A Variability-Aware Design Approach to the Data Analysis Modeling\n Process
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
The massive amount of current data has led to many different forms of data\nanalysis processes that aim to explore this data to uncover valuable insights.\nMethodologies to guide the development of big data science projects, including\nCRISP-DM and SEMMA, have been widely used in industry and academia. The data\nanalysis modeling phase, which involves decisions on the most appropriate\nmodels to adopt, is at the core of these projects. However, from a software\nengineering perspective, the design and automation of activities performed in\nthis phase are challenging. In this paper, we propose an approach to the data\nanalysis modeling process which involves (i) the assessment of the variability\ninherent in the CRISP-DM data analysis modeling phase and the provision of\nfeature models that represent this variability; (ii) the definition of a\nframework structural design that captures the identified variability; and (iii)\nevaluation of the developed framework design in terms of the possibilities for\nprocess automation. The proposed approach advances the state of the art by\noffering a variability-aware design solution that can enhance system\nflexibility, potentially leading to novel software frameworks which can\nsignificantly improve the level of automation in data analysis modeling\nprocess.\n
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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