Rigorous Measurement Model for Validity of Big Data: MEGA Approach
Bibliographic record
Abstract
Big Data is becoming a substantial part of the decision-making processes in both industry and academia, especially in areas where Big Data may have a profound impact on businesses and society. However, as more data is being processed, data quality is becoming a genuine issue that negatively affects credibility of the systems we build because of the lack of visibility and transparency of the underlying data. Therefore, Big Data quality measurement is becoming increasingly necessary in assessing whether data can serve its purpose in a particular context (such as Big Data analytics, for example). This research addresses Big Data quality measurement modelling and automation by proposing a novel quality measurement framework for Big Data (MEGA) that objectively assesses the underlying quality characteristics of Big Data (also known as the V's of Big Data) at each step of the Big Data Pipelines. Five of the Big Data V's (Volume, Variety, Velocity, Veracity and Validity) are currently automated by the MEGA framework. In this paper, a new theoretically valid quality measurement model is proposed for an essential quality characteristic of Big Data, called Validity. The proposed measurement information model for Validity of Big Data is a hierarchy of 4 derived measures / indicators and 5 based measures. Validity measurement is illustrated on a running example.
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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 | 0.048 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".