Project/Area Number |
15K12148
|
Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
Allocation Type | Multi-year Fund |
Research Field |
Web informatics, Service informatics
|
Research Institution | Hokkaido University |
Principal Investigator |
Oyama Satoshi 北海道大学, 情報科学研究科, 准教授 (30346100)
|
Research Collaborator |
SONG Jing
|
Project Period (FY) |
2015-04-01 – 2019-03-31
|
Project Status |
Completed (Fiscal Year 2018)
|
Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
|
Keywords | クラウドソーシング / オープンデータ / 因果分析 / 人工知能 |
Outline of Final Research Achievements |
In this research, we investigated fundamental technologies to analyze open data using the ability of many people by crowdsourcing. In particular, we focused on the task of analyzing causality but not correlation and developed a framework for analyzing open data in an exploratory manner. Human knowledge was incorporated by crowdsourcing to discover candidate variables that can affect causality. The elemental technologies and the framework were evaluated using the open data of the World Bank and the Japanese government.
|
Academic Significance and Societal Importance of the Research Achievements |
企業や官公庁の公開するオープンデータを活用して,社会的な意思決定やビジネス上の決定を行うことが今後ますます重要になってくると考えられる.その際に,相関関係と因果関係を区別することは重要であるが,そこには人間の背景知識の利用が不可欠である.本研究は,インターネット上で不特定多数に仕事を依頼できるクラウドソーシングを用いて,多くの人々の知識を活用して因果分析を行うフレームワークを提案しており,オープンデータ活用への一助になることが期待できる.
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