Lempel-ziv factorization using less time and space
Bibliographic record
Abstract
For 30 years the Lempel-Ziv factorization LZx of a string x = x[1..n] has been a fundamental data structure of string processing, especially valuable for string compression and for computing all the repetitions (runs) in x. When the Internet came in, a huge need for Lempel-Ziv factorization was created. Nowadays it has become a basic efficient data transmission format on the Internet. Traditionally the standard method for computing LZx was based on O(n)-time processing of the suffix tree STx of x. Ukkonen's algorithm constructs suffix tree online and so permits LZ to be built from subtrees of ST; this gives it an advantage, at least in terms of space, over the fast and compact version of McCreight's STCA [37] due to Kurtz [24]. In 2000 Abouelhoda, Kurtz & Ohlebusch proposed a O(n)-time Lempel-Ziv factorization algorithm based on an "enhanced" suffix array - that is, a suffix array SAx together with other supporting data structures. In this thesis we first examine some previous algorithms for computing Lempel-Ziv factorization. We then analyze the rationale of development and introduce a collection of new algorithms for computing LZ-factorization. By theoretical proof and experimental comparison based on running time and storage usage, we show that our new algorithms appear either in their theoretical behavior or in practice or both to be superior to those previously proposed. In the last chapter the conclusion of our new algorithms are given, and some open problems are pointed out for our future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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 source (direct Gemma or distilled Codex), 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".