研究者詳細 - 坂本　比呂志

論文 - 坂本　比呂志

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定数領域の頻度計算を用いたオンライン文法圧縮アルゴリズム

徳永啓太郎, 坂本比呂志

人工知能学会研究会資料人工知能基本問題研究会 ( 一般社団法人人工知能学会 ) 100 ( 0 ) 15 2016年03月

文法圧縮を用いた類似度計算の大規模データベースへの適用

大西孝典, 久保山哲二, 坂本比呂志

人工知能学会研究会資料人工知能基本問題研究会 ( 一般社団法人人工知能学会 ) 100 ( 0 ) 18 2016年03月

文法圧縮における逆引き辞書の省スペース化

福永祥平, 坂本比呂志

人工知能学会研究会資料人工知能基本問題研究会 ( 一般社団法人人工知能学会 ) 100 ( 0 ) 02 2016年03月

ストリームデータからの頻出パターンの近似発見

青山友紀, 高畠嘉将, 坂本比呂志

人工知能学会研究会資料人工知能基本問題研究会 ( 一般社団法人人工知能学会 ) 99 ( 0 ) 21 2016年01月

文法圧縮のハッシュ領域の削減

水野仁人, 高畠嘉将, 坂本比呂志

人工知能学会研究会資料人工知能基本問題研究会 ( 一般社団法人人工知能学会 ) 99 ( 0 ) 05 2016年01月

Lazy management for frequency table on hardware-based stream lossless data compression 査読有り国際誌

Marumo K., Yamagiwa S., Morita R., Sakamoto H.

Information (Switzerland) 7 ( 4 ) 2016年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2016 by the authors. The demand for communicating large amounts of data in real-time has raised new challenges with implementing high-speed communication paths for high definition video and sensory data. It requires the implementation of high speed data paths based on hardware. Implementation difficulties have to be addressed by applying new techniques based on data-oriented algorithms. This paper focuses on a solution for this problem by applying a lossless data compression mechanism on the communication data path. The new lossless data compression mechanism, called LCA-DLT, provides dynamic histogram management for symbol lookup tables used in the compression and the decompression operations. When the histogram memory is fully used, the management algorithm needs to find the least used entries and invalidate these entries. The invalidation operations cause the blocking of the compression and the decompression data stream. This paper proposes novel techniques to eliminate blocking by introducing a dynamic invalidation mechanism, which allows achievement of a high throughput data compression.

DOI： 10.3390/info7040063

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85007368023&origin=inward

Online Grammar Compression for Frequent Pattern Discovery 査読有り

井智弘, 坂本比呂志

ICGI 2016 - 93 - 104 2016年01月

Online Self-Indexed Grammar Compression 査読有り

Yoshimasa Takabatake, Yasuo Tabei, Hiroshi Sakamoto

Lecture Notes in Computer Science 9309 258 - 269 2015年09月

Finding ambiguous patterns on grammar compressed string 査読有り

Maeda K., Takabatake Y., Tabei Y., Sakamoto H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9067 331 - 339 2015年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer-Verlag Berlin Heidelberg 2015. Given a grammar compressed string S, a pattern P, and d ≥ 0, we consider the problem of finding all occurrences of Pʹ in S such that d(P, P ʹ) ≤ d with respect to Hamming distance. We propose an algorithm for this problem in O(lg lg n lg ∗ N(m+d occ d lg m/d lgN)) time, where N = |S|, m = |P|, n is the number of variables in the grammar compression, and occ d is the frequency of an evidence of a substring of P. We implement this algorithm and compare with a naive filtering on the grammar compression.

DOI： 10.1007/978-3-662-48119-6_25

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84945534777&origin=inward

Workshop on graph-based algorithms for big data and its applications (GABA2014) 査読有り

Kawahara Y., Kuboyama T., Sakamoto H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9067 293 - 295 2015年01月

An Extension of Community Extraction Algorithm on Bipartite Graph 査読有り

Y. Li, K. Maeda, T. Kuboyama, H. Sakamoto

International Journal of Advances in Computer Science and Its Applications 4 ( 4 ) 30 - 34 2014年12月

Online Pattern Matching for String Edit Distance with Moves 査読有り

Yoshimasa Takabatake, Yasuo Tabei, Hiroshi Sakamoto

21th International Symposium on String Processing and Information Retrieval 2014年10月

Grammar Compression: Grammatical Inference by Compression and Its Application to Real Data 招待有り査読有り

Hiroshi Sakamoto

12th International Conference on Grammatical Inference (ICGI2014, 2014-9) 3 - 20 2014年09月

Improved ESP-index: A Practical Self-index for Highly Repetitive Texts 査読有り

Yoshimasa Takabatake, Yasuo Tabei, Hiroshi Sakamoto

13th International Symposium on Experimental and Efficient Algorithms 2014年06月

Truss Decomposition for Extracting Communities in Bipartite Graph 査読有り

Y. Li, T. Kuboyama, H. Sakamoto

The Third International Conference on Advances in Information Mining and Management (IMMM2013) 2013年11月

Scalable Pattern Discovery on Knot Theory 査読有り

Y. Takabatake, T. Kuboyama, A. Yasuhara, H. Sakamoto

Workshop on Data Discretization and Segmentation for Knowledge Discovery (DDS 2013) 2013年10月

An Implementation of Truss Decomposition of Bipartite Graph 査読有り

Y. Li, T. Kuboyama, H. Sakamoto

Workshop on Data Discretization and Segmentation for Knowledge Discovery (DDS2013) 2013年10月

Fully-Online Grammar Compression 査読有り

S. Maruyama, Y. Tabei, H. Sakamoto, K. Sadakane

20th International Symposium on String Processing and Information Retrieval (SPIRE2013) 218 - 229 2013年10月

A Reconfigurable Stream Compression Hardware based on Static Symbol-Lookup Table 査読有り

S. Yamagiwa, H. Sakamoto

The First Workshop on Benchmarks, Performance Optimization, and Emerging hardware of Big Data Systems and Applications (BPOE 2013) 86 - 93 2013年10月

Pattern extraction from graphs and beyond 査読有り

Sakamoto H., Sakamoto H., Kuboyama T.

Smart Innovation, Systems and Technologies 24 115 - 145 2013年09月

記述言語：英語掲載種別：研究論文（学術雑誌）

We explain recent studies on pattern extraction from large-scale graphs. Patterns are represented explicitly and implicitly. Explicit patterns are concrete subgraphs defined in graph theory, e.g., clique and tree. For an explicit model of patterns, we introduce notable fast algorithms for finding all frequent patterns. We also confirm that these problems are closely related to traditional problems in data mining. On the other hand, implicit patterns are defined by statistical factors, e.g., modularity, betweenness, and flow determining optimal hidden subgraphs. For both models, we give an introductory survey focusing on notable pattern extraction algorithms.

DOI： 10.1007/978-3-319-00372-6J7

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84883304986&origin=inward

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