Functional programs as compressed data

研究成果: Conference contribution

12 引用 (Scopus)

抜粋

We propose an application of programming language techniques to lossless data compression, where tree data are compressed as functional programs that generate them. This "functional programs as compressed data" approach has several advantages. First, it follows from the standard argument of Kolmogorov complexity that the size of compressed data can be optimal up to an additive constant. Secondly, a compression algorithm is clean: it is just a sequence of β-expansions for ?-terms. Thirdly, one can use program verification and transformation techniques (higher-order model checking, in particular) to apply certain operations on data without decompression. In the paper, we present algorithms for data compression and manipulation based on the approach, and prove their correctness. We also report preliminary experiments on prototype data compression/transformation systems.

元の言語English
ホスト出版物のタイトルPOPL
ホスト出版物のサブタイトルPEPM'12 - Proceedings of the ACM SIGPLAN 2012 Workshop on Partial Evaluation and Program Manipulation
出版者Association for Computing Machinery
ページ121-130
ページ数10
ISBN(印刷物)9781450311182
DOI
出版物ステータスPublished - 2012
イベントACM SIGPLAN 2012 Workshop on Partial Evaluation and Program Manipulation, PEPM'12, Co-located with POPL 2012 - Philadelphia, PA, United States
継続期間: 2012 1 232012 1 24

出版物シリーズ

名前Conference Record of the Annual ACM Symposium on Principles of Programming Languages
ISSN(印刷物)0730-8566

Other

OtherACM SIGPLAN 2012 Workshop on Partial Evaluation and Program Manipulation, PEPM'12, Co-located with POPL 2012
United States
Philadelphia, PA
期間12/1/2312/1/24

ASJC Scopus subject areas

  • Software

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  • これを引用

    Kobayashi, N., Matsuda, K., & Shinohara, A. (2012). Functional programs as compressed data. : POPL: PEPM'12 - Proceedings of the ACM SIGPLAN 2012 Workshop on Partial Evaluation and Program Manipulation (pp. 121-130). (Conference Record of the Annual ACM Symposium on Principles of Programming Languages). Association for Computing Machinery. https://doi.org/10.1145/2103746.2103770