III: Small: Scalable RDF Query Processing Using a Cloud Infrastructure

资源描述框架（RDF），近年来，已成为一个越来越重要的数据和知识表示形式主义的广泛的应用，包括万维网。 随着RDF数据集规模的快速增长，越来越需要可扩展且高效的技术来存储，索引和查询大小为数万亿个三元组的RDF数据集。 该项目由Missouri-Kansas City大学的Praveen Rao博士领导，旨在通过开发以下内容来解决这一需求：（1）一种存储、索引和查询RDF数据的新方法，该方法将图视为一等公民，以减少使用RDF签名、RDF签名索引和线图进行图模式匹配所需的连接成本和数量;（2）一种用于在云平台上并行SPARQL查询处理的新方法，其使用基于RDF签名的数据分布方案，用于跨计算节点快速找到感兴趣的RDF图的位置索引，和流言驱动的查询执行模型;（3）基于RDF图模式基数估计的Gossip算法和一种新的RDF图模式选择性估计方法，分而治之的方法，有效的负载平衡和提高精度。该项目的更广泛影响包括涵盖RDF数据管理和云计算主题的新课程，肿瘤学家癌症数据的可扩展RDF推理工具，超大型RDF数据存储的新云服务，增加本科生和研究生（包括女性）基于研究的高级培训机会。这项研究的结果，包括出版物，软件和数据集将与更广泛的社区免费共享。有关的更多信息可通过项目网站http://vortex.sce.umkc.edu/ric.html获得。

Resource Description Framework (RDF) has, in recent years, become an increasingly important data and knowledge representation formalism for a broad range of applications, including the World Wide Web. With rapidly growth in the size of RDF datasets, there is growing need for scalable and efficient technologies for storing, indexing, and querying RDF datasets that are trillions of triples in size. This project, led by Dr. Praveen Rao of University of Missouri-Kansas City, aims to address this need by developing: (1) A novel approach to storing, indexing, and querying of RDF data that treats graphs as first-class citizens to reduce the cost and number of joins required for graph pattern matching using RDF signatures, RDF signature indexes and line graphs; (2) A new approach for parallel SPARQL query processing on cloud platforms using data distribution schemes based on RDF signatures, location index for quickly finding RDF graphs of interest across computing nodes, and a gossip-driven query execution model and (3) A new approach for selectivity estimation of RDF graph patterns for query optimization based on new gossip algorithms for cardinality estimation of RDF graph patterns and a divide-andconquer method for effective load balancing and improved accuracy. The broader impacts of this project include new courses covering topics in RDF data management and cloud computing, a scalable RDF reasoning tool over cancer data for oncologists, new cloud services for very large RDF data stores, increased opportunities for research-based advanced training of undergraduate and graduate students, including women. The results of this research, including publications, software, and data sets will be freely shared with the broader community. Additional information about the can be accessed through the project website at http://vortex.sce.umkc.edu/ric.html.