On MBR Approximation of Histories for Historical Queries: Expectations and Limitations
Bibliographic record
Abstract
Traditional approaches for efficiently processing historical queries, where a history is a multidimensional time-series, employ a two step filter-and-refine scheme. In the filter step, an approximation of each history often as a set of minimum bounding hyper-rectangles (MBRs) is organized using a spatial index structure such as R-tree. The index is used to prune redundant disk accesses and to reduce the number of pairwise comparisons required in the refine step. To improve the efficiency of the filtering step, a heuristic is used to decrease the expected number of MBRs that overlap with a query, by reducing the volume of empty space indexed by the index. The heuristic selects, among all possible splitting schemes of a history, the one which results to a set of MBRs with minimum total volume. Although this heuristic is expected to improve the performance of spatial and history based queries with small temporal and spatial extents, in many real settings, the performance of historical queries depends on the extent of the query. Moreover, the optimal approximation of a history is not always the one with minimum total volume. In this paper, we present the limitations of using volume as a criteria for approximating histories, specially in high dimensional cases, where it is not feasible to index MBRs by traditional spatial index structures.
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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 | 0.018 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".