Data Credibility, Diversity and Privacy in Large Graph Analytics

大图分析中的数据可信度、多样性和隐私

• 批准号: RGPIN-2017-04039
• 负责人: Srinivasan, Venkatesh
• 金额: $ 1.46万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634797
• 关键词: Data; Credibility; Diversity; Privacy; Large

One of the biggest challenges we face today is the need to handle large amounts of information. There is an urgent need to analyze data which is massive, highly interconnected and evolving with the goal of obtaining knowledge that aids us in making important decisions in application domains such as business growth, public administration, health, defense and the environment. Graphs are a natural choice to represent highly interconnected data. This research proposal addresses challenges in three core areas of large graph analytics,

我们今天面临的最大挑战之一是需要处理大量的信息。迫切需要分析大量、高度相互关联和不断发展的数据，目的是获取知识，帮助我们在业务增长、公共管理、卫生、国防和环境等应用领域做出重要决策。图是表示高度互连数据的自然选择。本研究计划解决了大图形分析的三个核心领域的挑战，