Sensible Graph Analytics for Massive Interlinked Data and Social Networks

针对海量互连数据和社交网络的敏感图形分析

• 批准号: RGPIN-2016-04022
• 负责人: Thomo, Alex
• 金额: $ 3.35万
• 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=634738
• 关键词: Sensible; Graph; Analytics; Massive; Interlinked

Data is generated at an exponential rate from millions of people around the world, large-scale open-data movements, scientific experiments, business information systems, social media, and so on. This data is often represented as a web of facts that link entities (people, organizations, and objects) to each other. Also, entities in different data-sources are interlinked together due to ever-increasing data integration initiatives. The result is an enormous amount of data which is represented as graphs (networks), where the nodes are entities and the edges are the links between them.

数据以指数级的速度从全球数百万人、大规模开放数据运动、科学实验、商业信息系统、社交媒体等产生。这些数据通常表示为将实体（人员、组织和对象）彼此连接起来的事实网络。此外，由于不断增加的数据集成活动，不同数据源中的实体相互链接在一起。其结果是大量的数据被表示为图（网络），其中节点是实体，边是它们之间的链接。