Designing for Recommending Intermediate States in A Scientific Workflow\n Management System
Bibliographic record
Abstract
To process a large amount of data sequentially and systematically, proper\nmanagement of workflow components (i.e., modules, data, configurations,\nassociations among ports and links) in a Scientific Workflow Management System\n(SWfMS) is inevitable. Managing data with provenance in a SWfMS to support\nreusability of workflows, modules, and data is not a simple task. Handling such\ncomponents is even more burdensome for frequently assembled and executed\ncomplex workflows for investigating large datasets with different technologies\n(i.e., various learning algorithms or models). However, a great many studies\npropose various techniques and technologies for managing and recommending\nservices in a SWfMS, but only a very few studies consider the management of\ndata in a SWfMS for efficient storing and facilitating workflow executions.\nFurthermore, there is no study to inquire about the effectiveness and\nefficiency of such data management in a SWfMS from a user perspective. In this\npaper, we present and evaluate a GUI version of such a novel approach of\nintermediate data management with two use cases (Plant Phenotyping and\nBioinformatics). The technique we call GUI-RISPTS (Recommending Intermediate\nStates from Pipelines Considering Tool-States) can facilitate executions of\nworkflows with processed data (i.e., intermediate outcomes of modules in a\nworkflow) and can thus reduce the computational time of some modules in a\nSWfMS. We integrated GUI-RISPTS with an existing workflow management system\ncalled SciWorCS. In SciWorCS, we present an interface that users use for\nselecting the recommendation of intermediate states (i.e., modules' outcomes).\nWe investigated GUI-RISP's effectiveness from users' perspectives along with\nmeasuring its overhead in terms of storage and efficiency in workflow\nexecution.\n
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".