Data compression using word encoding with Huffman code
A technique for compressing large databases is presented. The method replaces frequent variable‐length byte strings (words or word fragments) in the database by minimum‐redundancy codes—Huffman codes. An essential part of the technique is the construction of the dictionary to yield high compression...
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| Published in | Journal of the American Society for Information Science Vol. 42; no. 9; pp. 685 - 698 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Washington, D.C
Wiley Subscription Services, Inc., A Wiley Company
01.10.1991
John Wiley & Sons American Documentation Institute Wiley Periodicals Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0002-8231 1097-4571 |
| DOI | 10.1002/(SICI)1097-4571(199110)42:9<685::AID-ASI7>3.0.CO;2-1 |
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| Abstract | A technique for compressing large databases is presented. The method replaces frequent variable‐length byte strings (words or word fragments) in the database by minimum‐redundancy codes—Huffman codes. An essential part of the technique is the construction of the dictionary to yield high compression ratios. A heuristic is used to count frequencies of word fragments. A detailed analysis is provided of our implementaton in support of high compression ratios and efficient encoding and decoding under the constraint of a fixed amount of main memory. In each phase of our implementation, we explain why certain data structures or techniques are employed. Experimental results show that our compression scheme is very effective for compressing large databases of library records. © 1991 John Wiley & Sons, Inc. |
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| AbstractList | A technique for compressing large databases is presented. The method replaces frequent variable-length byte strings (words or word fragments) in the database by minimum-redundancy codes--Huffman codes. An essential part of the technique is the construction of the dictionary to yield high compression ratios. A heuristic is used to count frequencies of word fragments. A detailed analysis is provided of our implementaton in support of high compression ratios and efficient encoding and decoding under the constraint of a fixed amount of main memory. In each phase of our implementation, we explain why certain data structures or techniques are employed. Experimental results show that our compression scheme is very effective for com pressing large databases of library records. Describes a technigue for compressing large data bases. The method replaces frequent variable-length byte strings (words or word fragments) in the data base by minimum-redundancy codes: Huffman codes. An essential part of the technique is the construction of the dictionary to yield high compression ratios. A heuristic is used to count frequencies of word fragments. Experimental results show that the compression scheme is very effective for compressing large data bases of library records. 00 Original abstract--amended A technique for compressing large databases is presented. The method replaces frequent variable‐length byte strings (words or word fragments) in the database by minimum‐redundancy codes—Huffman codes. An essential part of the technique is the construction of the dictionary to yield high compression ratios. A heuristic is used to count frequencies of word fragments. A detailed analysis is provided of our implementaton in support of high compression ratios and efficient encoding and decoding under the constraint of a fixed amount of main memory. In each phase of our implementation, we explain why certain data structures or techniques are employed. Experimental results show that our compression scheme is very effective for compressing large databases of library records. © 1991 John Wiley & Sons, Inc. A method for compressing large databases is presented. The method replaces frequent variable-length byte strings (words or word fragments) in the database by minimum-redundancy codes called Huffman codes. An essential part of the technique is the construction of the dictionary to yield high compression ratios. A heuristic is used to count frequencies of word fragments. A detailed analysis is provided of the implementation in support of high compression ratios and efficient encoding and decoding under the constraint of a fixed amount of main memory. Experimental results indicate that the compression scheme is very effective for compressing large databases of library records. |
| Author | Yu, Clement Liu, Chengwen |
| Author_xml | – sequence: 1 givenname: Chengwen surname: Liu fullname: Liu, Chengwen organization: Department of Electrical Engineering and Computer Science, University of Illinois at Chicago, Chicago, IL 60680 – sequence: 2 givenname: Clement surname: Yu fullname: Yu, Clement organization: Department of Electrical Engineering and Computer Science, University of Illinois at Chicago, Chicago, IL 60680 |
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| Copyright | Copyright © 1991 John Wiley & Sons, Inc. 1992 INIST-CNRS Copyright Wiley Periodicals Inc. Oct 1991 |
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| DOI | 10.1002/(SICI)1097-4571(199110)42:9<685::AID-ASI7>3.0.CO;2-1 |
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| Keywords | Database Information storage Decoding Coding Huffman code Data compression |
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| References | Huffman, D. A. (1952). A method for the construction of minimum-redundancy codes, Proc. IRE, 40, 1098-1101. Cooper, D., & Lynch, M. F. (1982). Text compression using variable- to fixed-length encodings. Journal of the American Society for Information Science, 33, 18-31. Ziv, J., & Lempel, A. (1978). Compression of individual sequences via variable-rate coding, IEEE Trans. Inf. Theroy, 24, 530-536. Fraenkel, A. S., Mor, M., & Perl, Y. (1983). Is text compression by prefixes and suffixes Practical? Acta Informatica, 20, 371-389. Reingold, E. M., & Handsen, W. J. (1986). Data structures in PASCAL. Boston: Little, Brown. Gilbert, E. N., & Moore, E. F. (1959). Variable-length binary encoding, The Bell System Technical Journal, 38, 993-968. Langdon, G. G. (1984). An introduction to arithmetic coding. IBM J. Res. Dev., 28, 135-149. Bookstein, A., & Klein, S. T. (in press). Compression, information theory and grammars: A unified approach. ACM Trans. Inf. Systems (to appear). Witten, I. H., Neal, R. M., & Cleary, J. G., (1987). Arithmetic coding for data compression. Commun. ACM, 30, 520-540. Lelewer, D. A., & Hirschberg, D. S. (1987). Data compression. ACM Camp. Surveys, 19, 261-296. Jakobsson, M. (1988). One-pass text compression with a subword dictionary. Journal of the American Society for Information Science, 39, 262-269. Welch, T. A. (1984). A technique for high performance data compression, Computer, 17(6), 8-19. Ziv, J., & Lempel, A. (1977). A universal algorithm for sequential data compression. IEEE Trans. Inf. Theory, 23, 337-343. Tanaka, H. (1987). Data structure of Huffman codes and its application to efficient encoding and decoding, IEEE Trans. Inf. Theory, 33, 154-156. Knuth, D. E. (1985) Dynamic Huffman coding, J. Algorithms, 6, 163-180. Gallager, R. G. (1978). Variations on a theme by Huffman. IEEE Trans. Inf. Theory, 24, 668-674. Kernighan, B. W., & Ritchie, D. M. (1978). The C programming language, Englewood Cliffs, New Jersey: Prentice-Hall Inc. Schwartz, E. S., & Kallick, B. (1964). Generating a canonical prefix encoding, Commun. ACM, 7, 166-169. Cormack, G. V., & Horspool, R. N. (1984). Algorithms for adaptive Huffman codes. Inf. Processing Letters, 18, 159-165. Fiala, E. R., & Greene, D. H. (1989). Data compression with finite windows. Commun. ACM, 32, 490-505. 1987; 33 1989; 32 1987; 30 1984; 28 1964; 7 1984; 17 1982; 33 1952; 40 1988; 39 1985; 6 1983; 20 1976 1986 1973 1984; 18 1978; 24 1977; 23 1959; 38 1987; 19 1969 1978 1989 1988 |
| References_xml | – reference: Gallager, R. G. (1978). Variations on a theme by Huffman. IEEE Trans. Inf. Theory, 24, 668-674. – reference: Schwartz, E. S., & Kallick, B. (1964). Generating a canonical prefix encoding, Commun. ACM, 7, 166-169. – reference: Ziv, J., & Lempel, A. (1978). Compression of individual sequences via variable-rate coding, IEEE Trans. Inf. Theroy, 24, 530-536. – reference: Fraenkel, A. S., Mor, M., & Perl, Y. (1983). Is text compression by prefixes and suffixes Practical? Acta Informatica, 20, 371-389. – reference: Welch, T. A. (1984). A technique for high performance data compression, Computer, 17(6), 8-19. – reference: Gilbert, E. N., & Moore, E. F. (1959). Variable-length binary encoding, The Bell System Technical Journal, 38, 993-968. – reference: Knuth, D. E. (1985) Dynamic Huffman coding, J. Algorithms, 6, 163-180. – reference: Cormack, G. V., & Horspool, R. N. (1984). Algorithms for adaptive Huffman codes. Inf. Processing Letters, 18, 159-165. – reference: Kernighan, B. W., & Ritchie, D. M. (1978). The C programming language, Englewood Cliffs, New Jersey: Prentice-Hall Inc. – reference: Cooper, D., & Lynch, M. F. (1982). Text compression using variable- to fixed-length encodings. Journal of the American Society for Information Science, 33, 18-31. – reference: Jakobsson, M. (1988). One-pass text compression with a subword dictionary. Journal of the American Society for Information Science, 39, 262-269. – reference: Witten, I. H., Neal, R. M., & Cleary, J. G., (1987). Arithmetic coding for data compression. Commun. ACM, 30, 520-540. – reference: Bookstein, A., & Klein, S. T. (in press). Compression, information theory and grammars: A unified approach. ACM Trans. Inf. Systems (to appear). – reference: Huffman, D. A. (1952). A method for the construction of minimum-redundancy codes, Proc. IRE, 40, 1098-1101. – reference: Tanaka, H. (1987). Data structure of Huffman codes and its application to efficient encoding and decoding, IEEE Trans. Inf. Theory, 33, 154-156. – reference: Reingold, E. M., & Handsen, W. J. (1986). Data structures in PASCAL. Boston: Little, Brown. – reference: Langdon, G. G. (1984). An introduction to arithmetic coding. IBM J. Res. Dev., 28, 135-149. – reference: Ziv, J., & Lempel, A. (1977). A universal algorithm for sequential data compression. IEEE Trans. Inf. Theory, 23, 337-343. – reference: Fiala, E. R., & Greene, D. H. (1989). Data compression with finite windows. Commun. ACM, 32, 490-505. – reference: Lelewer, D. A., & Hirschberg, D. S. (1987). Data compression. ACM Camp. Surveys, 19, 261-296. – volume: 28 start-page: 135 year: 1984 end-page: 149 article-title: An introduction to arithmetic coding publication-title: IBM J. Res. Dev. – volume: 23 start-page: 337 year: 1977 end-page: 343 article-title: A universal algorithm for sequential data compression publication-title: IEEE Trans. Inf. Theory – volume: 7 start-page: 166 year: 1964 end-page: 169 article-title: Generating a canonical prefix encoding publication-title: Commun. ACM – volume: 19 start-page: 261 year: 1987 end-page: 296 article-title: Data compression publication-title: ACM Camp. Surveys – year: 1973 – start-page: 129 year: 1969 end-page: 135 – volume: 33 start-page: 154 year: 1987 end-page: 156 article-title: Data structure of Huffman codes and its application to efficient encoding and decoding publication-title: IEEE Trans. Inf. Theory – volume: 20 start-page: 371 year: 1983 end-page: 389 article-title: Is text compression by prefixes and suffixes Practical? publication-title: Acta Informatica – volume: 33 start-page: 18 year: 1982 end-page: 31 article-title: Text compression using variable‐ to fixed‐length encodings publication-title: Journal of the American Society for Information Science – start-page: 160 year: 1989 end-page: 167 – volume: 24 start-page: 530 year: 1978 end-page: 536 article-title: Compression of individual sequences via variable‐rate coding publication-title: IEEE Trans. Inf. Theroy – volume: 17 start-page: 8 issue: 6 year: 1984 end-page: 19 article-title: A technique for high performance data compression publication-title: Computer – year: 1986 – volume: 40 start-page: 1098 year: 1952 end-page: 1101 article-title: A method for the construction of minimum‐redundancy codes publication-title: Proc. IRE – article-title: Compression, information theory and grammars: A unified approach publication-title: ACM Trans. Inf. Systems – year: 1988 – volume: 39 start-page: 262 year: 1988 end-page: 269 article-title: One‐pass text compression with a subword dictionary publication-title: Journal of the American Society for Information Science – volume: 18 start-page: 159 year: 1984 end-page: 165 article-title: Algorithms for adaptive Huffman codes publication-title: Inf. Processing Letters – volume: 38 start-page: 993 year: 1959 end-page: 968 article-title: Variable‐length binary encoding publication-title: The Bell System Technical Journal – year: 1978 – volume: 30 start-page: 520 year: 1987 end-page: 540 article-title: Arithmetic coding for data compression publication-title: Commun. ACM – volume: 32 start-page: 490 year: 1989 end-page: 505 article-title: Data compression with finite windows publication-title: Commun. ACM – volume: 6 start-page: 163 year: 1985 end-page: 180 article-title: Dynamic Huffman coding publication-title: J. Algorithms – volume: 24 start-page: 668 year: 1978 end-page: 674 article-title: Variations on a theme by Huffman publication-title: IEEE Trans. Inf. Theory – start-page: 382 year: 1976 end-page: 410 |
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| Snippet | A technique for compressing large databases is presented. The method replaces frequent variable‐length byte strings (words or word fragments) in the database... A technique for compressing large databases is presented. The method replaces frequent variable-length byte strings (words or word fragments) in the database... Describes a technigue for compressing large data bases. The method replaces frequent variable-length byte strings (words or word fragments) in the data base by... A method for compressing large databases is presented. The method replaces frequent variable-length byte strings (words or word fragments) in the database by... |
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| SubjectTerms | Algorithms Codes Compression Compression ratio Computerised information retrieval Computerized information services Computerized information storage and retrieval Data compression Data structures Decoding Dictionaries Exact sciences and technology Formats. Markup languages. Codification. Conversion Fragments Huffman codes Information and communication sciences Information and document structure and analysis Information processing and retrieval Information science Information science. Documentation Information services Information storage Information storage and retrieval Information work Libraries Magnetic tape Methods Online information retrieval Ratios Redundancy Sciences and techniques of general use Searching Subject indexing |
| Title | Data compression using word encoding with Huffman code |
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| Volume | 42 |
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