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Access Time Tradeoffs in Archive Compression

  • Matthias Petri
  • Alistair Moffat
  • P. C. Nagesh
  • Anthony Wirth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9460)

Abstract

Web archives, query and proxy logs, and so on, can all be very large and highly repetitive; and are accessed only sporadically and partially, rather than continually and holistically. This type of data is ideal for compression-based archiving, provided that random-access to small fragments of the original data can be achieved without needing to decompress everything. The recent RLZ (relative Lempel Ziv) compression approach uses a semi-static model extracted from the text to be compressed, together with a greedy factorization of the whole text encoded using static integer codes. Here we demonstrate more precisely than before the scenarios in which RLZ excels. We contrast RLZ with alternatives based on block-based adaptive methods, including approaches that “prime” the encoding for each block, and measure a range of implementation options using both hard-disk (HDD) and solid-state disk (SSD) drives. For HDD, the dominant factor affecting access speed is the compression rate achieved, even when this involves larger dictionaries and larger blocks. When the data is on SSD the same effects are present, but not as markedly, and more complex trade-offs apply.

Keywords

Block Size Random Access Compression Rate Factor Length Dictionary Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported under the Australian Research Council’s Discovery Projects scheme (project DP140103256).We have had access to the code of Hoobin et al. while working on this project, and we thank them for making it available.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Matthias Petri
    • 1
  • Alistair Moffat
    • 1
  • P. C. Nagesh
    • 1
  • Anthony Wirth
    • 1
  1. 1.Department of Computing and Information SystemsThe University of MelbourneVictoriaAustralia

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