Managing Linked Data in the Cloud

该研究计划的目标是显著改进基于云的数据管理技术，以促进Web上结构化和半结构化数据的发布和消费，充分利用关联数据的能力。关联数据使用以资源描述格式（RDF）编码的图形数据模型，其中通过web地址识别现实世界中的实体。我们将重点从三个方面着手：(1)创建新颖的数据和元数据探索方法和工具，这些方法和工具将显著提高现有的探索和理解（在网络规模上）网络上关联数据集内部和之间使用的数据和元数据的能力；(2)引入一个灵活的、基于关联数据的框架，以支持隐私、透明度；和问责制，利用基于云的大数据分析技术，适合记录我们设置中的查询（例如检测隐私侵犯以及相关方的问责制和合规性查询），以及(3)通过将新问题正规化并开发针对云环境量身定制的实验验证解决方案，开发适合于使用云上关联数据的应用程序的自主和自我管理技术。

The research proposal objectives are to significantly improve cloud-based data management techniques to facilitate the publication and consumption of structured and semi-structured data on the Web, leveraging the capabilities of Linked Data. Linked Data uses a graph data model, encoded in the Resource Description Format (RDF), where real world entities are identified via web addresses. We will focus on three initial aspects; (1) creating novel data and metadata exploration approaches and tools that will significantly improve upon existing capabilities to explore and to understand (at web-scale) the data and metadata that is used within and across linked datasets on the Web, (2) introducing a flexible, linked data based framework to support privacy, transparency, and accountability that leverages cloud based Big Data analytics techniques that are suitable to log queries in our setting (such as accountability and compliance queries that detect privacy violations as well as the parties involved), and (3) developing autonomic and self-managing techniques appropriate to applications that consume linked data on the cloud, by formalizing new problems and developing experimentally validated solutions that are tailored to the cloud environment.