Online Interval Skyline Queries on Time Series
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In many applications, we need to analyze a large number of time series. Segments of time series demonstrating dominating advantages over others are often of particular interest. In this paper, we advocate interval skyline queries, a novel type of time series analysis queries. For a set of time series and a given time interval [i : j], an interval skyline query returns the time series which are not dominated by any other time series in the interval. We illustrate the usefulness of interval skyline queries in applications. Moreover, we develop an on-the-fly method and a view-materialization method to online answer interval skyline queries on time series. The on-the-fly method keeps the minimum and the maximum values of the time series using radix priority search trees and sketches, and computes the skyline at the query time. The view-materialization method maintains the skylines over all intervals in a compact data structure. Through theoretical analysis and extensive experiments, we show that both methods only require linear space and are efficient in query answering as well as incremental maintenance.
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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 | 0.000 | 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 | 0.003 |
| Open science | 0.003 | 0.001 |
| 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