Managing Linked Data in the Cloud

研究提案的目标是显著改进基于云的数据管理技术，以利用关联数据的能力，促进在Web上发布和使用结构化和半结构化数据。链接数据使用以资源描述格式(RDF)编码的图形数据模型，其中真实世界的实体通过网址标识。我们将重点关注三个最初的方面；(1)创建新的数据和元数据探索方法和工具，该方法和工具将在现有能力的基础上显著改进以探索和理解(在网络规模)在Web上的链接数据集中和跨Web链接数据集使用的数据和元数据，(2)引入灵活的基于链接数据的框架以支持隐私、透明度和责任，该框架利用适合于在我们的环境中记录查询的基于云的大数据分析技术(例如，检测隐私侵犯以及涉及各方的责任和合规查询)，以及(3)开发适合于使用云上的链接数据的应用的自主和自我管理技术，通过将新问题正式化并开发针对云环境量身定做的经过实验验证的解决方案。

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.