Development of a compressed information processing infrastructure for converting stream data into knowledge

• Project/Area Number: 17H01791
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: Intelligent informatics
• Research Institution: Kyushu Institute of Technology
• Principal Investigator: Sakamoto Hiroshi 九州工業大学, 大学院情報工学研究院, 教授 (50315123)
• Co-Investigator(Kenkyū-buntansha): 竹田 正幸 九州大学, システム情報科学研究院, 教授 (50216909) ; 申 吉浩 学習院大学, 付置研究所, 教授 (60523587)
• Project Period (FY): 2017-04-01 – 2022-03-31
• Project Status: Completed (Fiscal Year 2021)
• Budget Amount: ¥17,420,000 (Direct Cost: ¥13,400,000、Indirect Cost: ¥4,020,000); Fiscal Year 2021: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000); Fiscal Year 2020: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000); Fiscal Year 2019: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000); Fiscal Year 2018: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000); Fiscal Year 2017: ¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
• Keywords: 簡潔データ構造 / 圧縮情報検索 / ストリームアルゴリズム / 機械発見 / 秘匿計算 / データ圧縮 / 情報検索 / 機械翻訳 / プライバシー保護 / 知識発見 / ストリームデータ / 可逆圧縮 / オンラインアルゴリズム / パターンマイニング / データマイニング / 機械学習 / ストリームデータ圧縮 / 知識情報処理 / 知能情報処理

Outline of Final Research Achievements

Data that is too large to process is synonymous with nonexistence. This research aims to propose a computational infrastructure that accelerates information processing by data compression and makes it possible to understand huge data. We would like to propose a computational infrastructure that accelerates information processing and enables the understanding of huge data through data compression. Today, we are faced with Today, diverse and abundant data, innovative algorithms, and high-performance hardware are all available.

Academic Significance and Societal Importance of the Research Achievements

本研究は，データ圧縮を基盤とする新しい情報処理を創出しようとしている．
これまでの圧縮とその周辺技術は大まかに，(1) サイズを小さくする，(2) 圧縮データを高速照合する，(3)圧縮データを索引化する，(4) 圧縮データから類似性や頻出パターンを求めるという順に進歩してきた．しかしこれらの技術は静的データに対するものであるため現代のストリームデータには適用できない．本研究が目指す圧縮情報処理はこの弱点を克服し，既存の計算機やネットワークの処理能力をアルゴリズムによってスケールアップすることができる．

Research Products

• [Int'l Joint Research] CeBiB/Santiago(チリ)
• [Int'l Joint Research] Faculty of Computer Science/Dalhousie University(カナダ)
• [Int'l Joint Research] University of Eastern Piedmont/Alessandria(イタリア)
• [Journal Article] A Compression-Based Multiple Subword Segmentation for Neural Machine Translation (2022)
  • Author(s): Nonaka Keita、Yamanouchi Kazutaka、I Tomohiro、Okita Tsuyoshi、Shimada Kazutaka、Sakamoto Hiroshi
  • Journal Title: Electronics Volume: 11 Issue: 7 Pages: 1014-1014
  • DOI: 10.3390/electronics11071014
  • Peer Reviewed / Open Access
• [Journal Article] Practical Random Access to SLP-Compressed Texts (2020)
  • Author(s): Travis Gagie, Tomohiro I, Giovanni Manzini, Gonzalo Navarro, Hiroshi Sakamoto, Louisa Seelbach Benkner, Yoshimasa Takabatake,
  • Journal Title: Proc. 27th International Symposium on String Processing and Information Retrieval (SPIRE) 2020 Volume: - Pages: 221-231
  • DOI: 10.1007/978-3-030-59212-7_16
  • ISBN: 9783030592110, 9783030592127
  • Peer Reviewed / Int'l Joint Research
• [Journal Article] Faster Privacy-Preserving Computation of Edit Distance with Moves (2020)
  • Author(s): Yohei Yoshimoto, Masaharu Kataoka, Yoshimasa Takabatake, Tomohiro I, Kilho Shin, Hiroshi Sakamoto
  • Journal Title: Proc. International Workshop on Algorithms and Computation (WALCOM) 2020 Volume: - Pages: 308-320
  • DOI: 10.1007/978-3-030-39881-1_26
  • ISBN: 9783030398804, 9783030398811
  • Peer Reviewed
• [Journal Article] Rpair: Rescaling RePair with Rsync (2019)
  • Author(s): Travis Gagie, Tomohiro I, Giovanni Manzini, Gonzalo Navarro, Hiroshi Sakamoto, Yoshimasa Takabatake
  • Journal Title: Proc. 26th International Symposium on String Processing and Information Retrieval (SPIRE) 2019 Volume: - Pages: 35-44
  • DOI: 10.1007/978-3-030-32686-9_3
  • ISBN: 9783030326852, 9783030326869
  • Peer Reviewed / Int'l Joint Research
• [Journal Article] A faster implementation of online RLBWT and its application to LZ77 parsing (2018)
  • Author(s): Ohno Tatsuya、Sakai Kensuke、Takabatake Yoshimasa、I Tomohiro、Sakamoto Hiroshi
  • Journal Title: Journal of Discrete Algorithms Volume: 52-53 Pages: 18-28
  • DOI: 10.1016/j.jda.2018.11.002
  • Peer Reviewed / Open Access
• [Journal Article] LZ-ABT: A Practical Algorithm for α-Balanced Grammar Compression (2018)
  • Author(s): Tatsuya Ohno, Keisuke Goto, Yoshimasa Takabatake, Tomohiro I, Hiroshi Sakamoto
  • Journal Title: Lecture Notes in Computer Science Volume: 10979 Pages: 323-335
  • Peer Reviewed
• [Journal Article] Privacy-Preserving String Edit Distance with Moves (2018)
  • Author(s): Shunta Nakagawa, Tokio Sakamoto, Yoshimasa Takabatake, Tomohiro I, Kilho Shin, Hiroshi Sakamoto
  • Journal Title: Lecture Notes in Computer Science Volume: 11223 Pages: 226-240
  • Peer Reviewed
• [Journal Article] Approximate Frequent Pattern Discovery in Compressed Space (2018)
  • Author(s): Shouhei FUKUNAGA, Yoshimasa TAKABATAKE, Tomohiro I, Hiroshi SAKAMOTO
  • Journal Title: IEICE Transactions on Information and Systems Volume: E101.D Issue: 3 Pages: 593-601
  • DOI: 10.1587/transinf.2017FCP0010
  • NAID: 130006414054
  • ISSN: 0916-8532, 1745-1361
  • Peer Reviewed / Open Access
• [Journal Article] A Space-Optimal Grammar Compression (2017)
  • Author(s): Yoshimasa Takabatake, Tomohiro I, Hiroshi Sakamoto
  • Journal Title: Proc. 25th Annual European Symposium on Algorithms (ESA 2017) Volume: －
  • DOI: 10.4230/LIPIcs.ESA.2017.67
  • Peer Reviewed / Open Access
• [Journal Article] A Faster Implementation of Online Run-Length Burrows-Wheeler Transform (2017)
  • Author(s): Tatsuya Ohno, Yoshimasa Takabatake, Tomohiro I, Hiroshi Sakamoto
  • Journal Title: Proc. 28th International Workshop on Combinatorial Algorithms (IWOCA 2017) Volume: － Pages: 409-419
  • DOI: 10.1007/978-3-319-78825-8_33
  • ISBN: 9783319788241, 9783319788258
  • Peer Reviewed
• [Presentation] データ圧縮の機械学習と秘匿計算への応用 (2022)
  • Author(s): 坂本比呂志
  • Organizer: 第120回人工知能基本問題研究会
  • Invited
• [Presentation] JPEGの画像表現を用いた画像生成の高速化 (2021)
  • Author(s): 大北 剛, 管谷克彦, 坂本 比呂志
  • Organizer: 第24回情報論的学習理論ワークショップ(IBIS2021)
• [Presentation] Neural machine translation using subword segmentation with probabilistic locally consistent parsing (2021)
  • Author(s): Keita Nonaka, Hiroshi Sakamoto, Tomohiro I
  • Organizer: 9th International Symposium on Applied Engineering and Sciences (SAES2021)
  • Int'l Joint Research
• [Presentation] Secure computing of eigenvalues and eigenvectors using fully homomorphic encryption (2021)
  • Author(s): Kanta Moriyama, Hiroshi Sakamoto
  • Organizer: 9th International Symposium on Applied Engineering and Sciences (SAES2021)
  • Int'l Joint Research
• [Presentation] Evaluating the execution time of an efficient multiparty sorting protocol on TFHE (2021)
  • Author(s): Jun Takata, Shinji Ono, Tomohiro I, Hiroshi Sakamoto
  • Organizer: 9th International Symposium on Applied Engineering and Sciences (SAES2021)
  • Int'l Joint Research
• [Presentation] 非可逆圧縮データからの高速な画像生成 (2020)
  • Author(s): 管谷 克彦, 高畠 嘉将, 井 智弘, 申 吉浩, 坂本 比呂志
  • Organizer: IBIS2020
• [Presentation] RePair in Compressed Space and Time (2019)
  • Author(s): Kensuke Sakai, Tatsuya Ohno, Keisuke Goto, Yoshimasa Takabatake, Tomohiro I, Hiroshi Sakamoto
  • Organizer: Data Compression Conference 2019
  • Int'l Joint Research
• [Presentation] Stream Data Compression and Its Applications (2017)
  • Author(s): Hiroshi Sakamoto
  • Organizer: The 14th International Conference on Modeling Decisions for Artificial Intelligence
  • Int'l Joint Research / Invited
• [Presentation] ストリームデータ圧縮とその応用 (2017)
  • Author(s): 坂本比呂志
  • Organizer: 第30回 回路とシステムワークショップ プログラム
  • Invited
• [Remarks] 拡張TFHEライブラリ
  • URL: https://github.com/hiroshi-kyutech/FTHE-tool
• [Patent(Industrial Property Rights)] 秘匿検索システム及び秘匿検索プログラム (2019)
  • Inventor(s): 坂本比呂志 申吉浩
  • Industrial Property Rights Holder: 坂本比呂志 申吉浩
  • Industrial Property Rights Type: 特許
  • Industrial Property Number: 2019-093908
  • Filing Date: 2019