Bloom Filter Tree for Fast Search and Synchronization of Tree-Structured Data
Bibliographic record
Abstract
We consider two problems in the context of tree-structured data sets (e.g., XML): (1) searching for a data element, (2) synchronizing two data trees (replicas) stored at remote locations. We propose to compute bloom filters for the interior tree nodes, this bloom filter tree is used for both data search and synchronization. It is more efficient than tree traversal since it prunes out entire subtrees, while still retaining the important metadata (in the interior nodes) that cannot be achieved by any linear list search of leaf nodes. We present a theroetical analysis of the search complexity of selective placement of bloom filters in the tree leading to an optimal placement strategy. We implement the classic rsync algorithm for comparison and verify the efficiency of our method in terms of lower network overhead and faster runtime. We also implement our bloom filter search method as an Android application on the mobile device, tested its performance on the real DBLP data set and verified its efficiency.
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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".