Bibliographic record
Abstract
We study the problem of efficient processing of kNN joins over high-dimensional data streams, which is an operation required by many big data applications. Specifically, we are concerned with the continuous evaluation of a set of k nearest neighbor queries Q on streams of high-dimensional items at consecutive snapshots of those streams. While one possible solution is to evaluate the kNN joins starting from scratch at each snapshot, it is too expensive for large volumes of data we encounter in big data applications. We consider the data stream on a time window and maintain the join results for Q at every snapshot in main memory. Our approach to this problem is to build indexes on Q, and only update the results of the queries affected by the changes in the streams at each snapshot. We propose a main-memory structure called the High-dimensional R-tree (HDR-tree) to index the queries, which is efficient in finding affected queries with reasonable maintenance cost. HDR-tree takes advantage of the benefit of clustering and the principle component analysis (PCA) technique. Preliminary experimental results show that our index structures significantly outperform baseline methods.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".