Structure choices for two-dimensional histogram construction
Bibliographic record
Abstract
Histograms of the distributions of individual attributes are currently used in leading database management systems (e.g., IBM DB2, Oracle Database, and Microsoft SQL server). Because attribute pairs in databases are seldom independent, however, the use of the distributions of individual attributes with the attribute independence assumption often leads to poor estimates. More accurate answers can be obtained by using multi-dimensional histograms to characterize the joint distribution of two or more attributes. When moving from one-dimensional to two-dimensional histograms, several new issues relating to histogram structure arise: (1) Which attribute should take priority over the other with respect to data partitioning?; (2) Into how many partitions should each dimension be split to obtain a desired number of histogram buckets?; and (3) How many most frequent values should be isolated and stored in singleton buckets? In the context of real data, we experimentally show that our proposed methods for dealing with histogram structure choices lead to good quality histograms for a variety of histogram partitioning techniques and various types of data distributions.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".