| Project/Area Number |
18K18057
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 60080:Database-related
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| Research Institution | University of Tsukuba |
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Principal Investigator |
Shiokawa Hiroaki 筑波大学, 計算科学研究センター, 准教授 (90775248)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | データベース / グラフ / 問合せ処理 / ビッグデータ / 問合せ / アルゴリズム |
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| Outline of Final Research Achievements |
In this research project, we have developed graph parallel processing techniques that focus on the properties of real-world graph data, and have developed methods for accelerating various query processing problems for large-scale graphs. Specifically, we addressed (1) community search queries for large unweighted undirected graphs, (2) similarity search queries for heterogeneous graphs, (3) dense subgraph search queries for attributed graphs, and (4) range queries for complex networks, and we have developed a set of fast and scalable algorithms for distributed parallel environments. All of the results from (1) to (4) are 10 to 10,000 times faster than conventional methods while guaranteeing the same level of query processing quality.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究が対象とした大規模グラフデータはタンパク質データや化学物質データなどを扱う医療・ライフサイエンス分野，ソーシャルネットワークやWebデータなどを扱うSNSアプリケーションなど，我々の日常生活に密接に関わるデータである．本研究が開発した高速・高精度・スケーラブルな問合せ処理アルゴリズムはこれらの利用場面において，研究や開発サイクルの加速やアプリケーションの利便性向上に大きく寄与する技術である．
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