A Disc-based Approach to Data Summarization and Privacy Preservation
Bibliographic record
Abstract
Data summarization has been recognized as a fundamental operation in database systems and data mining with important applications such as data compression and privacy preservation. While the existing methods such as CF-values and DataBubbles may perform reasonably well, they cannot provide any guarantees on the quality of their results. In this paper, we introduce a summarization approach for numerical data based on discs formalizing the notion of quality. Our objective is to find a minimal set of discs, i.e. spheres satisfying a radius and a significance constraint, covering the given dataset. Since the proposed problem is NP-complete, we design two different approximation algorithms. These algorithms have a quality guarantee, but they do not scale well to large databases. However, the machinery from approximation algorithms allows a precise characterization of a further, heuristic algorithm. This heuristic, efficient algorithm exploits multi-dimensional index structures and can be well-integrated with database systems. The experiments show that our heuristic algorithm generates summaries that outperform the state-of-the-art data bubbles in terms of internal measures as well as in terms of external measures when using the data summaries as input for clustering methods
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".