The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pemampatan data popular digunakan pada sistem komputer dan sistem komunikasi untuk mengurangkan saiz storan dan masa komunikasi, masing-masing. Memandangkan data besar digunakan dengan kerap, pemadanan rentetan untuk data tersebut mengambil masa yang lama. Jika data dimampatkan, masa menjadi lebih lama kerana penyahmampatan diperlukan. Masa pemadanan rentetan yang panjang menjadikan masa imbasan virus komputer lebih lama dan memberi pengaruh yang serius kepada keselamatan data. Daripada ini, kaedah CPM (Compression Pattern Matching) untuk beberapa kaedah pemampatan telah dicadangkan. Kertas ini mencadangkan kaedah CPM untuk PPM yang mencapai imbasan virus pantas dan meningkatkan kebolehpercayaan data termampat, di mana PPM adalah berdasarkan model Markov, menggunakan maklumat konteks dan mencapai nisbah mampatan yang lebih baik daripada transformasi BW dan pengekodan Ziv-Lempel. Kaedah yang dicadangkan mengekod maklumat konteks, yang dijana dalam proses pemampatan dan menambahkan data yang dikodkan pada permulaan data dimampatkan sebagai pengepala. Kaedah yang dicadangkan hanya menggunakan maklumat pengepala. Simulasi komputer mengatakan bahawa penambahan nisbah mampatan adalah kurang daripada 5 peratus jika susunan PPM adalah kurang daripada 5 dan saiz fail sumber adalah lebih daripada 1 M bait, di mana susunan ialah panjang maksimum konteks yang digunakan dalam pemampatan PPM. Masa pemadanan rentetan adalah bebas daripada saiz fail sumber dan sangat singkat, kurang daripada 0.3 mikro saat dalam PC yang digunakan untuk simulasi.
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Salinan
Masato KITAKAMI, Toshihiro OKURA, "Dependability Improvement for PPM Compressed Data by Using Compression Pattern Matching" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 10, pp. 2435-2439, October 2008, doi: 10.1093/ietisy/e91-d.10.2435.
Abstract: Data compression is popularly applied to computer systems and communication systems in order to reduce storage size and communication time, respectively. Since large data are used frequently, string matching for such data takes a long time. If the data are compressed, the time gets much longer because decompression is necessary. Long string matching time makes computer virus scan time longer and gives serious influence to the security of data. From this, CPM (Compression Pattern Matching) methods for several compression methods have been proposed. This paper proposes CPM method for PPM which achieves fast virus scan and improves dependability of the compressed data, where PPM is based on a Markov model, uses a context information, and achieves a better compression ratio than BW transform and Ziv-Lempel coding. The proposed method encodes the context information, which is generated in the compression process, and appends the encoded data at the beginning of the compressed data as a header. The proposed method uses only the header information. Computer simulation says that augmentation of the compression ratio is less than 5 percent if the order of the PPM is less than 5 and the source file size is more than 1 M bytes, where order is the maximum length of the context used in PPM compression. String matching time is independent of the source file size and is very short, less than 0.3 micro seconds in the PC used for the simulation.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.10.2435/_p
Salinan
@ARTICLE{e91-d_10_2435,
author={Masato KITAKAMI, Toshihiro OKURA, },
journal={IEICE TRANSACTIONS on Information},
title={Dependability Improvement for PPM Compressed Data by Using Compression Pattern Matching},
year={2008},
volume={E91-D},
number={10},
pages={2435-2439},
abstract={Data compression is popularly applied to computer systems and communication systems in order to reduce storage size and communication time, respectively. Since large data are used frequently, string matching for such data takes a long time. If the data are compressed, the time gets much longer because decompression is necessary. Long string matching time makes computer virus scan time longer and gives serious influence to the security of data. From this, CPM (Compression Pattern Matching) methods for several compression methods have been proposed. This paper proposes CPM method for PPM which achieves fast virus scan and improves dependability of the compressed data, where PPM is based on a Markov model, uses a context information, and achieves a better compression ratio than BW transform and Ziv-Lempel coding. The proposed method encodes the context information, which is generated in the compression process, and appends the encoded data at the beginning of the compressed data as a header. The proposed method uses only the header information. Computer simulation says that augmentation of the compression ratio is less than 5 percent if the order of the PPM is less than 5 and the source file size is more than 1 M bytes, where order is the maximum length of the context used in PPM compression. String matching time is independent of the source file size and is very short, less than 0.3 micro seconds in the PC used for the simulation.},
keywords={},
doi={10.1093/ietisy/e91-d.10.2435},
ISSN={1745-1361},
month={October},}
Salinan
TY - JOUR
TI - Dependability Improvement for PPM Compressed Data by Using Compression Pattern Matching
T2 - IEICE TRANSACTIONS on Information
SP - 2435
EP - 2439
AU - Masato KITAKAMI
AU - Toshihiro OKURA
PY - 2008
DO - 10.1093/ietisy/e91-d.10.2435
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2008
AB - Data compression is popularly applied to computer systems and communication systems in order to reduce storage size and communication time, respectively. Since large data are used frequently, string matching for such data takes a long time. If the data are compressed, the time gets much longer because decompression is necessary. Long string matching time makes computer virus scan time longer and gives serious influence to the security of data. From this, CPM (Compression Pattern Matching) methods for several compression methods have been proposed. This paper proposes CPM method for PPM which achieves fast virus scan and improves dependability of the compressed data, where PPM is based on a Markov model, uses a context information, and achieves a better compression ratio than BW transform and Ziv-Lempel coding. The proposed method encodes the context information, which is generated in the compression process, and appends the encoded data at the beginning of the compressed data as a header. The proposed method uses only the header information. Computer simulation says that augmentation of the compression ratio is less than 5 percent if the order of the PPM is less than 5 and the source file size is more than 1 M bytes, where order is the maximum length of the context used in PPM compression. String matching time is independent of the source file size and is very short, less than 0.3 micro seconds in the PC used for the simulation.
ER -