Performance and Implementation Comparison of Knuth-Morris-Pratt and Boyer-Moore String Search Algorithms
Bibliographic record
Abstract
String search algorithms play an important role in many research areas such as data mining and bioinformatics. While there exist a number of algorithms that handles the topic, we are exploring the the Knuth-Morris-Pratt (KMP) and Boyer-Moore algorithms due to their efficiency and versatility. In this work, we compared the algorithms in terms of characteristics, performance and implementation details. We also tested both the algorithms with various patterns and texts that differs in size. We also analyzed the performance of the algorithms on 4 different processors to understand the technological advancements effects on their performance. Our findings suggest that the BM algorithm perform better with large texts and patterns, while the KMP algorithm is better suited for smaller ones. Also, while newer processor generally exhibit improved performance, the significance of these enhancements may vary. Thus, we should rather be looking specific architectural advancements within generations rather than focusing solely on the generational gap.
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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 | 0.001 |
| Open science | 0.000 | 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 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".