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
Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby massive amounts of heterogeneous data are gathered from multiple sources, managed, analyzed (in batch, stream or hybrid fashion), and served to end-users and external applications. Such systems pose specific challenges in all phases of software development lifecycle and might become very complex by evolving data, technologies, and target value over time. Consequently, many organizations and enterprises have found it difficult to adopt BDSs. In this article, we provide insight into three major activities of software engineering in the context of BDSs as well as the choices made to tackle them regarding state-of-the-art research and industry efforts. These activities include the engineering of requirements, designing and constructing software to meet the specified requirements, and software/data quality assurance. We also disclose some open challenges of developing effective BDSs, which need attention from both researchers and practitioners.
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 | 0.071 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.027 | 0.030 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.007 |
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