| Project/Area Number |
15K00428
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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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| Research Field |
Web informatics, Service informatics
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| Research Institution | The University of Aizu |
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Principal Investigator |
Paik Incheon 会津大学, コンピュータ理工学部, 教授 (70336478)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | Service Discovery / Service Network / Big Data Infrastructure / Map-Reduce Algorithm / Task Allocation / Big Data Processing / Social Service Network / Scale Free Network / Map-Reduce Operation / Service Graph / Distributed Processing / Big Data / Hadoop / Map-Reduce / Deep Learning / Graph Partition |
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| Outline of Final Research Achievements |
The objective and plan of this research is to develop distributed algorithm and system to discover services on Global Social Service Network (GSSN) on a distributed big data infrastructure and its evaluation and application. The contribution of this research is as follows.
First, a novel algorithm, called Map-Reduce Global Social Service Network (MR-GSSN), to generate large service network, has been developed on Hadoop cluster with 18 nodes. We evaluated service discovery performance based on MR-GSSN, and it shows almost same result as that of GSSN with 30 times speed up. Second, in this research, we proposed a new evaluation matric for service discovery on MR-GSSN has been developed. Third, as an application of big data infrastructure, a task allocation algorithm on big data infrastructure and its evaluation has been proposed.
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