Investigation of data compression algorithms for big data analysis
| Project/Area Number |
18K04141
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 21020:Communication and network engineering-related
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| Research Institution | Wakayama University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2021: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
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| Keywords | 情報理論 / 多端子仮説検定 / 分散関数計算 / 情報源符号化 / 推測問題 / 関数計算 / 仮説検定 / 多端子情報理論 |
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| Outline of Final Research Achievements |
Problems of data compression for function computation and multiple terminal hypothesis testing are investigated.
In the study of data compression for function computation, we extended the existing result, which mainly dealt with the case where the argument of the function is two, to the case where the argument is three. In addition, the study was conducted in a setting that takes into account the channel noise between data-observers and the node performing the computation, and extended the existing result that did not take into account the channel noise. In the study of the multiple terminal hypothesis testing, we derived general formulas which show relationship among multiple achievable error exponents. As secondary results, we also obtained results on the relationship between source coding and guessing problems, as well as results on VF source coding.
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| Academic Significance and Societal Importance of the Research Achievements |
情報機器の発達により蓄積されるデータが飛躍的に増大しつつある現在，従来のデータ処理技術では扱うことが困難な膨大なデータ（ビッグデータ）を効率よく扱うための手法が必要とされている．具体的には，観測データをネットワークを介して通信する際に，データの宛先で実行するデータ処理に適した形で通信するためのデータ圧縮技術が必要になる．本研究の成果は，そのような通信のためのデータ圧縮技術開発に貢献するものである．
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Report
(6 results)
Research Products
(16 results)
