Toward a Novel Measurement Framework for Big Data (MEGA)
Bibliographic record
Abstract
Big Data is quickly becoming a chief part of the decision-making process in both industry and academia. As more and more institutions begin relying on Big Data to make strategic decisions, the quality of the underlying data comes into question. The quality of Big Data isn’t always transparent and large-scale systems may even lack its visibility, which adversely affects the credibility of the Big Data systems. Continuous monitoring and measurement of data quality is therefore paramount in assessing whether the information can serve its purpose in a particular context (such as Big Data analytics, for example). This research addresses the need for Big Data quality measurement modeling and automation by proposing a novel conceptual quality measurement framework for Big Data (MEGA) with the purpose of assessing the underlying quality characteristics of Big Data (also known as the V’s of Big Data) at each step of the Big Data Pipelines. The theoretical quality measurement models for four of the Big Data V’s (Volume, Variety, Velocity, Veracity) are currently automated; the remaining 6 V’s (Vincularity, Validity, Value, Volatility, Valence and Vitality) will be tackled in our future work. The approach is illustrated on a case study.
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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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".