Bi-temporal Timeline Index: A data structure for Processing Queries on bi-temporal data
Why this work is in the frame
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Bibliographic record
Abstract
Following the adoption of basic temporal features in the SQL:2011 standard, there has been a tremendous interest within the database industry in supporting bi-temporal features, as a significant number of real-life workloads would greatly benefit from efficient temporal operations. However, current implementations of bi-temporal storage systems and operators are far from optimal. In this paper, we present the Bi-temporal Timeline Index, which supports a broad range of temporal operators and exploits the special properties of an in-memory column store database system. Comprehensive performance experiments with the TPC-BiH benchmark show that algorithms based on the Bi-temporal Timeline Index outperform significantly both existing commercial database systems and state-of-the-art data structures from research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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