Maintaining Knowledge-Bases of Navigational Patterns from Streams of Navigational Sequences
Bibliographic record
Abstract
In this paper we explore an alternative design goal for navigational pattern discovery in stream environments. Instead of mining based on thresholds and returning the patterns that satisfy the specified threshold(s), we propose to mine without thresholds and return all identified patterns along with their support counts in a single pass. We utilize a sliding window to capture recent navigational sequences and propose a batch-update strategy for maintaining the patterns within a sliding window. Our batch-update strategy depends on the ability to efficiently mine the navigational patterns without support thresholds. To achieve this, we have designed an efficient algorithm for mining contiguous navigational patterns without support thresholds. Our experiments show that our algorithm outperforms the existing techniques for mining contiguous navigational patterns. Our experiments also show that the proposed batch-update strategy achieves considerable speed-ups compared to the existing window update strategy, which requires total re-computation of patterns within each new window.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".