Evaluation of Basic Data Compression Algorithms in a Distributed Environment
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Bibliographic record
Abstract
Data compression methods aim at reducing the data size in order to make data transfer more efficient. To accomplish data compression, the basic algorithms such as Huffman, Lempel-Ziv (LZ), Shannon-Fano (SF) and Run-Length Encoding (RLE) are widely used. Most of the applications incorporate different variants of these algorithms. This paper analyzes the execution times, compression ratio and efficiency of compression methods in a client-server distributed environment. The data from a client is distributed to multiple processors/servers, subsequently compressed by the servers at remote locations, and sent back to the client. Our experimentation has been carried out using Simgrid Framework. Our results show that the LZ algorithm attains better efficiency/scalability and compression ratio, however, it works slower than other algorithms.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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