Name
Abstract | ||
|---|---|---|
Data compression is a powerful tool for managing massive but repetitive datasets, especially schemes such as grammar-based compression that support computation over the data without decompressing it. In the best case such a scheme takes a dataset so big that it must be stored on disk and shrinks it enough that it can be stored and processed in internal memory. Even then, however, the scheme is essentially useless unless it can be built on the original dataset reasonably quickly while keeping the dataset on disk. In this paper we show how we can preprocess such datasets with context-triggered piecewise hashing such that afterwards we can apply RePair and other grammar-based compressors more easily. We first give our algorithm, then show how a variant of it can be used to approximate the LZ77 parse, then leverage that to prove theoretical bounds on compression, and finally give experimental evidence that our approach is competitive in practice. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32686-9_3 | SPIRE |
Field | DocType | Volume |
Internal memory,Discrete mathematics,Algorithm,Grammar,Hash function,Parsing,Data compression,Mathematics,Piecewise,Computation | Journal | abs/1906.00809 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Travis Gagie | 1 | 643 | 63.61 |
| Tomohiro I | 2 | 148 | 22.06 |
| Giovanni Manzini | 3 | 1584 | 111.42 |
| Gonzalo Navarro | 4 | 109 | 11.07 |
| Hiroshi Sakamoto | 5 | 17 | 2.71 |
| Yoshimasa Takabatake | 6 | 29 | 7.27 |