Challenges of Provenance in Scientific Workflow Management Systems
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
Scientific workflow is one of the well-established pillars of large-scale computational science and emerged as a torchbearer to formalize and structure a massive amount of complex heterogeneous data and accelerate scientific progress. A workflow can analyze terabyte-scale datasets, contain numerous individual tasks, and coordinate between heterogeneous tasks with the help of scientific workflow management systems (SWfMSs). SWfMSs support the automation of repetitive tasks and capture complex analysis through workflows. However, the execution of workflows is costly and requires a lot of resource usage. At different phases of a workflow life cycle, most SWfMSs store provenance information, allowing result reproducibility, sharing, and knowledge reuse in the scientific community. But, this provenance information can be many times larger than the workflow and input data, and managing provenance data is growing in complexity with large-scale applications. Handling exponential increasing data volume and utilizing the technical resources for storage and computing are thus demanded by exploiting data-intensive computing in various application fields. This paper documented the challenges of provenance management and reuse in e-science, focusing primarily on scientific workflow approaches by exploring different SWfMSs and provenance management systems. We also investigated the ways to overcome the challenges.
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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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