An efficient graph compressor based on adaptive prefix encoding

Jinho Lee, Frank Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we introduce APEC, a graph compression/decompression framework. A key component of APEC is adaptive prefix code, a novel variable-length coding scheme which can adapt to varying characteristics of different vertices in the graph data. APEC also encompasses many software optimization techniques including compressed vertex indexing, bit counting and parallelization. The net outcome is that APEC not only achieves up to 20% improvement on compression ratio, which is equivalent to 2.28 bits/edge, but also as much as 9× faster in compression and up to 20× faster in decompression compared to the existing frameworks. Moreover, APEC is capable of random accessing compressed data and performing compression on extremely large graph datasets.

Original languageEnglish
Title of host publicationProceedings of the 31st International Conference on Scientific and Statistical Database Management, SSDBM 2019
EditorsTanu Malik, Carlos Maltzahn, Ivo Jimenez
PublisherAssociation for Computing Machinery
Pages85-96
Number of pages12
ISBN (Electronic)9781450362160
DOIs
Publication statusPublished - 2019 Jul 23
Event31st International Conference on Scientific and Statistical Database Management, SSDBM 2019 - Santa Cruz, United States
Duration: 2019 Jul 232019 Jul 25

Publication series

NameACM International Conference Proceeding Series

Conference

Conference31st International Conference on Scientific and Statistical Database Management, SSDBM 2019
CountryUnited States
CitySanta Cruz
Period19/7/2319/7/25

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Lee, J., & Liu, F. (2019). An efficient graph compressor based on adaptive prefix encoding. In T. Malik, C. Maltzahn, & I. Jimenez (Eds.), Proceedings of the 31st International Conference on Scientific and Statistical Database Management, SSDBM 2019 (pp. 85-96). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3335783.3335786