PPoSS: Planning: Dynamic Big Graph Store for High-Throughput and Secure Distributed Query Processing

由于图可以很容易地表示实体及其之间的关系，因此它们被广泛用于表示从在线购物平台到社交网络等领域的大量数据。这些图表在大小和结构上不断演变，因为它们集成了新出现的数据，通常是实时的，因此需要在线分析来根据最新的可用信息回答用户查询。这项研究的目标是解决跨越软硬件频谱的基本挑战，以开发一个可扩展的、安全的在线图形分析分布式平台。这项研究将发现新的技术，可以高效地为不断变化的数据查询提供有意义的答案，同时解决云环境中可能出现的安全问题。这个名为DyGr的平台的关键组件包括：具有一致性模型的图形存储，该模型定制一致性范围，以高效地计算有意义的查询答案；跨软件和硬件层支持的事件驱动的增量计算模型，以快速计算查询结果；以及硬件支持的协议，用于对私有数据进行安全的分布式查询评估。通过建立一个强大的在线分析系统，这项研究将有助于加速使用图形分析的领域的新发现。在线分析的进步将使与企业相关的新应用成为可能，从而有助于经济增长。参与该项目的学生将在系统建设的所有方面获得全面培训，并接受需要在线分析的应用程序培训。因此，以贡献的形式对国家需要领域的劳动力发展的更广泛的影响将是巨大的。这一奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Since graphs can readily express entities and relationships among them, they are widely used to represent large volumes of data from domains ranging from online shopping platforms to social networks. These graphs continue to evolve in size and structure as they integrate new data that emerges, often in real time, thus creating a need for online analytics to answer user queries based upon the latest available information. The goal of this research is to address fundamental challenges across the software-hardware spectrum to develop a scalable and secure distributed platform for online graph analytics. This research will discover novel techniques for delivering meaningful answers to queries over changing data, with a high degree of efficiency, while addressing security concerns that can arise in cloud settings. The key components of this platform, named DyGr, include: a graph store with a consistency model that tailors the scope of consistency to efficiently compute meaningful answers to queries; an event-driven incremental computation model that is supported across software and hardware layers to rapidly compute query results; and hardware supported protocols for secure distributed evaluation of queries over private data. By building of a powerful online analytics system this research will contribute to acceleration of new discoveries in fields that employ graph analytics. Advances in online analytics will enable new applications that are relevant to businesses and thus will contribute to economic growth. The students participating in this project will gain comprehensive training in all aspects of system building as well as receive training in applications that require online analytics. Therefore, the broader impact in form of contributions to workforce development in an area of national need will be tremendous.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.