CASCON workshop on developing big data applications and services
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.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.018 |
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".