A Variability-Aware Design Approach to the Data Analysis Modeling\n Process
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
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".