A Novel Rigorous Measurement Model for Big Data Quality Characteristics
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
Satisfiable data quality is the basic guarantee for data-based research, decision-making, and service. Today, new trends in the creation, collection, and utilization of data are constantly emerging. With the usage of massive data, the problem of data quality is highlighted. Several studies on the measurement, evaluation, and management of big data quality have been proposed, and the data quality problem in the big data environment has received attention. The big data characteristics Vs model describes the dimensions and attributes information of data sources in detail, which can be implemented in big data quality measurement. In this paper, a novel rigorous big data quality measurement architecture is proposed for automatically and parallelly quantifying the value of six big data Vs, which are Volume, Variety, Velocity, Veracity, Validity, and Vincularity according to the developed algorithms in every big data process step and time phase of the big data pipeline. Thresholds for the six big data Vs are provided correspondingly for analyzing the result values. The hierarchical measurement model is constructed with multiple-based measures, derived measures, and indicators. The model is verified by comparative experiments and experiments results indicate that the designed architecture can improve the outcomes of data source implementation.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.016 | 0.014 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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