Managing Linked Data in the Cloud

该研究提案的目标是显着改善基于云的数据管理技术，以促进结构化和半结构化数据在Web上的发布和消费，利用关联数据的功能。链接数据使用图形数据模型，以资源描述格式（RDF）编码，其中真实的世界实体通过网址标识。我们将着重于三个初步方面;（1）创造新的数据和元数据探索方法和工具，大大提高现有的探索和理解能力（在网络规模上）在网络上的链接数据集内部和之间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，和问责制，利用基于云的大数据分析技术，适合在我们的环境中记录查询（例如用于检测隐私侵犯行为以及相关方的责任和合规性查询），以及（3）开发适用于在云上消费链接数据的应用的自主和自我管理技术，通过形式化新问题和开发经过实验验证的解决方案，为云环境量身定制。

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.