Effective Summarization of Multi-Dimensional Data Streams for Historical Stream Mining
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
We consider the following problem: given a very large data stream, a limited space to encode the stream, and a compression technique to compress the stream, retain the most important information from the distant past of the stream while at the same time retain high quality of the compressed information that is in the recent part of the stream to perform temporal analysis of the summarized information. Simple schemes for accumulating micro-clustering summaries of stream windows that have been previously proposed are very ineffective for solving this challenging task. We overcome the limitations of these schemes by first identifying spatial summaries that compress "similar' regions in the data space, and reduce their space consumption using novel approximate spatio-temporal summaries. Second, we present policies for effectively utilizing the space budget and managing these novel approximate spatio-temporal summaries.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 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