Development of Stream-based Lossless Data Compression Technology

【 Research Outline 】

Due to fast growth of the amount of data from the data sources such as network, video, sensors, etc., the fast communication data path is demanded. It is getting to reach the technological limit for the data migration due to the BigData applications. The conventional data compression technology uses blocking approach that treats a data block stored once in memory. It degrades the performance when it is applied to a situation that treats data streams. Thus, the data compression technology requires to treat directly the data stream without buffering. However, there does not exist any data compression technology that fully treats data stream without any stall. This research project will develop a new stream-based data compression/decompression technology that treats a data stream. This technology will contribute a reliable technology to modern computing systems. Read More

Research articles in Journals and Magazines

  1. Shinichi Yamagiwa, Koichi Marumo, Suzukaze Kuwabara. Exception Handling Method Based on Event from Look-Up Table Applying Stream-Based Lossless Data Compression, Electronics, 240, (2021-01-21), DOI:10.3390/electronics10030240
  2. Koichi Marumo, Shinichi Yamagiwa, Ryuta Morita, Hiroshi Sakamoto. Lazy Management for Frequency Table on Hardware-Based Stream Lossless Data Compression, Information, 63, (2016-10-31), DOI:10.3390/info7040063

Conference Proceedings

  1. Shinichi Yamagiwa, Ryuta Morita, Koichi Marumo. Reducing Symbol Search Overhead on Stream-Based Lossless Data Compression, ICCS 2019: Computational Science, 619--626, (2019-06-08), DOI:10.1007/978-3-030-22750-0_59
  2. Shinichi Yamagiwa, Ryuta Morita, Koichi Marumo. Bank Select Method for Reducing Symbol Search Operations on Stream-Based Lossless Data Compression, 2019 Data Compression Conference (DCC), , (2019-03-26), DOI:10.1109/dcc.2019.00123
  3. Koichi Marumo, Shinichi Yamagiwa. Time-Sharing Multithreading on Stream-Based Lossless Data Compression, 2017 Fifth International Symposium on Computing and Networking (CANDAR), , (2017-11-19), DOI:10.1109/candar.2017.42
  4. Shinichi Yamagiwa, Koichi Marumo, Hiroshi Sakamoto. Stream-Based Lossless Data Compression Hardware Using Adaptive Frequency Table Management, In proceedings of Big Data Benchmarks, Performance Optimization, and Emerging Hardware. BPOE 2015. Lecture Notes in Computer Science Vol. 9495, 133--146, (2016-01-09), DOI:10.1007/978-3-319-29006-5_11
  5. Shinichi Yamagiwa, Hiroshi Sakamoto. A reconfigurable stream compression hardware based on static symbol-lookup table, 2013 IEEE International Conference on Big Data, , (2013-10-06), DOI:10.1109/bigdata.2013.6691702