An Empirical Study of Rabin Fingerprinting Parameters
Why this work is in the frame
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Bibliographic record
Abstract
Summarizing, comparing, and indexing large data files are common operations in information retrieval and data-storage systems. Consequently, Rabin fingerprinting is a common technique for string matching, and to cut a file into variable-length chunks for faster pattern matching of large datasets. Ordered lists of hashes (of the chunks), representing the original files, can require fewer bytes to store and to transmit across a network than the files themselves. Lists of hashes can also be compared like substrings to find files with common chunks of data. Finally, lists of hashes can also be used for data deduplication to save on storage (and transmission) or to synchronize two similar copies of the same file. However, it is important to make appropriate choices for Rabin fingerprinting parameters such as the sliding window size, the degree of the irreducible polynomial, the mask size, and the cut value for the hash. Therefore, we present an empirical, parameter-sweep study of Rabin fingerprinting on a non-trivial workload based on the Linux kernel source code. As a result, we make some best-practice recommendations for using Rabin fingerprinting. For example, we characterize how a cut value of one is less problematic than a natural choice of zero for the cut value.
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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 | 0.000 | 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