Efficiently Evaluating Order Preserving Similarity Queries over Historical Market-Basket Data
Why this work is in the frame
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Bibliographic record
Abstract
We introduce a new domain-independent framework for formulating and efficiently evaluating similarity queries over historical data, where given a history as a sequence of timestamped observations and the pair-wise similarity of observations, we want to find similar histories. For instance, given a database of customer transactions and a time period, we can find customers with similar purchasing behaviors over this period. Our work is different from the work on retrieving similar time series; it addresses the general problem in which a history cannot be modeled as a time series, hence the relevant conventional approaches are not applicable. We derive a similarity measure for histories, based on an aggregation of the similarities between the observations of the two histories, and propose efficient algorithms for finding an optimal alignment between two histories. Given the non-metric nature of our measure, we develop some upper bounds and an algorithm that makes use of those bounds to prune histories that are guaranteed not to be in the answer set. Our experimental results on real and synthetic data confirm the effectiveness and efficiency of our approach. For instance, when the minimum length of a match is provided, our algorithm achieves up to an order of magnitude speed-up over alternative methods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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