting locks on a targeted part of the collaborative workflow. We conducted several experiments to analyze the performance of the proposed method in comparison to related existing methods. Our studies show that the proposed method can reduce the average waiting time of a collaborator by up to 36.19% in comparison to existing descendent modular level locking techniques for collaborative scientific workflow management systems.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".