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Top- k Frequent Patterns in Streams and Parameterized-Space LZ Compression

Authors Patrick Dinklage , Johnnes Fischer, Nicola Prezza

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Author Details

Patrick Dinklage
  • TU Dortmund University, Germany
Johnnes Fischer
  • TU Dortmund University, Germany
Nicola Prezza
  • Ca' Foscari University of Venice, Italy

Funding

  • Dinklage, Patrick: Funded by the Deutsche Forschungsgemeinschaft (DFG) under the Research Grants programme (project No. 501086801).
  • Fischer, Johnnes: Funded by the Deutsche Forschungsgemeinschaft (DFG) under the Research Grants programme (project No. 501086801).
  • Prezza, Nicola: Funded by the European Union (ERC, REGINDEX, 101039208). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

Acknowledgements

The authors gratefully acknowledge the computing time provided on the Linux HPC cluster at Technical University Dortmund (LiDO3), partially funded in the course of the Large-Scale Equipment Initiative by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) as project 271512359.

Cite AsGet BibTex

Patrick Dinklage, Johnnes Fischer, and Nicola Prezza. Top- k Frequent Patterns in Streams and Parameterized-Space LZ Compression. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 9:1-9:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.SEA.2024.9

Abstract

We present novel online approximations of the Lempel-Ziv 77 (LZ77) and Lempel-Ziv 78 (LZ78) compression schemes [Lempel & Ziv, 1977/1978] with parameterizable space usage based on estimating which k patterns occur the most frequently in the streamed input for parameter k. This new approach overcomes the issue of finding only local repetitions, which is a natural limitation of algorithms that compress using a sliding window or by partitioning the input into blocks. For this, we introduce the top-k trie, a summary for maintaining online the top-k frequent consecutive patterns in a stream of characters based on a combination of the Lempel-Ziv 78 compression scheme and the Misra-Gries algorithm for frequent item estimation in streams. Using straightforward encoding, our implementations yield compression ratios (output over input size) competitive with established general-purpose LZ-based compression utilities such as gzip or xz.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data compression
  • Theory of computation → Pattern matching
  • Theory of computation → Sketching and sampling
Keywords
  • compression
  • streaming
  • heavy hitters
  • algorithm engineering

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Supplementary Materials

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