CASCON workshop on developing big data applications and services
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
Research from Gartner (2015) indicates that, in 2017, 60% of Big Data projects failed or did not provide the expected benefits [1]. However, in November 2017, Nick Heudecker, a Gartner analyst, posted in his twitter account that they were too conservative. The Big Data project failure rate is now close to 85%. The reasons are not only related to technology itself [2]. It is a mix of environmental, technological and managerial problems. Some of the reasons for Big Data projects failure are: At the project level [3], [4]: missing link to business objectives, lacking big data skills, relying too much on the data, failing to convince executives, and poor planning; At the technical level [5]: Rapid technology changes, difficulty in selecting Big Data technologies to address the systems and project requirements, complex integration between new and old systems, computation of intensive analytics, and the necessity of high scalability, availability and reliability, to name a few. Further, a previous study [6] has shown that there is approximately a 80:20 split in the industry focus in favor of algorithms for analytics and infrastructure, thereby shortchanging the aspects of creating and evolving applications and services concerned with Big Data.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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